{ "cells": [ { "cell_type": "markdown", "id": "a2729eee", "metadata": {}, "source": [ "# 因子型变量_数据框_函数式编程purrr包\n", "## 什么是因子\n", "因子是把数据进行分类并标记为不同层级(`level`，有时候也翻译成因子水平， 我个人觉得翻译为层级，更接近它的特性，因此，我都会用层级来描述)的数据对象，他们可以存储字符串和整数。因子类型有三个属性：\n", "\n", "- 存储类别的数据类型\n", "- 离散变量\n", "- 因子的层级是有限的，只能取因子层级中的值或缺失(`NA`)" ] }, { "cell_type": "markdown", "id": "9446b52e", "metadata": {}, "source": [ "## 创建因子\n", "- 因子层级会自动按照字符串的字母顺序排序，比如`high low medium`。也可以用`levels=c()`指定顺序\n", "- 不属于因子层级中的值, 会被当作缺省值`NA`" ] }, { "cell_type": "code", "execution_count": 1, "id": "3a92ba20", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "── \u001b[1mAttaching core tidyverse packages\u001b[22m ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 2.0.0 ──\n", "\u001b[32m✔\u001b[39m \u001b[34mdplyr \u001b[39m 1.1.4 \u001b[32m✔\u001b[39m \u001b[34mreadr \u001b[39m 2.1.5\n", "\u001b[32m✔\u001b[39m \u001b[34mforcats \u001b[39m 1.0.0 \u001b[32m✔\u001b[39m \u001b[34mstringr \u001b[39m 1.5.1\n", "\u001b[32m✔\u001b[39m \u001b[34mggplot2 \u001b[39m 3.5.0 \u001b[32m✔\u001b[39m \u001b[34mtibble \u001b[39m 3.2.1\n", "\u001b[32m✔\u001b[39m \u001b[34mlubridate\u001b[39m 1.9.3 \u001b[32m✔\u001b[39m \u001b[34mtidyr \u001b[39m 1.3.1\n", "\u001b[32m✔\u001b[39m \u001b[34mpurrr \u001b[39m 1.0.2 \n", "── \u001b[1mConflicts\u001b[22m ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──\n", "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n", "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m masks \u001b[34mstats\u001b[39m::lag()\n", "\u001b[36mℹ\u001b[39m Use the conflicted package (\u001b[3m\u001b[34m\u001b[39m\u001b[23m) to force all conflicts to become errors\n" ] } ], "source": [ "library(tidyverse)" ] }, { "cell_type": "code", "execution_count": 2, "id": "b4386551", "metadata": {}, "outputs": [], "source": [ "library(palmerpenguins)" ] }, { "cell_type": "code", "execution_count": 3, "id": "ba6f5864", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
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" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item low\n", "\\item high\n", "\\item medium\n", "\\item medium\n", "\\item low\n", "\\item high\n", "\\item high\n", "\\end{enumerate*}\n", "\n", "\\emph{Levels}: \\begin{enumerate*}\n", "\\item 'high'\n", "\\item 'low'\n", "\\item 'medium'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. low\n", "2. high\n", "3. medium\n", "4. medium\n", "5. low\n", "6. high\n", "7. high\n", "\n", "\n", "\n", "**Levels**: 1. 'high'\n", "2. 'low'\n", "3. 'medium'\n", "\n", "\n" ], "text/plain": [ "[1] low high medium medium low high high \n", "Levels: high low medium" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "income <- c(\"low\", \"high\", \"medium\", \"medium\", \"low\", \"high\", \"high\")\n", "factor(income)" ] }, { "cell_type": "code", "execution_count": 4, "id": "74568dc7", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
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" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item low\n", "\\item high\n", "\\item medium\n", "\\item medium\n", "\\item low\n", "\\item high\n", "\\item high\n", "\\end{enumerate*}\n", "\n", "\\emph{Levels}: \\begin{enumerate*}\n", "\\item 'low'\n", "\\item 'high'\n", "\\item 'medium'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. low\n", "2. high\n", "3. medium\n", "4. medium\n", "5. low\n", "6. high\n", "7. high\n", "\n", "\n", "\n", "**Levels**: 1. 'low'\n", "2. 'high'\n", "3. 'medium'\n", "\n", "\n" ], "text/plain": [ "[1] low high medium medium low high high \n", "Levels: low high medium" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "## 指定顺序\n", "factor(income, levels=c(\"low\", \"high\", \"medium\"))" ] }, { "cell_type": "code", "execution_count": 5, "id": "649c8423", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
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" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item low\n", "\\item high\n", "\\item \n", "\\item \n", "\\item low\n", "\\item high\n", "\\item high\n", "\\end{enumerate*}\n", "\n", "\\emph{Levels}: \\begin{enumerate*}\n", "\\item 'low'\n", "\\item 'high'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. low\n", "2. high\n", "3. <NA>\n", "4. <NA>\n", "5. low\n", "6. high\n", "7. high\n", "\n", "\n", "\n", "**Levels**: 1. 'low'\n", "2. 'high'\n", "\n", "\n" ], "text/plain": [ "[1] low high low high high\n", "Levels: low high" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "factor(income, levels=c(\"low\", \"high\"))" ] }, { "cell_type": "markdown", "id": "65cb0b17", "metadata": {}, "source": [ "相比较字符串而言，因子类型更容易处理，因此很多函数会自动的将字符串转换为因子来处理，但事实上，这也会造成，不想当做因子的却又当做了因子的情形\n", "\n", "最典型的是在`R 4.0`之前，`data.frame()`中`stringsAsFactors`选项，**默认将字符串类型转换为因子类型**，但这个默认也带来一些不方便，因此在R 4.0之后取消了这个默认。\n", "\n", "在`tidyverse`集合里，有专门处理因子的宏包`forcats`" ] }, { "cell_type": "code", "execution_count": 6, "id": "9df0e209", "metadata": {}, "outputs": [], "source": [ "library(forcats)" ] }, { "cell_type": "markdown", "id": "135e2dd3", "metadata": {}, "source": [ "## 调整因子顺序\n", "- 因子层级默认是按照字母顺序排序\n", "- `fct_relevel()`指定因子顺序 \n", " - `after=Inf`将某个因子移到最后面\n", "- `fct_inorder()` 按照字符串第一次出现的次序\n", "- `fct_reorder()` 按照其他变量的升序排序 `.fun=`指定函数\n", "- `fct_rev()` 按照因子层级的逆序排序\n", "- `fct_infreq()`按照因子频率排序，从大到小\n", " - `fct_rev(fct_infreq())`按照因子频率排序，从小到大" ] }, { "cell_type": "code", "execution_count": 18, "id": "0e71b4ea", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "\n", "
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" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item low\n", "\\item high\n", "\\item medium\n", "\\item medium\n", "\\item low\n", "\\item high\n", "\\item high\n", "\\end{enumerate*}\n", "\n", "\\emph{Levels}: \\begin{enumerate*}\n", "\\item 'low'\n", "\\item 'high'\n", "\\item 'medium'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. low\n", "2. high\n", "3. medium\n", "4. medium\n", "5. low\n", "6. high\n", "7. high\n", "\n", "\n", "\n", "**Levels**: 1. 'low'\n", "2. 'high'\n", "3. 'medium'\n", "\n", "\n" ], "text/plain": [ "[1] low high medium medium low high high \n", "Levels: low high medium" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "income <- c(\"low\", \"high\", \"medium\", \"medium\", \"low\", \"high\", \"high\")\n", "x <- factor(income)\n", "x\n", "# 指定顺序\n", "x %>% fct_relevel(c(\"high\", \"medium\", \"low\"))\n", "x %>% fct_relevel(\"medium\")\n", "x %>% fct_relevel(\"medium\", after=Inf)\n", "\n", "# 按照字符串第一次出现的次序\n", "x %>% fct_inorder()" ] }, { "cell_type": "code", "execution_count": 19, "id": "73598c1f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
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" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item low\n", "\\item high\n", "\\item medium\n", "\\item medium\n", "\\item low\n", "\\item high\n", "\\item high\n", "\\end{enumerate*}\n", "\n", "\\emph{Levels}: \\begin{enumerate*}\n", "\\item 'low'\n", "\\item 'medium'\n", "\\item 'high'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. low\n", "2. high\n", "3. medium\n", "4. medium\n", "5. low\n", "6. high\n", "7. high\n", "\n", "\n", "\n", "**Levels**: 1. 'low'\n", "2. 'medium'\n", "3. 'high'\n", "\n", "\n" ], "text/plain": [ "[1] low high medium medium low high high \n", "Levels: low medium high" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 按照其他变量的中位数的升序排序\n", "x %>% fct_reorder(c(1:7), .fun=median)" ] }, { "cell_type": "markdown", "id": "2a8fac8c", "metadata": {}, "source": [ "## 应用\n", "调整因子层级有什么用呢？\n", "\n", "这个功能在ggplot可视化中调整分类变量的顺序非常方便" ] }, { "cell_type": "code", "execution_count": 20, "id": "b8aef96a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
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\n" ], "text/latex": [ "A tibble: 6 × 2\n", "\\begin{tabular}{ll}\n", " x & y\\\\\n", " & \\\\\n", "\\hline\n", "\t a & 2\\\\\n", "\t a & 2\\\\\n", "\t b & 1\\\\\n", "\t b & 5\\\\\n", "\t c & 0\\\\\n", "\t c & 3\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 6 × 2\n", "\n", "| x <chr> | y <dbl> |\n", "|---|---|\n", "| a | 2 |\n", "| a | 2 |\n", "| b | 1 |\n", "| b | 5 |\n", "| c | 0 |\n", "| c | 3 |\n", "\n" ], "text/plain": [ " x y\n", "1 a 2\n", "2 a 2\n", "3 b 1\n", "4 b 5\n", "5 c 0\n", "6 c 3" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d <- tibble(\n", " x = c(\"a\",\"a\", \"b\", \"b\", \"c\", \"c\"),\n", " y = c(2, 2, 1, 5, 0, 3)\n", " \n", ")\n", "d" ] }, { "cell_type": "code", "execution_count": 21, "id": "a31637c8", "metadata": {}, "outputs": [ { "data": { "image/png": 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ABBCDsAgCCEHQBAEMIOACAIYQcAEISwAwAIQtgBAAQh7AAAghB2AABBCDsAgCCE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", "text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "d %>% \n", " ggplot(aes(x = x, y = y))+\n", " geom_point()" ] }, { "cell_type": "markdown", "id": "0b4a16ef", "metadata": {}, "source": [ "### ` fct_reorder()`\n", "- `fct_reorder(x, y, .fun=median)`可以让`x`的顺序按照`x`中每个分类变量对应`y`值的中位数升序排序\n", "- `.desc = TRUE`颠倒顺序" ] }, { "cell_type": "code", "execution_count": 26, "id": "3ca8b529", "metadata": {}, "outputs": [ { "data": { "image/png": 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CCEHQBAEMIOACAIYQcAEISwAwAIQtgBAAQh7AAAghB2AABBCDsAgCCEHQBAEMIO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\n+a7fumB40mPiPS3LvAeAnLCDzqD+9zsnSfcJdy5a8MYV3+yRpDc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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "d %>% \n", " ggplot(aes(x = fct_reorder(x, y, .fun=median), y = y)) + \n", " geom_point()" ] }, { "cell_type": "code", "execution_count": 28, "id": "f243a104", "metadata": {}, "outputs": [ { "data": { "image/png": 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XEcFxUVVVVVzZs3L9uzQJoVFhbGcTxn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\nwg4AIBDCD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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "d %>% \n", " ggplot(aes(x = fct_reorder(x,y, .fun=median, .desc=TRUE), y = y)) + \n", " geom_point()\n", "d %>% \n", " mutate(x = fct_reorder(x, y, .fun=median, .desc=TRUE)) %>% \n", " ggplot(aes(x = x, y = y)) +\n", " geom_point()" ] }, { "cell_type": "markdown", "id": "6c4a56a0", "metadata": {}, "source": [ "### `fct_rev()`\n", "按照因子层级的逆序排序" ] }, { "cell_type": "code", "execution_count": 31, "id": "a5341350", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "d %>% \n", " mutate(x = fct_rev(x)) %>% \n", " ggplot(aes(x, y)) + \n", " geom_point()" ] }, { "cell_type": "markdown", "id": "f83ec930", "metadata": {}, "source": [ "### `fct_relevel()`" ] }, { "cell_type": "code", "execution_count": 32, "id": "f19b9994", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "d %>% \n", " mutate(\n", " x = fct_relevel(x, c(\"c\", \"a\", \"b\"))\n", " ) %>% \n", " ggplot(aes(x, y))+\n", " geom_point()" ] }, { "cell_type": "markdown", "id": "e25fc1ad", "metadata": {}, "source": [ "## 可视化中应用" ] }, { "cell_type": "code", "execution_count": 35, "id": "6d7d0529", "metadata": {}, "outputs": [ { "data": { "image/png": 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37mUyN/16qbrgMAyL1lbLLCjt02XD6DAACwbHzzBABAEMIOACAIYQcAEISwAwAI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\njLwPy0eTDEcAAAAASUVORK5CYII=", "text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "library(palmerpenguins)\n", "\n", "ggplot(penguins, aes(y=species))+\n", " geom_bar()" ] }, { "cell_type": "code", "execution_count": 36, "id": "b8357cc3", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "# 按species逆序\n", "ggplot(penguins, aes(y = fct_rev(species)))+\n", " geom_bar()" ] }, { "cell_type": "code", "execution_count": 40, "id": "14bcb1f4", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
  1. Adelie
  2. Chinstrap
  3. Gentoo
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\n", "\t\n", "\t\tLevels:\n", "\t\n", "\t\n", "\t
  1. 'Adelie'
  2. 'Chinstrap'
  3. 'Gentoo'
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" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item Adelie\n", "\\item Chinstrap\n", "\\item Gentoo\n", "\\end{enumerate*}\n", "\n", "\\emph{Levels}: \\begin{enumerate*}\n", "\\item 'Adelie'\n", "\\item 'Chinstrap'\n", "\\item 'Gentoo'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. Adelie\n", "2. Chinstrap\n", "3. Gentoo\n", "\n", "\n", "\n", "**Levels**: 1. 'Adelie'\n", "2. 'Chinstrap'\n", "3. 'Gentoo'\n", "\n", "\n" ], "text/plain": [ "[1] Adelie Chinstrap Gentoo \n", "Levels: Adelie Chinstrap Gentoo" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "
  1. Adelie
  2. Chinstrap
  3. Gentoo
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\n", "\t\n", "\t\tLevels:\n", "\t\n", "\t\n", "\t
  1. 'Chinstrap'
  2. 'Gentoo'
  3. 'Adelie'
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" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item Adelie\n", "\\item Chinstrap\n", "\\item Gentoo\n", "\\end{enumerate*}\n", "\n", "\\emph{Levels}: \\begin{enumerate*}\n", "\\item 'Chinstrap'\n", "\\item 'Gentoo'\n", "\\item 'Adelie'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. Adelie\n", "2. Chinstrap\n", "3. Gentoo\n", "\n", "\n", "\n", "**Levels**: 1. 'Chinstrap'\n", "2. 'Gentoo'\n", "3. 'Adelie'\n", "\n", "\n" ], "text/plain": [ "[1] Adelie Chinstrap Gentoo \n", "Levels: Chinstrap Gentoo Adelie" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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9Rto79pd+ZLb93V48fHzl521ZDre3a/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\n0lv3uWZ4qzEPPvnKQ6/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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "penguins %>% \n", " count(species) %>% \n", " pull(species)\n", "\n", "penguins %>% \n", " count(species) %>% \n", " mutate(species = fct_relevel(species, c(\"Chinstrap\", \"Gentoo\", \"Adelie\"))) %>% \n", " pull(species)\n", "\n", "# 把Chinstrap移到前面, 其他顺序不变\n", "ggplot(penguins, aes(y = fct_relevel(species, \"Chinstrap\"))) +\n", " geom_bar()" ] }, { "cell_type": "code", "execution_count": 41, "id": "d52b4483", "metadata": {}, "outputs": [ { "data": { "image/png": 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R0mrnrPPrbxsDee97Hs1Vrffsyq6NGz21uYKBsAAACMUL5iZ17/uSmzn7vzs32n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\nkCx29Xq8uyLqxPVrcT8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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "# Use order \"Chinstrap\", \"Gentoo\", \"Adelie\"\n", "ggplot(penguins, aes(y = fct_relevel(species, \"Chinstrap\", \"Gentoo\", \"Adelie\"))) +\n", " geom_bar()" ] }, { "cell_type": "code", "execution_count": 44, "id": "36c3ae93", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "penguins %>% \n", " mutate(species = fct_relevel(species, \"Chinstrap\", \"Gentoo\", \"Adelie\")) %>% \n", " ggplot(aes( y = species))+\n", " geom_bar()" ] }, { "cell_type": "code", "execution_count": 45, "id": "4ac53ec2", "metadata": {}, "outputs": [ { "data": { "image/png": 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uYcEQAAoCwqOOw+3vF7Kc9xV/6VDR99+G2Ma/ug7m/0dbdNvXp8x88zf6g69z9P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", "text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "# 把Adelie放到最后\n", "ggplot(penguins, aes(y = fct_relevel(species, \"Adelie\", after=Inf)))+\n", " geom_bar()" ] }, { "cell_type": "code", "execution_count": 48, "id": "ac75ae2c", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "# fct_infreq() 按照因子频率，从小到大\n", "penguins %>% \n", " mutate(species = fct_infreq(species)) %>% \n", " ggplot(aes(y = species))+\n", " geom_bar()\n", "\n", "penguins %>% \n", " mutate(species = fct_rev(fct_infreq(species))) %>% \n", " ggplot(aes(y = species))+\n", " geom_bar()" ] }, { "cell_type": "code", "execution_count": 51, "id": "e67f92f7", "metadata": {}, "outputs": [ { "data": { "image/png": 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BCEsAMACELYAQAEIewAAIIQdgAAQQg7AIAghB0AQBDCDgAgCGEHABCEsAMACELY\nAQAEIewAAIIQdgAAQQg7AIAghB0AQBDCDgAgCGEHABCEsAMACELYAQAEIewAAIIQdgAAQQg7\nAIAghB0AQBDCDgAgCGEHABCEsAMACELYAQAEIewAAIIQdgAAQQg7AIAghB0AQBDCDgAgCGEH\nABCEsAMACELYAQAEIewAAIIQdgAAQQg7AIAghB0AQBDCDgAgCGEHABCEsAMACELYAQAEIewA\nAIIQdgAAQQg7AIAghB0AQBDCDgAgCGEHABCEsAMACELYAQAEIewAAIIQdgAAQQg7AIAghB0A\nQBDCDgAgCGEHABCEsAMACELYAQAEIewAAIIQdgAAQQg7AIAghB0AQBDCDgAgCGEHABCEsAMA\nCELYAQAEIewAAIIQdgAAQQg7AIAghB0AQBDCDgAgCGEHABCEsAMACELYAQAEIewAAIIQdgAA\nQQg7AIAghB0AQBDCDgAgCGEHABCEsAMACELYAQAEIewAAIIQdgAAQQg7AIAghB0AQBDCDgAg\nCGEHABCEsAMACELYAQAEIewAAIIQdgAAQQg7AIAghB0AQBDCDgAgCGEHABCEsAMACELYAQAE\nIewAAIIoWNi99cj1106a8UVLFr166wW/venF2tU9EQBA89Yi74/QWPXS36+6aeJLU2fMK+22\n8ebf2e+QPfq1zqS3Jl13bUOXA7br8blfqHtvyvPPLdq8Jm1SugIPV/v+lBkNXft0Llv5yQEA\nmpU8H7FrfOees479xTXT2m2z79Enjf7RkJ6z/3bunyfXLPd3Wg8adfmVv9yp3Yo94pRbTj/7\ntukr9rsAAM1Zfo/YzXvg0j8/s/bBvzt7aPeWKaW0+ebb7rD/otIVORKXo8aqqgWpTf62DwCw\nxspr2L39j1sntxpy1t5Lqy6llFKmtLTko5+zRQ3vTrzislsnvvB2faeB+x13zO7rlaSU0iPn\n7TOm4YTbR2+/9Ocb1ht7YueHLr/ts6tVv/b3i/90+3+mV7Xs1HvATsMP3nuTGX8accZdHzQ2\npNF73ZlS/6OuPnvX1umR8/a5af1xx7e/44/XPTRtk1FXDCu6+5a7Hn1hyvQP0tp9v/Ojow/d\nau3s0ge6rtuZPyn957X3T369cq0eA773k5/uvaETugBA85HPsKufMWNm2nDoxk09RsMzl55T\ns8vew47fq+rhS8b/aUKfrc7dq+PnV3vnzrPO6bXD9z+zWuMLV5116Rvbjjzj59KW4+EAAAzq\nSURBVBuVzH7uX68szGRSv4PGXtD+7GNu63by+EP6pBZl5R9uYfZj48cV997xx6OHd9vgzfvO\nf75026FHDe9c/O69E8b+5qIel542pCKllNKMG8ZcvuMP9z96j3YLnr/l4it+eUHrP588uPVH\nY9TU1MydO3fZVCUlJdlsdlU9VZ+XyWTyt3GgKXn6/7qoqCiTyeT1RSO8pa+KnsZVwnO4kpbu\njdlstrGxcfU/elHR8t5Hl8+wm/v++/VlvduXNLW8sef+Z52yR0UmpdR7r3/dPeG1aY2p4+dz\nprbLPr/6/GqL5sxZ0KpLv816d67IdB5yUP+UUkplFRWtilNRSeu2bdt+YguVs7sef/GRA5ae\nAT7ozFM/vLvXsO/dd8eEp19uHLJNJqWUGrY8/Lxjd8ymlFLv45ZMPfScux/6YPCea3+49mOP\nPTZq1Khl2/zDH/6w9dZbr+AzA6yp2rVbwff35qJly5ZfvhLL1aJFi7z+N/qa8ByuEp9ujdWn\noaFhOUvzGXbt11k7u3Du3LqUmmi7ioqKDzuutG3b0iX/XVCX0he8/+4LVyvZdvhhj5w74Yj/\neXDH3ff+/s4DOi/vjXt9Bwz4eHHjwnee+/dDjzw/deac2W8V1Xf774KUlh6XK26x7J8wpb03\n6JqenvluSh+FXceOHYcMGbJsK23atKmrq/vS52CFtWiR/w8sA5+Tp/+vi4qKstns4sWL87Hx\nr4+SkpKGhgZP40pq2bLlokWLCj1F81ZcXFxUVJTXDFi+kpImj5rlsx6y3bp3Tve/8mr9jpt9\n6THfHE88fmK1lr32OGnCd9585O5bb73w6Ou7Dz/r9H3XL27q15b9VDv1lrPOuq98z4P2PeT7\nG3R8/dKDfvGF19Jb+upeUvqJ522TTTYZM2bMspuVlZVVVVU5Db1CysvLv3wlYFXL0//XxcXF\npaWleX3RCC+TyZSUlNTX13saV1L79u09hyupoqKiqKhowYIFBTkVm81mlxN2eb3cSc9d9uy/\n4IFr73y3/uP7Fs6aVbnKHqCo9Xrf/uFx5134o24v3HzPKymllM0Wpbq62qae59rHrvnLjG/9\n7P/2226jjmXZ1JiaWvH9p59+p2Xv3p+/yh4AwJoqv+f7Ou56xCGTTv7zyF/MGrbHVr3a1s58\n4aGbb5n+zTMvGtF3Jbdc9/LV596a3Wmf7ft1zr476bmZRet+q2NKKZV37dZm4RMP3P9ax43L\nWnXu1v4zx/AaGlOqfGHiYzM6rr/ozUduvvLBhan3x0ufvnHc7SW7bdFp0ZR7L73mlW77XLBd\nHi/MAgCwiuX5jVxFPfc6a1z3m6+85cG//Ouv1Wt13aDfziePGdp35T+NU7L+DkN6XPv3i04Z\nP2thq879dho9co9OKaWU6T/0Z7u/+efLTh5ZtuGwU8/eu+enf61s0IhjX/jdX343cmK79Tcf\n9IMTDpp//KPLFmY2GrjR27edf8PUqrLu39jvV0cM7eWbdAGAZiRTkNPDa6BPXjwvF5WVlXl9\nA295efmIESPyt33gC40bNy4fm/Ueu5WXyWQ6dOiwePHiyspV94aer6X27dt/8updrICKiori\n4uI5c+YU6j12y/lcs2NSAABBCDsAgCCEHQBAEK6C+6Fvjfrb7YWeAQBgZThiBwAQhLADAAhC\n2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEI\nOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhh\nBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHs\nAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQd\nAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLAD\nAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYA\nAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4A\nIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEA\nBCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABNGi0APQpGuuuWbevHn1\n9fWFHqQZKysrq6+vr6urK/QgzVjLli3btGlTXV1dU1NT6FkA+BKO2AEABCHsAACCEHYAAEEI\nOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhh\nBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHs\nAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEFkGhsbCz0D\nX+C88867++67L7vssvXWW6/Qs/C19u9///u000474ogjhg0bVuhZ+FpbtGjRrrvuuvnmm48d\nO7bQs/B1d8IJJzzzzDN33313SUlJoWf5LEfs1lC1tbXz589vaGgo9CB83S1evHj+/PmLFi0q\n9CCQ5s+fv3DhwkJPAammpmb+/Plr5qExYQcAEISwAwAIokWhB+CL9evXr7q6eq211ir0IHzd\nderUaciQId7rScEVFRUNGTKkV69ehR4E0oABAyoqKoqK1sSjYz48AQAQxJoYmwAArABhBwAQ\nhPfYrYFqXr31okvvfvrtRev0/c7wow/dun2m0BPxdfLB7aNHXPryx7d7HjBh/AFdU7Jnsjos\nfOuJhyc++uC9/3q517E3n7JD8bIFTe1+dkvyo4ldcc1/hRR2a5wP7jv31OsW7XPir/+3/NVr\nf3PuKdkxvz94Q69UrDbVCxakjYedd/S3Wy29XVzRKaVkz2T1mDft6ZferS/KLvn03U3tfnZL\n8qWJXXHNf4V0KnZNM/P+25/qut8xwwb07NZnl2MO3fq9f9z19OJCD8XXyYIFC7LrrLdRj4+s\nW9EiJXsmq0nXHY484YQT9tzo0/c2tfvZLcmbL94Vm8ErpLBbw1Q+++z0zgMGrLv0VukW39i4\n6ulnphV2Jr5eFiyoblNR8dl77ZkUUFO7n92S1W7Nf4V0KnYNM3fe3NShfYePbpZ3aN/yv3Pn\nNabk3AKrx6IFCxbVvXHNL342Y9rcluv23+HAww7YqlMLeyaF1NTuV2K3ZDVrBq+Qwm4Ns6Bq\nQWpV1mrZ7bK1yhremV+dUnkBh+LrJLvJLof8YPHmO2zZveWc528Z/9tf/6rotxce2MueSQE1\ntfuV2y1ZzZrBK6RTsWuY8tblqWZhzbLbC6sXZtu08f0TrDbZztvsN3T7DTuUlbbuvtUhP9ur\n8/R/TZpuz6Sgmtr97Jasbs3gFVLYrWHat2+f5syZ89HNBXPmLmrbzsf3KZBMp86d0tx5c+2Z\nFFRTu5/dkoJaM18hhd0apmLzb6w/6+mn3l16q/bpZ15pvcUWvQs7E18nDVVVCz++tejVV95I\nPXr0sGdSUE3tfnZLVrPm8AqZPf300wvzyHyx1usWT7n+6kcbNtyo/YInL7vo1sW7HDXiG/4F\nympSNXHs/4z558JWrVq2aPjvaw9cPOGWeVv99GdDupXYM1kdGuvmz61cUFPz5mO3Pbmo/5DN\nO9TVpZJWxUVN7X52S/Lki3fF2keawStkprGxsRCPy3LUvnbbRX+686m3F6298Q4HHuNC6qxW\ntdMfvP7Gfz3/2pQZC1p12WTQ/iOGbbtuyw8X2TPJs9r7Tv/h+Kc+ec+6+//u4gPXT03vfnZL\n8qGpXbEZvEIKOwCAILzHDgAgCGEHABCEsAMACELYAQAEIewAAIIQdgAAQQg7AIAghB0AQBDC\nDgAgCGEHABCEsAMACELYAQAEIewAVoubD8hkBv3h/fq3/nHuT/b6Zo92bbv33/XEW99cUui5\ngEiEHcBq8/ptxw3Z6qAr3mr37YMO+946b99z3n77j3+j0EMBgbQo9AAAXx8zH31132ufuX/3\nbtmU0i+2K+q67zXX3DDt+NG9Cz0YEIQjdgCrz3dPHLu06lJK5dttt3lKM2bMKOxIQCTCDqBA\n2rRpk9LChQsLPQcQh7ADKJBMJlPoEYBghB0AQBDCDgAgCGEHABCEsAMACCLT2NhY6BkAAFgF\nHLEDAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACC\nEHYAAEEIOwCAIP4fx9sD1A13vaUAAAAASUVORK5CYII=", "text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "# n是count()函数产生的结果\n", "penguins %>% \n", " count(species) %>% \n", " mutate(species = fct_reorder(species, n)) %>% \n", " ggplot(aes(n, species))+\n", " geom_col()" ] }, { "cell_type": "code", "execution_count": 69, "id": "99c35fa5", "metadata": {}, "outputs": [ { "data": { "image/png": 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thti5USWA4AgJ5bfNi9P+VXPz/70WHN/7PbH7cI//7D7rtPXuxZJ04WdgAAy9ri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\n9Jk2an5bNxLYBtorBzTyaEZcbIUBR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BeE1AhR0CgUAgEAjEawIq7BAIBAKBQCBe\nE1Bhh0AgEAgEAvGagAo7BAKBQCAQiNcEVNghEAgEAoFAvCb8PyCJKTd7jfPOAAAAAElFTkSu\nQmCC", "text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "# 画出的2007年美洲人口寿命的柱状图，要求从高到低排序\n", "\n", "library(gapminder) # install.packages(\"gapminder\")\n", "gapminder %>% \n", " filter(year == 2007 & continent == \"Americas\") %>% \n", " ggplot(aes(x = country, y = lifeExp))+\n", " geom_col()+\n", " theme_bw()" ] }, { "cell_type": "code", "execution_count": 71, "id": "ea81baea", "metadata": {}, "outputs": [ { "data": { "image/png": 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HT03r17lUrlokWL+vfvL3RH8A/M2AGULxaLZfHixT179rx27VpERERcXBxSHQCU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Fh4cH289lZ2dznREsB40dAJjTlStXJk2a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\n1aGxs9z9+/dHjRrF4/FWr17t6urKdRwAAACo6tDYWSg1NbVr167379//6qu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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "# 四个国家人口寿命的变化图\n", "gapminder %>%\n", " filter(country %in% c(\"Norway\", \"Portugal\", \"Spain\", \"Austria\")) %>%\n", " ggplot(aes(year, lifeExp)) + \n", " geom_line() +\n", " facet_wrap(vars(country), nrow = 2)" ] }, { "cell_type": "code", "execution_count": 80, "id": "d50ecae4", "metadata": {}, "outputs": [ { "data": { "image/png": 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gBACCEkLS1twYIFV65cYbd3njFjRpUqVbgOBQDlUXZ2dnR09Pr167VabdOmTT//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\nIBCQlN9oNIbDYRkvEuLskx0MBuXVF0JIKq4Os1AoFAwGJdU3GAzyiksNL4Q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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "# 按每个国家寿命的中位数\n", "gapminder %>%\n", " filter(country %in% c(\"Norway\", \"Portugal\", \"Spain\", \"Austria\")) %>%\n", " mutate(country = fct_reorder(country, lifeExp, .fun=median)) %>% \n", " ggplot(aes(year, lifeExp)) + \n", " geom_line() +\n", " facet_wrap(vars(country), nrow = 2)" ] }, { "cell_type": "code", "execution_count": 85, "id": "a3b9c6a6", "metadata": {}, "outputs": [ { "data": { "image/png": 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j168N2Rs8KMHUDpZbPZVq9e3a1btzNnzgQGBp44caJLly58NwUAYGexsbFdunQ5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\nK5VKWccBAGvDMXYAAA5Cr9ePGjUqJydnwYIFjRo1Yh0HABjAFjsAAEfw4MG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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "# 按每个国家寿命差（最大值减去最小值）\n", "cha <- function(x){\n", " max(x) - min(x)\n", "}\n", "\n", "gapminder %>%\n", " filter(country %in% c(\"Norway\", \"Portugal\", \"Spain\", \"Austria\")) %>%\n", " mutate(country = fct_reorder(country, lifeExp, .fun=cha)) %>% \n", " ggplot(aes(year, lifeExp)) + \n", " geom_line() +\n", " facet_wrap(vars(country), nrow = 2)" ] }, { "cell_type": "markdown", "id": "84df929e", "metadata": {}, "source": [ "# 简单数据框\n", "## `tidyverse` 家族\n", "前面陆续介绍了`tidyverse`家族，家庭主要成员包括\n", "\n", "功能\t|宏包\n", "------ | ------\n", "有颜值担当\t|`ggplot2`\n", "数据处理王者\t|`dplyr`\n", "数据转换专家\t|`tidyr`\n", "数据载入利器\t|`readr`\n", "循环加速器\t|`purrr`\n", "强化数据框\t|`tibble`\n", "字符串处理\t|`stringr`\n", "因子处理|\t`forcats`" ] }, { "cell_type": "markdown", "id": "63fd8a40", "metadata": {}, "source": [ "## 人性化的`tibble`\n", "![image.png](image/tibble.png)\n", "- `tibble`是用来替换`data.frame`类型的扩展的数据框\n", "- `tibble`继承了`data.frame`，是弱类型的。换句话说，`tibble`是`data.frame`的子类型\n", "- `tibble`与`data.frame`有相同的语法，使用起来更方便\n", "- `tibble`更早的检查数据，方便写出更干净、更多富有表现力的代码\n", "\n", "`tibble`对`data.frame`做了重新的设定：\n", "\n", "- `tibble`，不关心输入类型，可存储任意类型，包括`list`类型\n", "- `tibble`，没有行名设置 `row.names`\n", "- `tibble`，支持任意的列名\n", "- `tibble`，会自动添加列名\n", "- `tibble`，类型只能回收长度为1的输入\n", "- `tibble`，会懒加载参数，并按顺序运行\n", "- `tibble`，是`tbl_df`类型" ] }, { "cell_type": "markdown", "id": "7d9a1fb0", "metadata": {}, "source": [ "## tibble 与 data.frame" ] }, { "cell_type": "code", "execution_count": 86, "id": "46c84901", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 5 × 2
ab
<int><chr>
1a
2b
3c
4d
5e
\n" ], "text/latex": [ "A data.frame: 5 × 2\n", "\\begin{tabular}{ll}\n", " a & b\\\\\n", " & \\\\\n", "\\hline\n", "\t 1 & a\\\\\n", "\t 2 & b\\\\\n", "\t 3 & c\\\\\n", "\t 4 & d\\\\\n", "\t 5 & e\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 5 × 2\n", "\n", "| a <int> | b <chr> |\n", "|---|---|\n", "| 1 | a |\n", "| 2 | b |\n", "| 3 | c |\n", "| 4 | d |\n", "| 5 | e |\n", "\n" ], "text/plain": [ " a b\n", "1 1 a\n", "2 2 b\n", "3 3 c\n", "4 4 d\n", "5 5 e" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 传统创建数据框\n", "data.frame(\n", " a = 1:5,\n", " b = letters[1:5]\n", ")" ] }, { "cell_type": "markdown", "id": "89ff5b31", "metadata": {}, "source": [ "发现，`data.frame()`会自动将**字符串型**的变量转换成**因子型**，如果想保持原来的字符串型，就得" ] }, { "cell_type": "code", "execution_count": 87, "id": "dbace9e0", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 5 × 2
ab
<int><chr>
1a
2b
3c
4d
5e
\n" ], "text/latex": [ "A data.frame: 5 × 2\n", "\\begin{tabular}{ll}\n", " a & b\\\\\n", " & \\\\\n", "\\hline\n", "\t 1 & a\\\\\n", "\t 2 & b\\\\\n", "\t 3 & c\\\\\n", "\t 4 & d\\\\\n", "\t 5 & e\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 5 × 2\n", "\n", "| a <int> | b <chr> |\n", "|---|---|\n", "| 1 | a |\n", "| 2 | b |\n", "| 3 | c |\n", "| 4 | d |\n", "| 5 | e |\n", "\n" ], "text/plain": [ " a b\n", "1 1 a\n", "2 2 b\n", "3 3 c\n", "4 4 d\n", "5 5 e" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data.frame(\n", " a = 1:5,\n", " b = letters[1:5],\n", " stringsAsFactors = FALSE\n", ")" ] }, { "cell_type": "markdown", "id": "aaa576c3", "metadata": {}, "source": [ "Note： - 在`R 4.0` 后，`data.frame()` 不会将字符串型变量自动转换成因子型\n", "\n", "用`tibble`创建数据框，不会这么麻烦，输出的就是原来的字符串类型" ] }, { "cell_type": "code", "execution_count": 88, "id": "0adb25a0", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 5 × 2
ab
<int><chr>
1a
2b
3c
4d
5e
\n" ], "text/latex": [ "A tibble: 5 × 2\n", "\\begin{tabular}{ll}\n", " a & b\\\\\n", " & \\\\\n", "\\hline\n", "\t 1 & a\\\\\n", "\t 2 & b\\\\\n", "\t 3 & c\\\\\n", "\t 4 & d\\\\\n", "\t 5 & e\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 5 × 2\n", "\n", "| a <int> | b <chr> |\n", "|---|---|\n", "| 1 | a |\n", "| 2 | b |\n", "| 3 | c |\n", "| 4 | d |\n", "| 5 | e |\n", "\n" ], "text/plain": [ " a b\n", "1 1 a\n", "2 2 b\n", "3 3 c\n", "4 4 d\n", "5 5 e" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tibble(\n", " a = 1:5,\n", " b = letters[1:5]\n", ")" ] }, { "cell_type": "markdown", "id": "e0a04b51", "metadata": {}, "source": [ "构建两个有关联的变量，传统的`data.frame()`会报错" ] }, { "cell_type": "code", "execution_count": 90, "id": "490b07ff", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 3 × 2
xy
<int><dbl>
13
24
35
\n" ], "text/latex": [ "A tibble: 3 × 2\n", "\\begin{tabular}{ll}\n", " x & y\\\\\n", " & \\\\\n", "\\hline\n", "\t 1 & 3\\\\\n", "\t 2 & 4\\\\\n", "\t 3 & 5\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 3 × 2\n", "\n", "| x <int> | y <dbl> |\n", "|---|---|\n", "| 1 | 3 |\n", "| 2 | 4 |\n", "| 3 | 5 |\n", "\n" ], "text/plain": [ " x y\n", "1 1 3\n", "2 2 4\n", "3 3 5" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in Ops.factor(x, 2):\n", "“‘+’ not meaningful for factors”\n" ] }, { "ename": "ERROR", "evalue": "Error in data.frame(x = 1:3, y = x + 2): 参数值意味着不同的行数: 3, 7\n", "output_type": "error", "traceback": [ "Error in data.frame(x = 1:3, y = x + 2): 参数值意味着不同的行数: 3, 7\nTraceback:\n", "1. data.frame(x = 1:3, y = x + 2)", "2. stop(gettextf(\"arguments imply differing number of rows: %s\", \n . paste(unique(nrows), collapse = \", \")), domain = NA)" ] } ], "source": [ "tb <- tibble(\n", " x = 1:3,\n", " y = x+2)\n", "tb\n", "\n", "df <- data.frame(\n", " x = 1:3,\n", " y = x+2\n", ")" ] }, { "cell_type": "markdown", "id": "dcdba85d", "metadata": {}, "source": [ "`tibble`用缩写定义了7种类型：\n", "\n", "类型\t|含义\n", "------|------\n", "`int`\t|代表`integer`\n", "`dbl`\t|代表`double`\n", "`chr`\t|代表`character`向量或字符串\n", "`dttm`\t|代表日期+时间(`date`+`time`)\n", "`lgl`\t|代表逻辑判断`TRUE`或者`FALSE`\n", "`fctr`\t|代表因子类型`factor`\n", "`date`\t|代表日期`dates`" ] }, { "cell_type": "markdown", "id": "436d31c7", "metadata": {}, "source": [ "## `tibble`数据操作\n", "### 1 创建`tibble`\n", "- `tibble()` 创建方式和`data.frame()`一样\n", "- `tibble::tribble()`更加直观" ] }, { "cell_type": "code", "execution_count": 92, "id": "43c119fa", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 5 × 2
ab
<int><chr>
1a
2b
3c
4d
5e
\n" ], "text/latex": [ "A tibble: 5 × 2\n", "\\begin{tabular}{ll}\n", " a & b\\\\\n", " & \\\\\n", "\\hline\n", "\t 1 & a\\\\\n", "\t 2 & b\\\\\n", "\t 3 & c\\\\\n", "\t 4 & d\\\\\n", "\t 5 & e\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 5 × 2\n", "\n", "| a <int> | b <chr> |\n", "|---|---|\n", "| 1 | a |\n", "| 2 | b |\n", "| 3 | c |\n", "| 4 | d |\n", "| 5 | e |\n", "\n" ], "text/plain": [ " a b\n", "1 1 a\n", "2 2 b\n", "3 3 c\n", "4 4 d\n", "5 5 e" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 5 × 3
abc
<int><int><int>
11011
21113
31215
41317
51419
\n" ], "text/latex": [ "A tibble: 5 × 3\n", "\\begin{tabular}{lll}\n", " a & b & c\\\\\n", " & & \\\\\n", "\\hline\n", "\t 1 & 10 & 11\\\\\n", "\t 2 & 11 & 13\\\\\n", "\t 3 & 12 & 15\\\\\n", "\t 4 & 13 & 17\\\\\n", "\t 5 & 14 & 19\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 5 × 3\n", "\n", "| a <int> | b <int> | c <int> |\n", "|---|---|---|\n", "| 1 | 10 | 11 |\n", "| 2 | 11 | 13 |\n", "| 3 | 12 | 15 |\n", "| 4 | 13 | 17 |\n", "| 5 | 14 | 19 |\n", "\n" ], "text/plain": [ " a b c \n", "1 1 10 11\n", "2 2 11 13\n", "3 3 12 15\n", "4 4 13 17\n", "5 5 14 19" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# tibble()创建一个tibble类型的data.frame:\n", "tibble(a = 1:5, b = letters[1:5])\n", "tibble(a = 1:5,\n", " b = 10:14,\n", " c = a + b)" ] }, { "cell_type": "code", "execution_count": 94, "id": "971861e6", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\n", "
A tibble: 2 × 3
xyz
<chr><dbl><dbl>
a23.6
b18.5
\n" ], "text/latex": [ "A tibble: 2 × 3\n", "\\begin{tabular}{lll}\n", " x & y & z\\\\\n", " & & \\\\\n", "\\hline\n", "\t a & 2 & 3.6\\\\\n", "\t b & 1 & 8.5\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 2 × 3\n", "\n", "| x <chr> | y <dbl> | z <dbl> |\n", "|---|---|---|\n", "| a | 2 | 3.6 |\n", "| b | 1 | 8.5 |\n", "\n" ], "text/plain": [ " x y z \n", "1 a 2 3.6\n", "2 b 1 8.5" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 为了让每列更加直观，也可以tribble()创建，数据量不大的时候\n", "tibble::tribble(\n", " ~x , ~y, ~z,\n", " \"a\", 2, 3.6,\n", " \"b\", 1, 8.5\n", ")" ] }, { "cell_type": "markdown", "id": "d39e97eb", "metadata": {}, "source": [ "### 2 转换成`tibble`类型\n", "转换成`tibble`类型意思就是说，刚开始不是`tibble`, 现在转换成`tibble`， 包括\n", "\n", "- `data.frame`转换成`tibble`\n", " - `as_tibble()`\n", " - `runif(n, min=0, max=1)`生成n个0-1之间的均匀分布随机数\n", " - `as.data.frame()`转回去\n", "- `vector`转换成`tibble`\n", "- `list`转换成`tibble`\n", " - `as.list()`转回去\n", "- `matrix`转换成`tibble`\n", " - `tibble`转回`matrix`? `as.matrix()`" ] }, { "cell_type": "code", "execution_count": 100, "id": "e4934436", "metadata": {}, "outputs": [ { "data": { "text/html": [ "'data.frame'" ], "text/latex": [ "'data.frame'" ], "text/markdown": [ "'data.frame'" ], "text/plain": [ "[1] \"data.frame\"" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 4
Sepal.LengthSepal.WidthPetal.LengthPetal.Width
<dbl><dbl><dbl><dbl>
5.13.51.40.2
4.93.01.40.2
4.73.21.30.2
4.63.11.50.2
5.03.61.40.2
5.43.91.70.4
\n" ], "text/latex": [ "A data.frame: 6 × 4\n", "\\begin{tabular}{llll}\n", " Sepal.Length & Sepal.Width & Petal.Length & Petal.Width\\\\\n", " & & & \\\\\n", "\\hline\n", "\t 5.1 & 3.5 & 1.4 & 0.2\\\\\n", "\t 4.9 & 3.0 & 1.4 & 0.2\\\\\n", "\t 4.7 & 3.2 & 1.3 & 0.2\\\\\n", "\t 4.6 & 3.1 & 1.5 & 0.2\\\\\n", "\t 5.0 & 3.6 & 1.4 & 0.2\\\\\n", "\t 5.4 & 3.9 & 1.7 & 0.4\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 4\n", "\n", "| Sepal.Length <dbl> | Sepal.Width <dbl> | Petal.Length <dbl> | Petal.Width <dbl> |\n", "|---|---|---|---|\n", "| 5.1 | 3.5 | 1.4 | 0.2 |\n", "| 4.9 | 3.0 | 1.4 | 0.2 |\n", "| 4.7 | 3.2 | 1.3 | 0.2 |\n", "| 4.6 | 3.1 | 1.5 | 0.2 |\n", "| 5.0 | 3.6 | 1.4 | 0.2 |\n", "| 5.4 | 3.9 | 1.7 | 0.4 |\n", "\n" ], "text/plain": [ " Sepal.Length Sepal.Width Petal.Length Petal.Width\n", "1 5.1 3.5 1.4 0.2 \n", "2 4.9 3.0 1.4 0.2 \n", "3 4.7 3.2 1.3 0.2 \n", "4 4.6 3.1 1.5 0.2 \n", "5 5.0 3.6 1.4 0.2 \n", "6 5.4 3.9 1.7 0.4 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# data.frame转换成tibble\n", "t1 <- iris[1:6, 1:4]\n", "class(t1)\n", "\n", "as_tibble(t1)" ] }, { "cell_type": "code", "execution_count": 98, "id": "a1c56b91", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 5 × 1
value
<int>
1
2
3
4
5
\n" ], "text/latex": [ "A tibble: 5 × 1\n", "\\begin{tabular}{l}\n", " value\\\\\n", " \\\\\n", "\\hline\n", "\t 1\\\\\n", "\t 2\\\\\n", "\t 3\\\\\n", "\t 4\\\\\n", "\t 5\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 5 × 1\n", "\n", "| value <int> |\n", "|---|\n", "| 1 |\n", "| 2 |\n", "| 3 |\n", "| 4 |\n", "| 5 |\n", "\n" ], "text/plain": [ " value\n", "1 1 \n", "2 2 \n", "3 3 \n", "4 4 \n", "5 5 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# vector转型到tibble\n", "x <- as_tibble(1:5)\n", "x" ] }, { "cell_type": "code", "execution_count": 99, "id": "754d19e5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 6 × 3
xyz
<int><dbl><int>
10.18114886
20.18142345
30.96607584
40.73225633
50.51794242
60.05052041
\n" ], "text/latex": [ "A tibble: 6 × 3\n", "\\begin{tabular}{lll}\n", " x & y & z\\\\\n", " & & \\\\\n", "\\hline\n", "\t 1 & 0.1811488 & 6\\\\\n", "\t 2 & 0.1814234 & 5\\\\\n", "\t 3 & 0.9660758 & 4\\\\\n", "\t 4 & 0.7322563 & 3\\\\\n", "\t 5 & 0.5179424 & 2\\\\\n", "\t 6 & 0.0505204 & 1\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 6 × 3\n", "\n", "| x <int> | y <dbl> | z <int> |\n", "|---|---|---|\n", "| 1 | 0.1811488 | 6 |\n", "| 2 | 0.1814234 | 5 |\n", "| 3 | 0.9660758 | 4 |\n", "| 4 | 0.7322563 | 3 |\n", "| 5 | 0.5179424 | 2 |\n", "| 6 | 0.0505204 | 1 |\n", "\n" ], "text/plain": [ " x y z\n", "1 1 0.1811488 6\n", "2 2 0.1814234 5\n", "3 3 0.9660758 4\n", "4 4 0.7322563 3\n", "5 5 0.5179424 2\n", "6 6 0.0505204 1" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 把list转型为tibble\n", "df <- as_tibble(list(x = 1:6, y = runif(6), z= 6:1))\n", "df\n", "# 把tibble再转为list? as.list(df)" ] }, { "cell_type": "code", "execution_count": 102, "id": "416765cd", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A matrix: 3 × 5 of type dbl
-0.7579117 1.42382701.2555227-0.5938130 1.0590847
0.2615187 0.63555970.7695958 0.3268994-1.3785640
-1.2224583-0.93557170.5463617 0.5253380-0.9228583
\n" ], "text/latex": [ "A matrix: 3 × 5 of type dbl\n", "\\begin{tabular}{lllll}\n", "\t -0.7579117 & 1.4238270 & 1.2555227 & -0.5938130 & 1.0590847\\\\\n", "\t 0.2615187 & 0.6355597 & 0.7695958 & 0.3268994 & -1.3785640\\\\\n", "\t -1.2224583 & -0.9355717 & 0.5463617 & 0.5253380 & -0.9228583\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A matrix: 3 × 5 of type dbl\n", "\n", "| -0.7579117 | 1.4238270 | 1.2555227 | -0.5938130 | 1.0590847 |\n", "| 0.2615187 | 0.6355597 | 0.7695958 | 0.3268994 | -1.3785640 |\n", "| -1.2224583 | -0.9355717 | 0.5463617 | 0.5253380 | -0.9228583 |\n", "\n" ], "text/plain": [ " [,1] [,2] [,3] [,4] [,5] \n", "[1,] -0.7579117 1.4238270 1.2555227 -0.5938130 1.0590847\n", "[2,] 0.2615187 0.6355597 0.7695958 0.3268994 -1.3785640\n", "[3,] -1.2224583 -0.9355717 0.5463617 0.5253380 -0.9228583" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 3 × 5
V1V2V3V4V5
<dbl><dbl><dbl><dbl><dbl>
-0.7579117 1.42382701.2555227-0.5938130 1.0590847
0.2615187 0.63555970.7695958 0.3268994-1.3785640
-1.2224583-0.93557170.5463617 0.5253380-0.9228583
\n" ], "text/latex": [ "A tibble: 3 × 5\n", "\\begin{tabular}{lllll}\n", " V1 & V2 & V3 & V4 & V5\\\\\n", " & & & & \\\\\n", "\\hline\n", "\t -0.7579117 & 1.4238270 & 1.2555227 & -0.5938130 & 1.0590847\\\\\n", "\t 0.2615187 & 0.6355597 & 0.7695958 & 0.3268994 & -1.3785640\\\\\n", "\t -1.2224583 & -0.9355717 & 0.5463617 & 0.5253380 & -0.9228583\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 3 × 5\n", "\n", "| V1 <dbl> | V2 <dbl> | V3 <dbl> | V4 <dbl> | V5 <dbl> |\n", "|---|---|---|---|---|\n", "| -0.7579117 | 1.4238270 | 1.2555227 | -0.5938130 | 1.0590847 |\n", "| 0.2615187 | 0.6355597 | 0.7695958 | 0.3268994 | -1.3785640 |\n", "| -1.2224583 | -0.9355717 | 0.5463617 | 0.5253380 | -0.9228583 |\n", "\n" ], "text/plain": [ " V1 V2 V3 V4 V5 \n", "1 -0.7579117 1.4238270 1.2555227 -0.5938130 1.0590847\n", "2 0.2615187 0.6355597 0.7695958 0.3268994 -1.3785640\n", "3 -1.2224583 -0.9355717 0.5463617 0.5253380 -0.9228583" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 把matrix转型为tibble\n", "m <- matrix(rnorm(15), ncol=5)\n", "m\n", "as_tibble(m)\n", "# tibble转回matrix? as.matrix(df)" ] }, { "cell_type": "markdown", "id": "c41a9c4e", "metadata": {}, "source": [ "### 3 `tibble`简单操作\n", "- 增加一列\n", " - `add_column()`\n", " - `mutate()`\n", "- 增加一行\n", " - `add_row()` 默认加在最后\n", " - `.before=n`指定加在哪一行" ] }, { "cell_type": "code", "execution_count": 103, "id": "21b2fea9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\n", "
A tibble: 2 × 2
xy
<int><int>
12
21
\n" ], "text/latex": [ "A tibble: 2 × 2\n", "\\begin{tabular}{ll}\n", " x & y\\\\\n", " & \\\\\n", "\\hline\n", "\t 1 & 2\\\\\n", "\t 2 & 1\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 2 × 2\n", "\n", "| x <int> | y <int> |\n", "|---|---|\n", "| 1 | 2 |\n", "| 2 | 1 |\n", "\n" ], "text/plain": [ " x y\n", "1 1 2\n", "2 2 1" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 构建一个简单的数据框\n", "df <- tibble(\n", " x = 1:2,\n", " y = 2:1\n", ")\n", "df" ] }, { "cell_type": "code", "execution_count": 106, "id": "8e09ed6d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\n", "
A tibble: 2 × 4
xyzw
<int><int><int><dbl>
1200
2110
\n" ], "text/latex": [ "A tibble: 2 × 4\n", "\\begin{tabular}{llll}\n", " x & y & z & w\\\\\n", " & & & \\\\\n", "\\hline\n", "\t 1 & 2 & 0 & 0\\\\\n", "\t 2 & 1 & 1 & 0\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 2 × 4\n", "\n", "| x <int> | y <int> | z <int> | w <dbl> |\n", "|---|---|---|---|\n", "| 1 | 2 | 0 | 0 |\n", "| 2 | 1 | 1 | 0 |\n", "\n" ], "text/plain": [ " x y z w\n", "1 1 2 0 0\n", "2 2 1 1 0" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\n", "
A tibble: 2 × 4
xyzw
<int><int><int><dbl>
1200
2110
\n" ], "text/latex": [ "A tibble: 2 × 4\n", "\\begin{tabular}{llll}\n", " x & y & z & w\\\\\n", " & & & \\\\\n", "\\hline\n", "\t 1 & 2 & 0 & 0\\\\\n", "\t 2 & 1 & 1 & 0\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 2 × 4\n", "\n", "| x <int> | y <int> | z <int> | w <dbl> |\n", "|---|---|---|---|\n", "| 1 | 2 | 0 | 0 |\n", "| 2 | 1 | 1 | 0 |\n", "\n" ], "text/plain": [ " x y z w\n", "1 1 2 0 0\n", "2 2 1 1 0" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 增加一列\n", "add_column(df, z = 0:1, w = 0)\n", "\n", "df %>% \n", " mutate(z = 0:1,\n", " w = 0)" ] }, { "cell_type": "code", "execution_count": 108, "id": "62f6ec1c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 3 × 2
xy
<dbl><dbl>
12
21
999
\n" ], "text/latex": [ "A tibble: 3 × 2\n", "\\begin{tabular}{ll}\n", " x & y\\\\\n", " & \\\\\n", "\\hline\n", "\t 1 & 2\\\\\n", "\t 2 & 1\\\\\n", "\t 99 & 9\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 3 × 2\n", "\n", "| x <dbl> | y <dbl> |\n", "|---|---|\n", "| 1 | 2 |\n", "| 2 | 1 |\n", "| 99 | 9 |\n", "\n" ], "text/plain": [ " x y\n", "1 1 2\n", "2 2 1\n", "3 99 9" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 3 × 2
xy
<dbl><dbl>
12
999
21
\n" ], "text/latex": [ "A tibble: 3 × 2\n", "\\begin{tabular}{ll}\n", " x & y\\\\\n", " & \\\\\n", "\\hline\n", "\t 1 & 2\\\\\n", "\t 99 & 9\\\\\n", "\t 2 & 1\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 3 × 2\n", "\n", "| x <dbl> | y <dbl> |\n", "|---|---|\n", "| 1 | 2 |\n", "| 99 | 9 |\n", "| 2 | 1 |\n", "\n" ], "text/plain": [ " x y\n", "1 1 2\n", "2 99 9\n", "3 2 1" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 增加一行\n", "add_row(df, x = 99, y = 9)\n", "\n", "# 在第二行，增加一行\n", "add_row(df, x = 99, y = 9, .before=2)" ] }, { "cell_type": "markdown", "id": "0348fe01", "metadata": {}, "source": [ "### 4 有用的函数`lst`\n", "- `lst`，创建一个`list`，具有`tibble`特性的`list`。" ] }, { "cell_type": "code", "execution_count": 110, "id": "38593657", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\t
$n
\n", "\t\t
5
\n", "\t
$x
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  1. 0.910465918714181
  2. 0.365922675235197
  3. 0.48560184543021
  4. 0.836525636259466
  5. 0.291983403731138
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TRUE
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\n" ], "text/latex": [ "\\begin{description}\n", "\\item[\\$n] 5\n", "\\item[\\$x] \\begin{enumerate*}\n", "\\item 0.910465918714181\n", "\\item 0.365922675235197\n", "\\item 0.48560184543021\n", "\\item 0.836525636259466\n", "\\item 0.291983403731138\n", "\\end{enumerate*}\n", "\n", "\\item[\\$y] TRUE\n", "\\end{description}\n" ], "text/markdown": [ "$n\n", ": 5\n", "$x\n", ": 1. 0.910465918714181\n", "2. 0.365922675235197\n", "3. 0.48560184543021\n", "4. 0.836525636259466\n", "5. 0.291983403731138\n", "\n", "\n", "\n", "$y\n", ": TRUE\n", "\n", "\n" ], "text/plain": [ "$n\n", "[1] 5\n", "\n", "$x\n", "[1] 0.9104659 0.3659227 0.4856018 0.8365256 0.2919834\n", "\n", "$y\n", "[1] TRUE\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tibble::lst(n = 5, x = runif(n), y = TRUE)" ] }, { "cell_type": "markdown", "id": "a20e1e53", "metadata": {}, "source": [ "### 5 有用的函数`enframe`\n", "- `enframe()`将矢量快速创建`tibble`，创建的`tibble`只有2列: `name`和`value`" ] }, { "cell_type": "code", "execution_count": 111, "id": "5ba33c92", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 3 × 2
namevalue
<int><int>
11
22
33
\n" ], "text/latex": [ "A tibble: 3 × 2\n", "\\begin{tabular}{ll}\n", " name & value\\\\\n", " & \\\\\n", "\\hline\n", "\t 1 & 1\\\\\n", "\t 2 & 2\\\\\n", "\t 3 & 3\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 3 × 2\n", "\n", "| name <int> | value <int> |\n", "|---|---|\n", "| 1 | 1 |\n", "| 2 | 2 |\n", "| 3 | 3 |\n", "\n" ], "text/plain": [ " name value\n", "1 1 1 \n", "2 2 2 \n", "3 3 3 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "enframe(1:3)" ] }, { "cell_type": "code", "execution_count": 112, "id": "f8eb0815", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 3 × 2
namevalue
<chr><dbl>
a5
b7
c9
\n" ], "text/latex": [ "A tibble: 3 × 2\n", "\\begin{tabular}{ll}\n", " name & value\\\\\n", " & \\\\\n", "\\hline\n", "\t a & 5\\\\\n", "\t b & 7\\\\\n", "\t c & 9\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 3 × 2\n", "\n", "| name <chr> | value <dbl> |\n", "|---|---|\n", "| a | 5 |\n", "| b | 7 |\n", "| c | 9 |\n", "\n" ], "text/plain": [ " name value\n", "1 a 5 \n", "2 b 7 \n", "3 c 9 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "enframe(c(a = 5, b = 7, c = 9))" ] }, { "cell_type": "markdown", "id": "60824c7c", "metadata": {}, "source": [ "### 6 有用的函数`deframe`\n", "- `deframe()`可以看做是`enframe()`的反操作，把`tibble`反向转成向量" ] }, { "cell_type": "code", "execution_count": 114, "id": "5c21672b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\n", "
A tibble: 2 × 2
namevalue
<chr><dbl>
a5
b7
\n" ], "text/latex": [ "A tibble: 2 × 2\n", "\\begin{tabular}{ll}\n", " name & value\\\\\n", " & \\\\\n", "\\hline\n", "\t a & 5\\\\\n", "\t b & 7\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 2 × 2\n", "\n", "| name <chr> | value <dbl> |\n", "|---|---|\n", "| a | 5 |\n", "| b | 7 |\n", "\n" ], "text/plain": [ " name value\n", "1 a 5 \n", "2 b 7 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
a
5
b
7
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[a] 5\n", "\\item[b] 7\n", "\\end{description*}\n" ], "text/markdown": [ "a\n", ": 5b\n", ": 7\n", "\n" ], "text/plain": [ "a b \n", "5 7 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df <- enframe(c(a = 5, b = 7))\n", "df\n", "\n", "deframe(df)" ] }, { "cell_type": "markdown", "id": "39a1db2e", "metadata": {}, "source": [ "### 7 读取文件\n", "- `read_csv()`读取文件时，生成的直接就是`tibble`" ] }, { "cell_type": "code", "execution_count": 120, "id": "89d59f36", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1mRows: \u001b[22m\u001b[34m2\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m3\u001b[39m\n", "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[39m\n", "\u001b[1mDelimiter:\u001b[22m \",\"\n", "\u001b[32mdbl\u001b[39m (3): a, b, c\n", "\n", "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n", "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\n", "
A spec_tbl_df: 2 × 3
abc
<dbl><dbl><dbl>
123
456
\n" ], "text/latex": [ "A spec\\_tbl\\_df: 2 × 3\n", "\\begin{tabular}{lll}\n", " a & b & c\\\\\n", " & & \\\\\n", "\\hline\n", "\t 1 & 2 & 3\\\\\n", "\t 4 & 5 & 6\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A spec_tbl_df: 2 × 3\n", "\n", "| a <dbl> | b <dbl> | c <dbl> |\n", "|---|---|---|\n", "| 1 | 2 | 3 |\n", "| 4 | 5 | 6 |\n", "\n" ], "text/plain": [ " a b c\n", "1 1 2 3\n", "2 4 5 6" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "read_csv(\"./test.csv\")" ] }, { "cell_type": "markdown", "id": "688266c7", "metadata": {}, "source": [ "## 关于行名\n", "`data.frame`是支持行名的，但`tibble`不支持行名，这也是两者不同的地方\n", "- `has_rownames(df)` 判断是否有行名\n", "- `rownames_to_column(df, var=\"rowname\")`把df的行名转换为单独的一列`rowname`，没行索引了就\n", "- `rowid_to_column(df, var=\"rowid\")`把df的把行索引转换为单独的一列,多一列`rowid`" ] }, { "cell_type": "code", "execution_count": 125, "id": "8c2a2d52", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 3 × 2
xy
<int><int>
A13
B22
C31
\n" ], "text/latex": [ "A data.frame: 3 × 2\n", "\\begin{tabular}{r|ll}\n", " & x & y\\\\\n", " & & \\\\\n", "\\hline\n", "\tA & 1 & 3\\\\\n", "\tB & 2 & 2\\\\\n", "\tC & 3 & 1\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 3 × 2\n", "\n", "| | x <int> | y <int> |\n", "|---|---|---|\n", "| A | 1 | 3 |\n", "| B | 2 | 2 |\n", "| C | 3 | 1 |\n", "\n" ], "text/plain": [ " x y\n", "A 1 3\n", "B 2 2\n", "C 3 1" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"判断是否有行名\"\n" ] }, { "data": { "text/html": [ "TRUE" ], "text/latex": [ "TRUE" ], "text/markdown": [ "TRUE" ], "text/plain": [ "[1] TRUE" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "“Setting row names on a tibble is deprecated.”\n" ] } ], "source": [ "# dataframe 支持行名\n", "df <- data.frame(x = 1:3, y = 3:1)\n", "\n", "row.names(df) <- LETTERS[1:3]\n", "df\n", "\n", "print(\"判断是否有行名\")\n", "has_rownames(df)\n", "\n", "# tibble 不支持行名\n", "tb <- tibble(x = 1:3, y = 3:1)\n", "\n", "row.names(tb) <- LETTERS[1:3]" ] }, { "cell_type": "markdown", "id": "4a75764b", "metadata": {}, "source": [ "需要注意的：\n", "\n", "- 有时候遇到含有行名的`data.frame`，转换成`tibble`后，**行名会被丢弃**\n", "- 如果想保留行名，就需要把行名转换成单独的一列" ] }, { "cell_type": "code", "execution_count": 133, "id": "dfa0c8bf", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 3 × 3
mpgcyldisp
<dbl><dbl><dbl>
Mazda RX421.06160
Mazda RX4 Wag21.06160
Datsun 71022.84108
\n" ], "text/latex": [ "A data.frame: 3 × 3\n", "\\begin{tabular}{r|lll}\n", " & mpg & cyl & disp\\\\\n", " & & & \\\\\n", "\\hline\n", "\tMazda RX4 & 21.0 & 6 & 160\\\\\n", "\tMazda RX4 Wag & 21.0 & 6 & 160\\\\\n", "\tDatsun 710 & 22.8 & 4 & 108\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 3 × 3\n", "\n", "| | mpg <dbl> | cyl <dbl> | disp <dbl> |\n", "|---|---|---|---|\n", "| Mazda RX4 | 21.0 | 6 | 160 |\n", "| Mazda RX4 Wag | 21.0 | 6 | 160 |\n", "| Datsun 710 | 22.8 | 4 | 108 |\n", "\n" ], "text/plain": [ " mpg cyl disp\n", "Mazda RX4 21.0 6 160 \n", "Mazda RX4 Wag 21.0 6 160 \n", "Datsun 710 22.8 4 108 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 3 × 4
myrowmpgcyldisp
<chr><dbl><dbl><dbl>
Mazda RX4 21.06160
Mazda RX4 Wag21.06160
Datsun 710 22.84108
\n" ], "text/latex": [ "A data.frame: 3 × 4\n", "\\begin{tabular}{llll}\n", " myrow & mpg & cyl & disp\\\\\n", " & & & \\\\\n", "\\hline\n", "\t Mazda RX4 & 21.0 & 6 & 160\\\\\n", "\t Mazda RX4 Wag & 21.0 & 6 & 160\\\\\n", "\t Datsun 710 & 22.8 & 4 & 108\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 3 × 4\n", "\n", "| myrow <chr> | mpg <dbl> | cyl <dbl> | disp <dbl> |\n", "|---|---|---|---|\n", "| Mazda RX4 | 21.0 | 6 | 160 |\n", "| Mazda RX4 Wag | 21.0 | 6 | 160 |\n", "| Datsun 710 | 22.8 | 4 | 108 |\n", "\n" ], "text/plain": [ " myrow mpg cyl disp\n", "1 Mazda RX4 21.0 6 160 \n", "2 Mazda RX4 Wag 21.0 6 160 \n", "3 Datsun 710 22.8 4 108 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 3 × 4
mpgcyldisprowname
<dbl><dbl><dbl><chr>
Mazda RX421.06160Mazda RX4
Mazda RX4 Wag21.06160Mazda RX4 Wag
Datsun 71022.84108Datsun 710
\n" ], "text/latex": [ "A data.frame: 3 × 4\n", "\\begin{tabular}{r|llll}\n", " & mpg & cyl & disp & rowname\\\\\n", " & & & & \\\\\n", "\\hline\n", "\tMazda RX4 & 21.0 & 6 & 160 & Mazda RX4 \\\\\n", "\tMazda RX4 Wag & 21.0 & 6 & 160 & Mazda RX4 Wag\\\\\n", "\tDatsun 710 & 22.8 & 4 & 108 & Datsun 710 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 3 × 4\n", "\n", "| | mpg <dbl> | cyl <dbl> | disp <dbl> | rowname <chr> |\n", "|---|---|---|---|---|\n", "| Mazda RX4 | 21.0 | 6 | 160 | Mazda RX4 |\n", "| Mazda RX4 Wag | 21.0 | 6 | 160 | Mazda RX4 Wag |\n", "| Datsun 710 | 22.8 | 4 | 108 | Datsun 710 |\n", "\n" ], "text/plain": [ " mpg cyl disp rowname \n", "Mazda RX4 21.0 6 160 Mazda RX4 \n", "Mazda RX4 Wag 21.0 6 160 Mazda RX4 Wag\n", "Datsun 710 22.8 4 108 Datsun 710 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 3 × 4
mpgcyldisprowname
<dbl><dbl><dbl><chr>
Mazda RX421.06160Mazda RX4
Mazda RX4 Wag21.06160Mazda RX4 Wag
Datsun 71022.84108Datsun 710
\n" ], "text/latex": [ "A data.frame: 3 × 4\n", "\\begin{tabular}{r|llll}\n", " & mpg & cyl & disp & rowname\\\\\n", " & & & & \\\\\n", "\\hline\n", "\tMazda RX4 & 21.0 & 6 & 160 & Mazda RX4 \\\\\n", "\tMazda RX4 Wag & 21.0 & 6 & 160 & Mazda RX4 Wag\\\\\n", "\tDatsun 710 & 22.8 & 4 & 108 & Datsun 710 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 3 × 4\n", "\n", "| | mpg <dbl> | cyl <dbl> | disp <dbl> | rowname <chr> |\n", "|---|---|---|---|---|\n", "| Mazda RX4 | 21.0 | 6 | 160 | Mazda RX4 |\n", "| Mazda RX4 Wag | 21.0 | 6 | 160 | Mazda RX4 Wag |\n", "| Datsun 710 | 22.8 | 4 | 108 | Datsun 710 |\n", "\n" ], "text/plain": [ " mpg cyl disp rowname \n", "Mazda RX4 21.0 6 160 Mazda RX4 \n", "Mazda RX4 Wag 21.0 6 160 Mazda RX4 Wag\n", "Datsun 710 22.8 4 108 Datsun 710 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df <- mtcars[1:3, 1:3]\n", "df\n", "\n", "# 把行名转换为单独的一列\n", "rownames_to_column(df, var = \"myrow\")\n", "\n", "# 这俩是添加一列，但行名还在\n", "df$rowname <- rownames(df)\n", "df\n", "\n", "df %>% \n", " mutate(rowname = rownames(df))" ] }, { "cell_type": "code", "execution_count": 135, "id": "cbfb9924", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 3 × 5
rowidmpgcyldisprowname
<int><dbl><dbl><dbl><chr>
121.06160Mazda RX4
221.06160Mazda RX4 Wag
322.84108Datsun 710
\n" ], "text/latex": [ "A data.frame: 3 × 5\n", "\\begin{tabular}{lllll}\n", " rowid & mpg & cyl & disp & rowname\\\\\n", " & & & & \\\\\n", "\\hline\n", "\t 1 & 21.0 & 6 & 160 & Mazda RX4 \\\\\n", "\t 2 & 21.0 & 6 & 160 & Mazda RX4 Wag\\\\\n", "\t 3 & 22.8 & 4 & 108 & Datsun 710 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 3 × 5\n", "\n", "| rowid <int> | mpg <dbl> | cyl <dbl> | disp <dbl> | rowname <chr> |\n", "|---|---|---|---|---|\n", "| 1 | 21.0 | 6 | 160 | Mazda RX4 |\n", "| 2 | 21.0 | 6 | 160 | Mazda RX4 Wag |\n", "| 3 | 22.8 | 4 | 108 | Datsun 710 |\n", "\n" ], "text/plain": [ " rowid mpg cyl disp rowname \n", "1 1 21.0 6 160 Mazda RX4 \n", "2 2 21.0 6 160 Mazda RX4 Wag\n", "3 3 22.8 4 108 Datsun 710 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 把行索引转换为单独的一列\n", "rowid_to_column(df, var=\"rowid\")" ] }, { "cell_type": "markdown", "id": "4ec595b6", "metadata": {}, "source": [ "## 修复列名\n", "规范的来说，数据框的列名应该是唯一。但现实中代码是人写的，因此可能会稀奇古怪的，所幸的是`tibble`也提供了人性化的解决方案\n", "- `.name_repair = \"check_unique\"` 检查列名唯一性，但不做修复（默认）\n", "\n", "- `.name_repair = \"minimal\"`， 不检查也不修复，维持现状\n", "\n", "- `.name_repair = \"unique\"` 修复列名，使得列名唯一且不为空\n", "\n", "- `.name_repair = \"universal\"` 修复列名，使得列名唯一且语法可读\n", "\n", "- `make.unique(.x, sep=\"_\")`指定修复函数" ] }, { "cell_type": "code", "execution_count": 136, "id": "5aa0f66b", "metadata": {}, "outputs": [ { "ename": "ERROR", "evalue": "\u001b[1m\u001b[33mError\u001b[39m in `tibble()`:\u001b[22m\n\u001b[1m\u001b[22m\u001b[33m!\u001b[39m Column name `x` must not be duplicated.\nUse `.name_repair` to specify repair.\n\u001b[1mCaused by error in `repaired_names()`:\u001b[22m\n\u001b[33m!\u001b[39m Names must be unique.\n\u001b[31m✖\u001b[39m These names are duplicated:\n * \"x\" at locations 1 and 2.\n", "output_type": "error", "traceback": [ "\u001b[1m\u001b[33mError\u001b[39m in `tibble()`:\u001b[22m\n\u001b[1m\u001b[22m\u001b[33m!\u001b[39m Column name `x` must not be duplicated.\nUse `.name_repair` to specify repair.\n\u001b[1mCaused by error in `repaired_names()`:\u001b[22m\n\u001b[33m!\u001b[39m Names must be unique.\n\u001b[31m✖\u001b[39m These names are duplicated:\n * \"x\" at locations 1 and 2.\nTraceback:\n", "1. tibble(x = 1, x = 2)", "2. tibble_quos(xs, .rows, .name_repair)", "3. set_repaired_names(output, repair_hint = TRUE, .name_repair = .name_repair, \n . call = call)", "4. repaired_names(names2(x), repair_hint, .name_repair = .name_repair, \n . quiet = quiet, call = call)", "5. subclass_name_repair_errors(name = name, details = details, repair_hint = repair_hint, \n . vec_as_names(name, repair = .name_repair, quiet = quiet || \n . !is_character(.name_repair)), call = call)", "6. withCallingHandlers(expr, vctrs_error_names_cannot_be_empty = function(cnd) {\n . abort_column_names_cannot_be_empty(detect_empty_names(name), \n . details = details, parent = cnd, repair_hint = repair_hint, \n . call = call)\n . }, vctrs_error_names_cannot_be_dot_dot = function(cnd) {\n . abort_column_names_cannot_be_dot_dot(detect_dot_dot(name), \n . parent = cnd, repair_hint = repair_hint, call = call)\n . }, vctrs_error_names_must_be_unique = function(cnd) {\n . abort_column_names_must_be_unique(detect_duplicates(name), \n . parent = cnd, repair_hint = repair_hint, call = call)\n . })", "7. vec_as_names(name, repair = .name_repair, quiet = quiet || !is_character(.name_repair))", "8. (function () \n . validate_unique(names = names, arg = arg, call = call))()", "9. validate_unique(names = names, arg = arg, call = call)", "10. stop_names_must_be_unique(names, arg, call = call)", "11. stop_names(class = \"vctrs_error_names_must_be_unique\", arg = arg, \n . names = names, call = call)", "12. stop_vctrs(class = c(class, \"vctrs_error_names\"), ..., call = call)", "13. abort(message, class = c(class, \"vctrs_error\"), ..., call = call)", "14. signal_abort(cnd, .file)", "15. signalCondition(cnd)", "16. (function (cnd) \n . {\n . abort_column_names_must_be_unique(detect_duplicates(name), \n . parent = cnd, repair_hint = repair_hint, call = call)\n . })(structure(list(message = \"\", trace = structure(list(call = list(\n . IRkernel::main(), kernel$run(), handle_shell(), executor$execute(msg), \n . tryCatch(evaluate(request$content$code, envir = .GlobalEnv, \n . output_handler = oh, stop_on_error = 1L), interrupt = function(cond) {\n . log_debug(\"Interrupt during execution\")\n . interrupted <<- TRUE\n . }, error = .self$handle_error), tryCatchList(expr, classes, \n . parentenv, handlers), tryCatchOne(tryCatchList(expr, \n . names[-nh], parentenv, handlers[-nh]), names[nh], parentenv, \n . handlers[[nh]]), doTryCatch(return(expr), name, parentenv, \n . handler), tryCatchList(expr, names[-nh], parentenv, handlers[-nh]), \n . tryCatchOne(expr, names, parentenv, handlers[[1L]]), doTryCatch(return(expr), \n . name, parentenv, handler), evaluate(request$content$code, \n . envir = .GlobalEnv, output_handler = oh, stop_on_error = 1L), \n . evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos, \n . debug = debug, last = i == length(out), use_try = stop_on_error != \n . 2L, keep_warning = keep_warning, keep_message = keep_message, \n . log_echo = log_echo, log_warning = log_warning, output_handler = output_handler, \n . include_timing = include_timing), timing_fn(handle(ev <- withCallingHandlers(withVisible(eval_with_user_handlers(expr, \n . envir, enclos, user_handlers)), warning = wHandler, error = eHandler, \n . message = mHandler))), handle(ev <- withCallingHandlers(withVisible(eval_with_user_handlers(expr, \n . envir, enclos, user_handlers)), warning = wHandler, error = eHandler, \n . message = mHandler)), try(f, silent = TRUE), tryCatch(expr, \n . error = function(e) {\n . call <- conditionCall(e)\n . if (!is.null(call)) {\n . if (identical(call[[1L]], quote(doTryCatch))) \n . call <- sys.call(-4L)\n . dcall <- deparse(call, nlines = 1L)\n . prefix <- paste(\"Error in\", dcall, \": \")\n . LONG <- 75L\n . sm <- strsplit(conditionMessage(e), \"\\n\")[[1L]]\n . w <- 14L + nchar(dcall, type = \"w\") + nchar(sm[1L], \n . type = \"w\")\n . if (is.na(w)) \n . w <- 14L + nchar(dcall, type = \"b\") + nchar(sm[1L], \n . type = \"b\")\n . if (w > LONG) \n . prefix <- paste0(prefix, \"\\n \")\n . }\n . else prefix <- \"Error : \"\n . msg <- paste0(prefix, conditionMessage(e), \"\\n\")\n . .Internal(seterrmessage(msg[1L]))\n . if (!silent && isTRUE(getOption(\"show.error.messages\"))) {\n . cat(msg, file = outFile)\n . .Internal(printDeferredWarnings())\n . }\n . invisible(structure(msg, class = \"try-error\", condition = e))\n . }), tryCatchList(expr, classes, parentenv, handlers), \n . tryCatchOne(expr, names, parentenv, handlers[[1L]]), doTryCatch(return(expr), \n . name, parentenv, handler), withCallingHandlers(withVisible(eval_with_user_handlers(expr, \n . envir, enclos, user_handlers)), warning = wHandler, error = eHandler, \n . message = mHandler), withVisible(eval_with_user_handlers(expr, \n . envir, enclos, user_handlers)), eval_with_user_handlers(expr, \n . envir, enclos, user_handlers), eval(expr, envir, enclos), \n . eval(expr, envir, enclos), tibble(x = 1, x = 2), tibble_quos(xs, \n . .rows, .name_repair), set_repaired_names(output, repair_hint = TRUE, \n . .name_repair = .name_repair, call = call), repaired_names(names2(x), \n . repair_hint, .name_repair = .name_repair, quiet = quiet, \n . call = call), subclass_name_repair_errors(name = name, \n . details = details, repair_hint = repair_hint, vec_as_names(name, \n . repair = .name_repair, quiet = quiet || !is_character(.name_repair)), \n . call = call), withCallingHandlers(expr, vctrs_error_names_cannot_be_empty = function(cnd) {\n . abort_column_names_cannot_be_empty(detect_empty_names(name), \n . details = details, parent = cnd, repair_hint = repair_hint, \n . call = call)\n . }, vctrs_error_names_cannot_be_dot_dot = function(cnd) {\n . abort_column_names_cannot_be_dot_dot(detect_dot_dot(name), \n . parent = cnd, repair_hint = repair_hint, call = call)\n . }, vctrs_error_names_must_be_unique = function(cnd) {\n . abort_column_names_must_be_unique(detect_duplicates(name), \n . parent = cnd, repair_hint = repair_hint, call = call)\n . }), vec_as_names(name, repair = .name_repair, quiet = quiet || \n . !is_character(.name_repair)), ``(), validate_unique(names = names, \n . arg = arg, call = call), stop_names_must_be_unique(names, \n . arg, call = call), stop_names(class = \"vctrs_error_names_must_be_unique\", \n . arg = arg, names = names, call = call), stop_vctrs(class = c(class, \n . \"vctrs_error_names\"), ..., call = call), abort(message, \n . class = c(class, \"vctrs_error\"), ..., call = call)), \n . parent = c(0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 6L, 9L, 10L, 4L, \n . 12L, 13L, 13L, 15L, 16L, 17L, 18L, 19L, 13L, 13L, 13L, 23L, \n . 24L, 0L, 26L, 27L, 28L, 29L, 30L, 29L, 32L, 33L, 34L, 35L, \n . 36L, 37L), visible = c(TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, \n . TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, \n . TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, \n . TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, \n . FALSE, FALSE, FALSE), namespace = c(\"IRkernel\", NA, \"IRkernel\", \n . NA, \"base\", \"base\", \"base\", \"base\", \"base\", \"base\", \"base\", \n . \"evaluate\", \"evaluate\", \"evaluate\", \"evaluate\", \"base\", \"base\", \n . \"base\", \"base\", \"base\", \"base\", \"base\", \"evaluate\", \"base\", \n . \"base\", \"tibble\", \"tibble\", \"tibble\", \"tibble\", \"tibble\", \n . \"base\", \"vctrs\", \"vctrs\", \"vctrs\", \"vctrs\", \"vctrs\", \"vctrs\", \n . \"rlang\"), scope = c(\"::\", NA, \"local\", NA, \"::\", \"local\", \n . \"local\", \"local\", \"local\", \"local\", \"local\", \"::\", \":::\", \n . \"local\", \"local\", \"::\", \"::\", \"local\", \"local\", \"local\", \n . \"::\", \"::\", \":::\", \"::\", \"::\", \"::\", \":::\", \":::\", \":::\", \n . \":::\", \"::\", \"::\", \"local\", \":::\", \":::\", \":::\", \":::\", \"::\"\n . ), error_frame = c(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, \n . FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, \n . FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, \n . FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, \n . FALSE, FALSE, FALSE, FALSE, FALSE)), row.names = c(NA, -38L\n . ), version = 2L, class = c(\"rlang_trace\", \"rlib_trace\", \"tbl\", \n . \"data.frame\")), parent = NULL, arg = NULL, names = c(\"x\", \"x\"\n . ), rlang = list(inherit = TRUE), call = repaired_names(names2(x), \n . repair_hint, .name_repair = .name_repair, quiet = quiet, \n . call = call)), class = c(\"vctrs_error_names_must_be_unique\", \n . \"vctrs_error_names\", \"vctrs_error\", \"rlang_error\", \"error\", \"condition\"\n . )))", "17. abort_column_names_must_be_unique(detect_duplicates(name), parent = cnd, \n . repair_hint = repair_hint, call = call)", "18. tibble_abort(invalid_df(\"must not be duplicated\", names, use_repair(repair_hint), \n . message = \"Column name(s)\"), names = names, parent = parent, \n . call = call)", "19. abort(x, class, ..., call = call, parent = parent, use_cli_format = TRUE)", "20. signal_abort(cnd, .file)" ] } ], "source": [ "tibble(x = 1, x = 2)" ] }, { "cell_type": "code", "execution_count": 142, "id": "5d219771", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\n", "
A tibble: 1 × 2
xx
<dbl><dbl>
12
\n" ], "text/latex": [ "A tibble: 1 × 2\n", "\\begin{tabular}{ll}\n", " x & x\\\\\n", " & \\\\\n", "\\hline\n", "\t 1 & 2\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 1 × 2\n", "\n", "| x <dbl> | x <dbl> |\n", "|---|---|\n", "| 1 | 2 |\n", "\n" ], "text/plain": [ " x x\n", "1 1 2" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mNew names:\n", "\u001b[36m•\u001b[39m `x` -> `x...1`\n", "\u001b[36m•\u001b[39m `x` -> `x...2`\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\n", "
A tibble: 1 × 2
x...1x...2
<dbl><dbl>
12
\n" ], "text/latex": [ "A tibble: 1 × 2\n", "\\begin{tabular}{ll}\n", " x...1 & x...2\\\\\n", " & \\\\\n", "\\hline\n", "\t 1 & 2\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 1 × 2\n", "\n", "| x...1 <dbl> | x...2 <dbl> |\n", "|---|---|\n", "| 1 | 2 |\n", "\n" ], "text/plain": [ " x...1 x...2\n", "1 1 2 " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mNew names:\n", "\u001b[36m•\u001b[39m `x` -> `x...1`\n", "\u001b[36m•\u001b[39m `x` -> `x...2`\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\n", "
A tibble: 1 × 2
x...1x...2
<dbl><dbl>
12
\n" ], "text/latex": [ "A tibble: 1 × 2\n", "\\begin{tabular}{ll}\n", " x...1 & x...2\\\\\n", " & \\\\\n", "\\hline\n", "\t 1 & 2\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 1 × 2\n", "\n", "| x...1 <dbl> | x...2 <dbl> |\n", "|---|---|\n", "| 1 | 2 |\n", "\n" ], "text/plain": [ " x...1 x...2\n", "1 1 2 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tibble(x = 1, x = 2, .name_repair = \"minimal\")\n", "tibble(x = 1, x = 2, .name_repair = \"unique\")\n", "tibble(x = 1, x = 2, .name_repair = \"universal\")" ] }, { "cell_type": "code", "execution_count": 144, "id": "b8be944c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\n", "
A tibble: 1 × 2
xx.1
<dbl><dbl>
12
\n" ], "text/latex": [ "A tibble: 1 × 2\n", "\\begin{tabular}{ll}\n", " x & x.1\\\\\n", " & \\\\\n", "\\hline\n", "\t 1 & 2\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 1 × 2\n", "\n", "| x <dbl> | x.1 <dbl> |\n", "|---|---|\n", "| 1 | 2 |\n", "\n" ], "text/plain": [ " x x.1\n", "1 1 2 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\n", "
A tibble: 1 × 2
xx_1
<dbl><dbl>
12
\n" ], "text/latex": [ "A tibble: 1 × 2\n", "\\begin{tabular}{ll}\n", " x & x\\_1\\\\\n", " & \\\\\n", "\\hline\n", "\t 1 & 2\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 1 × 2\n", "\n", "| x <dbl> | x_1 <dbl> |\n", "|---|---|\n", "| 1 | 2 |\n", "\n" ], "text/plain": [ " x x_1\n", "1 1 2 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\n", "
A tibble: 1 × 2
xx.1
<dbl><dbl>
12
\n" ], "text/latex": [ "A tibble: 1 × 2\n", "\\begin{tabular}{ll}\n", " x & x.1\\\\\n", " & \\\\\n", "\\hline\n", "\t 1 & 2\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 1 × 2\n", "\n", "| x <dbl> | x.1 <dbl> |\n", "|---|---|\n", "| 1 | 2 |\n", "\n" ], "text/plain": [ " x x.1\n", "1 1 2 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tibble(x = 1, x = 2, .name_repair = make.unique) # 指定修复函数\n", "tibble(x = 1, x = 2, .name_repair = ~make.unique(.x, sep = \"_\"))\n", "tibble(x = 1, x = 2, .name_repair = ~make.names(., unique = TRUE))" ] }, { "cell_type": "markdown", "id": "f36ad11c", "metadata": {}, "source": [ "注意`make.unique(names, sep = \".\")`和`make.names(names, unique = FALSE, allow_ = TRUE)` 是基础包的函数" ] }, { "cell_type": "markdown", "id": "8802f5d5", "metadata": {}, "source": [ "## `List-columns`(列表列)\n", "`tibble` 本质上是向量构成的列表\n", " - ![image.png](image/tibble.png.1)\n", "\n", "大多情况下，我们接触到的向量是原子型向量(`atomic vectors`)，所谓原子型向量就是向量元素为单个值，比如 \"`a`\" 或者 `1`\n", " - ![image-2.png](image/tibble-atomic.png)\n", " \n", "`tibble`还有可以允许某一列为列表(`list`)，那么列表构成的列，称之为列表列（`list columns`）\n", " - ![image-3.png](image/tibble-list-col.png)\n", " \n", "这样一来，列表列非常灵活，因为列表元素可以是原子型向量、列表、矩阵或者小的`tibble`\n", " - ![image-4.png](image/tibble-list-col-vectors.png)" ] }, { "cell_type": "markdown", "id": "1b60095e", "metadata": {}, "source": [ "## `nested tibble`\n", "`tibble`的列表列装载数据的能力很强大，也很灵活。\n", "\n", "如何创建和操控有列表列的`tibble`。\n", "### 1 `creating`\n", "假定我们这里有一个`tibble`， 我们有三种方法可以创建列表列\n", "- `nest()`\n", "- `summarise()` and `list()`\n", "- `mutate()` and `map()`\n", " ### `tidyr::nest()`创建\n", "使用`tidyr::nest(data = c())`函数，创建有列表列的`tibble`, `data`指定那几列合成列表列`data`。![image.png](image/tibble-list-col-tibbles.png)\n", "\n", " 除了x列外的其他列就可用`nest(data = !x)`\n" ] }, { "cell_type": "code", "execution_count": 156, "id": "e66cdd12", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 6 × 4
speciesbill_length_mmbill_depth_mmbody_mass_g
<fct><dbl><dbl><int>
Adelie39.118.73750
Adelie39.517.43800
Adelie40.318.03250
Adelie36.719.33450
Adelie39.320.63650
Adelie38.917.83625
\n" ], "text/latex": [ "A tibble: 6 × 4\n", "\\begin{tabular}{llll}\n", " species & bill\\_length\\_mm & bill\\_depth\\_mm & body\\_mass\\_g\\\\\n", " & & & \\\\\n", "\\hline\n", "\t Adelie & 39.1 & 18.7 & 3750\\\\\n", "\t Adelie & 39.5 & 17.4 & 3800\\\\\n", "\t Adelie & 40.3 & 18.0 & 3250\\\\\n", "\t Adelie & 36.7 & 19.3 & 3450\\\\\n", "\t Adelie & 39.3 & 20.6 & 3650\\\\\n", "\t Adelie & 38.9 & 17.8 & 3625\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 6 × 4\n", "\n", "| species <fct> | bill_length_mm <dbl> | bill_depth_mm <dbl> | body_mass_g <int> |\n", "|---|---|---|---|\n", "| Adelie | 39.1 | 18.7 | 3750 |\n", "| Adelie | 39.5 | 17.4 | 3800 |\n", "| Adelie | 40.3 | 18.0 | 3250 |\n", "| Adelie | 36.7 | 19.3 | 3450 |\n", "| Adelie | 39.3 | 20.6 | 3650 |\n", "| Adelie | 38.9 | 17.8 | 3625 |\n", "\n" ], "text/plain": [ " species bill_length_mm bill_depth_mm body_mass_g\n", "1 Adelie 39.1 18.7 3750 \n", "2 Adelie 39.5 17.4 3800 \n", "3 Adelie 40.3 18.0 3250 \n", "4 Adelie 36.7 19.3 3450 \n", "5 Adelie 39.3 20.6 3650 \n", "6 Adelie 38.9 17.8 3625 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 3 × 2
speciesdata
<fct><list>
Adelie 39.1, 39.5, 40.3, 36.7, 39.3, 38.9, 39.2, 41.1, 38.6, 34.6, 36.6, 38.7, 42.5, 34.4, 46.0, 37.8, 37.7, 35.9, 38.2, 38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9, 36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36.0, 44.1, 37.0, 39.6, 41.1, 36.0, 42.3, 39.6, 40.1, 35.0, 42.0, 34.5, 41.4, 39.0, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4, 41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5, 42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3, 41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34.0, 39.6, 36.2, 40.8, 38.1, 40.3, 33.1, 43.2, 35.0, 41.0, 37.7, 37.8, 37.9, 39.7, 38.6, 38.2, 38.1, 43.2, 38.1, 45.6, 39.7, 42.2, 39.6, 42.7, 38.6, 37.3, 35.7, 41.1, 36.2, 37.7, 40.2, 41.4, 35.2, 40.6, 38.8, 41.5, 39.0, 44.1, 38.5, 43.1, 36.8, 37.5, 38.1, 41.1, 35.6, 40.2, 37.0, 39.7, 40.2, 40.6, 32.1, 40.7, 37.3, 39.0, 39.2, 36.6, 36.0, 37.8, 36.0, 41.5, 18.7, 17.4, 18.0, 19.3, 20.6, 17.8, 19.6, 17.6, 21.2, 21.1, 17.8, 19.0, 20.7, 18.4, 21.5, 18.3, 18.7, 19.2, 18.1, 17.2, 18.9, 18.6, 17.9, 18.6, 18.9, 16.7, 18.1, 17.8, 18.9, 17.0, 21.1, 20.0, 18.5, 19.3, 19.1, 18.0, 18.4, 18.5, 19.7, 16.9, 18.8, 19.0, 17.9, 21.2, 17.7, 18.9, 17.9, 19.5, 18.1, 18.6, 17.5, 18.8, 16.6, 19.1, 16.9, 21.1, 17.0, 18.2, 17.1, 18.0, 16.2, 19.1, 16.6, 19.4, 19.0, 18.4, 17.2, 18.9, 17.5, 18.5, 16.8, 19.4, 16.1, 19.1, 17.2, 17.6, 18.8, 19.4, 17.8, 20.3, 19.5, 18.6, 19.2, 18.8, 18.0, 18.1, 17.1, 18.1, 17.3, 18.9, 18.6, 18.5, 16.1, 18.5, 17.9, 20.0, 16.0, 20.0, 18.6, 18.9, 17.2, 20.0, 17.0, 19.0, 16.5, 20.3, 17.7, 19.5, 20.7, 18.3, 17.0, 20.5, 17.0, 18.6, 17.2, 19.8, 17.0, 18.5, 15.9, 19.0, 17.6, 18.3, 17.1, 18.0, 17.9, 19.2, 18.5, 18.5, 17.6, 17.5, 17.5, 20.1, 16.5, 17.9, 17.1, 17.2, 15.5, 17.0, 16.8, 18.7, 18.6, 18.4, 17.8, 18.1, 17.1, 18.5, 3750.0, 3800.0, 3250.0, 3450.0, 3650.0, 3625.0, 4675.0, 3200.0, 3800.0, 4400.0, 3700.0, 3450.0, 4500.0, 3325.0, 4200.0, 3400.0, 3600.0, 3800.0, 3950.0, 3800.0, 3800.0, 3550.0, 3200.0, 3150.0, 3950.0, 3250.0, 3900.0, 3300.0, 3900.0, 3325.0, 4150.0, 3950.0, 3550.0, 3300.0, 4650.0, 3150.0, 3900.0, 3100.0, 4400.0, 3000.0, 4600.0, 3425.0, 3450.0, 4150.0, 3500.0, 4300.0, 3450.0, 4050.0, 2900.0, 3700.0, 3550.0, 3800.0, 2850.0, 3750.0, 3150.0, 4400.0, 3600.0, 4050.0, 2850.0, 3950.0, 3350.0, 4100.0, 3050.0, 4450.0, 3600.0, 3900.0, 3550.0, 4150.0, 3700.0, 4250.0, 3700.0, 3900.0, 3550.0, 4000.0, 3200.0, 4700.0, 3800.0, 4200.0, 3350.0, 3550.0, 3800.0, 3500.0, 3950.0, 3600.0, 3550.0, 4300.0, 3400.0, 4450.0, 3300.0, 4300.0, 3700.0, 4350.0, 2900.0, 4100.0, 3725.0, 4725.0, 3075.0, 4250.0, 2925.0, 3550.0, 3750.0, 3900.0, 3175.0, 4775.0, 3825.0, 4600.0, 3200.0, 4275.0, 3900.0, 4075.0, 2900.0, 3775.0, 3350.0, 3325.0, 3150.0, 3500.0, 3450.0, 3875.0, 3050.0, 4000.0, 3275.0, 4300.0, 3050.0, 4000.0, 3325.0, 3500.0, 3500.0, 4475.0, 3425.0, 3900.0, 3175.0, 3975.0, 3400.0, 4250.0, 3400.0, 3475.0, 3050.0, 3725.0, 3000.0, 3650.0, 4250.0, 3475.0, 3450.0, 3750.0, 3700.0, 4000.0
Gentoo 46.1, 50.0, 48.7, 50.0, 47.6, 46.5, 45.4, 46.7, 43.3, 46.8, 40.9, 49.0, 45.5, 48.4, 45.8, 49.3, 42.0, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1, 47.8, 48.2, 50.0, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4, 42.6, 44.4, 44.0, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6, 45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.0, 43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 44.9, 50.8, 43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9, 13.2, 16.3, 14.1, 15.2, 14.5, 13.5, 14.6, 15.3, 13.4, 15.4, 13.7, 16.1, 13.7, 14.6, 14.6, 15.7, 13.5, 15.2, 14.5, 15.1, 14.3, 14.5, 14.5, 15.8, 13.1, 15.1, 15.0, 14.3, 15.3, 15.3, 14.2, 14.5, 17.0, 14.8, 16.3, 13.7, 17.3, 13.6, 15.7, 13.7, 16.0, 13.7, 15.0, 15.9, 13.9, 13.9, 15.9, 13.3, 15.8, 14.2, 14.1, 14.4, 15.0, 14.4, 15.4, 13.9, 15.0, 14.5, 15.3, 13.8, 14.9, 13.9, 15.7, 14.2, 16.8, 16.2, 14.2, 15.0, 15.0, 15.6, 15.6, 14.8, 15.0, 16.0, 14.2, 16.3, 13.8, 16.4, 14.5, 15.6, 14.6, 15.9, 13.8, 17.3, 14.4, 14.2, 14.0, 17.0, 15.0, 17.1, 14.5, 16.1, 14.7, 15.7, 15.8, 14.6, 14.4, 16.5, 15.0, 17.0, 15.5, 15.0, 16.1, 14.7, 15.8, 14.0, 15.1, 15.2, 15.9, 15.2, 16.3, 14.1, 16.0, 16.2, 13.7, 14.3, 15.7, 14.8, 16.1, 4500.0, 5700.0, 4450.0, 5700.0, 5400.0, 4550.0, 4800.0, 5200.0, 4400.0, 5150.0, 4650.0, 5550.0, 4650.0, 5850.0, 4200.0, 5850.0, 4150.0, 6300.0, 4800.0, 5350.0, 5700.0, 5000.0, 4400.0, 5050.0, 5000.0, 5100.0, 5650.0, 4600.0, 5550.0, 5250.0, 4700.0, 5050.0, 6050.0, 5150.0, 5400.0, 4950.0, 5250.0, 4350.0, 5350.0, 3950.0, 5700.0, 4300.0, 4750.0, 5550.0, 4900.0, 4200.0, 5400.0, 5100.0, 5300.0, 4850.0, 5300.0, 4400.0, 5000.0, 4900.0, 5050.0, 4300.0, 5000.0, 4450.0, 5550.0, 4200.0, 5300.0, 4400.0, 5650.0, 4700.0, 5700.0, 5800.0, 4700.0, 5550.0, 4750.0, 5000.0, 5100.0, 5200.0, 4700.0, 5800.0, 4600.0, 6000.0, 4750.0, 5950.0, 4625.0, 5450.0, 4725.0, 5350.0, 4750.0, 5600.0, 4600.0, 5300.0, 4875.0, 5550.0, 4950.0, 5400.0, 4750.0, 5650.0, 4850.0, 5200.0, 4925.0, 4875.0, 4625.0, 5250.0, 4850.0, 5600.0, 4975.0, 5500.0, 5500.0, 4700.0, 5500.0, 4575.0, 5500.0, 5000.0, 5950.0, 4650.0, 5500.0, 4375.0, 5850.0, 6000.0, 4925.0, 4850.0, 5750.0, 5200.0, 5400.0
Chinstrap46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 50.8, 50.2, 17.9, 19.5, 19.2, 18.7, 19.8, 17.8, 18.2, 18.2, 18.9, 19.9, 17.8, 20.3, 17.3, 18.1, 17.1, 19.6, 20.0, 17.8, 18.6, 18.2, 17.3, 17.5, 16.6, 19.4, 17.9, 19.0, 18.4, 19.0, 17.8, 20.0, 16.6, 20.8, 16.7, 18.8, 18.6, 16.8, 18.3, 20.7, 16.6, 19.9, 19.5, 17.5, 19.1, 17.0, 17.9, 18.5, 17.9, 19.6, 18.7, 17.3, 16.4, 19.0, 17.3, 19.7, 17.3, 18.8, 16.6, 19.9, 18.8, 19.4, 19.5, 16.5, 17.0, 19.8, 18.1, 18.2, 19.0, 18.7, 3500.0, 3900.0, 3650.0, 3525.0, 3725.0, 3950.0, 3250.0, 3750.0, 4150.0, 3700.0, 3800.0, 3775.0, 3700.0, 4050.0, 3575.0, 4050.0, 3300.0, 3700.0, 3450.0, 4400.0, 3600.0, 3400.0, 2900.0, 3800.0, 3300.0, 4150.0, 3400.0, 3800.0, 3700.0, 4550.0, 3200.0, 4300.0, 3350.0, 4100.0, 3600.0, 3900.0, 3850.0, 4800.0, 2700.0, 4500.0, 3950.0, 3650.0, 3550.0, 3500.0, 3675.0, 4450.0, 3400.0, 4300.0, 3250.0, 3675.0, 3325.0, 3950.0, 3600.0, 4050.0, 3350.0, 3450.0, 3250.0, 4050.0, 3800.0, 3525.0, 3950.0, 3650.0, 3650.0, 4000.0, 3400.0, 3775.0, 4100.0, 3775.0
\n" ], "text/latex": [ "A tibble: 3 × 2\n", "\\begin{tabular}{ll}\n", " species & data\\\\\n", " & \\\\\n", "\\hline\n", "\t Adelie & 39.1, 39.5, 40.3, 36.7, 39.3, 38.9, 39.2, 41.1, 38.6, 34.6, 36.6, 38.7, 42.5, 34.4, 46.0, 37.8, 37.7, 35.9, 38.2, 38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9, 36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36.0, 44.1, 37.0, 39.6, 41.1, 36.0, 42.3, 39.6, 40.1, 35.0, 42.0, 34.5, 41.4, 39.0, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4, 41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5, 42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3, 41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34.0, 39.6, 36.2, 40.8, 38.1, 40.3, 33.1, 43.2, 35.0, 41.0, 37.7, 37.8, 37.9, 39.7, 38.6, 38.2, 38.1, 43.2, 38.1, 45.6, 39.7, 42.2, 39.6, 42.7, 38.6, 37.3, 35.7, 41.1, 36.2, 37.7, 40.2, 41.4, 35.2, 40.6, 38.8, 41.5, 39.0, 44.1, 38.5, 43.1, 36.8, 37.5, 38.1, 41.1, 35.6, 40.2, 37.0, 39.7, 40.2, 40.6, 32.1, 40.7, 37.3, 39.0, 39.2, 36.6, 36.0, 37.8, 36.0, 41.5, 18.7, 17.4, 18.0, 19.3, 20.6, 17.8, 19.6, 17.6, 21.2, 21.1, 17.8, 19.0, 20.7, 18.4, 21.5, 18.3, 18.7, 19.2, 18.1, 17.2, 18.9, 18.6, 17.9, 18.6, 18.9, 16.7, 18.1, 17.8, 18.9, 17.0, 21.1, 20.0, 18.5, 19.3, 19.1, 18.0, 18.4, 18.5, 19.7, 16.9, 18.8, 19.0, 17.9, 21.2, 17.7, 18.9, 17.9, 19.5, 18.1, 18.6, 17.5, 18.8, 16.6, 19.1, 16.9, 21.1, 17.0, 18.2, 17.1, 18.0, 16.2, 19.1, 16.6, 19.4, 19.0, 18.4, 17.2, 18.9, 17.5, 18.5, 16.8, 19.4, 16.1, 19.1, 17.2, 17.6, 18.8, 19.4, 17.8, 20.3, 19.5, 18.6, 19.2, 18.8, 18.0, 18.1, 17.1, 18.1, 17.3, 18.9, 18.6, 18.5, 16.1, 18.5, 17.9, 20.0, 16.0, 20.0, 18.6, 18.9, 17.2, 20.0, 17.0, 19.0, 16.5, 20.3, 17.7, 19.5, 20.7, 18.3, 17.0, 20.5, 17.0, 18.6, 17.2, 19.8, 17.0, 18.5, 15.9, 19.0, 17.6, 18.3, 17.1, 18.0, 17.9, 19.2, 18.5, 18.5, 17.6, 17.5, 17.5, 20.1, 16.5, 17.9, 17.1, 17.2, 15.5, 17.0, 16.8, 18.7, 18.6, 18.4, 17.8, 18.1, 17.1, 18.5, 3750.0, 3800.0, 3250.0, 3450.0, 3650.0, 3625.0, 4675.0, 3200.0, 3800.0, 4400.0, 3700.0, 3450.0, 4500.0, 3325.0, 4200.0, 3400.0, 3600.0, 3800.0, 3950.0, 3800.0, 3800.0, 3550.0, 3200.0, 3150.0, 3950.0, 3250.0, 3900.0, 3300.0, 3900.0, 3325.0, 4150.0, 3950.0, 3550.0, 3300.0, 4650.0, 3150.0, 3900.0, 3100.0, 4400.0, 3000.0, 4600.0, 3425.0, 3450.0, 4150.0, 3500.0, 4300.0, 3450.0, 4050.0, 2900.0, 3700.0, 3550.0, 3800.0, 2850.0, 3750.0, 3150.0, 4400.0, 3600.0, 4050.0, 2850.0, 3950.0, 3350.0, 4100.0, 3050.0, 4450.0, 3600.0, 3900.0, 3550.0, 4150.0, 3700.0, 4250.0, 3700.0, 3900.0, 3550.0, 4000.0, 3200.0, 4700.0, 3800.0, 4200.0, 3350.0, 3550.0, 3800.0, 3500.0, 3950.0, 3600.0, 3550.0, 4300.0, 3400.0, 4450.0, 3300.0, 4300.0, 3700.0, 4350.0, 2900.0, 4100.0, 3725.0, 4725.0, 3075.0, 4250.0, 2925.0, 3550.0, 3750.0, 3900.0, 3175.0, 4775.0, 3825.0, 4600.0, 3200.0, 4275.0, 3900.0, 4075.0, 2900.0, 3775.0, 3350.0, 3325.0, 3150.0, 3500.0, 3450.0, 3875.0, 3050.0, 4000.0, 3275.0, 4300.0, 3050.0, 4000.0, 3325.0, 3500.0, 3500.0, 4475.0, 3425.0, 3900.0, 3175.0, 3975.0, 3400.0, 4250.0, 3400.0, 3475.0, 3050.0, 3725.0, 3000.0, 3650.0, 4250.0, 3475.0, 3450.0, 3750.0, 3700.0, 4000.0\\\\\n", "\t Gentoo & 46.1, 50.0, 48.7, 50.0, 47.6, 46.5, 45.4, 46.7, 43.3, 46.8, 40.9, 49.0, 45.5, 48.4, 45.8, 49.3, 42.0, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1, 47.8, 48.2, 50.0, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4, 42.6, 44.4, 44.0, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6, 45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.0, 43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 44.9, 50.8, 43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9, 13.2, 16.3, 14.1, 15.2, 14.5, 13.5, 14.6, 15.3, 13.4, 15.4, 13.7, 16.1, 13.7, 14.6, 14.6, 15.7, 13.5, 15.2, 14.5, 15.1, 14.3, 14.5, 14.5, 15.8, 13.1, 15.1, 15.0, 14.3, 15.3, 15.3, 14.2, 14.5, 17.0, 14.8, 16.3, 13.7, 17.3, 13.6, 15.7, 13.7, 16.0, 13.7, 15.0, 15.9, 13.9, 13.9, 15.9, 13.3, 15.8, 14.2, 14.1, 14.4, 15.0, 14.4, 15.4, 13.9, 15.0, 14.5, 15.3, 13.8, 14.9, 13.9, 15.7, 14.2, 16.8, 16.2, 14.2, 15.0, 15.0, 15.6, 15.6, 14.8, 15.0, 16.0, 14.2, 16.3, 13.8, 16.4, 14.5, 15.6, 14.6, 15.9, 13.8, 17.3, 14.4, 14.2, 14.0, 17.0, 15.0, 17.1, 14.5, 16.1, 14.7, 15.7, 15.8, 14.6, 14.4, 16.5, 15.0, 17.0, 15.5, 15.0, 16.1, 14.7, 15.8, 14.0, 15.1, 15.2, 15.9, 15.2, 16.3, 14.1, 16.0, 16.2, 13.7, 14.3, 15.7, 14.8, 16.1, 4500.0, 5700.0, 4450.0, 5700.0, 5400.0, 4550.0, 4800.0, 5200.0, 4400.0, 5150.0, 4650.0, 5550.0, 4650.0, 5850.0, 4200.0, 5850.0, 4150.0, 6300.0, 4800.0, 5350.0, 5700.0, 5000.0, 4400.0, 5050.0, 5000.0, 5100.0, 5650.0, 4600.0, 5550.0, 5250.0, 4700.0, 5050.0, 6050.0, 5150.0, 5400.0, 4950.0, 5250.0, 4350.0, 5350.0, 3950.0, 5700.0, 4300.0, 4750.0, 5550.0, 4900.0, 4200.0, 5400.0, 5100.0, 5300.0, 4850.0, 5300.0, 4400.0, 5000.0, 4900.0, 5050.0, 4300.0, 5000.0, 4450.0, 5550.0, 4200.0, 5300.0, 4400.0, 5650.0, 4700.0, 5700.0, 5800.0, 4700.0, 5550.0, 4750.0, 5000.0, 5100.0, 5200.0, 4700.0, 5800.0, 4600.0, 6000.0, 4750.0, 5950.0, 4625.0, 5450.0, 4725.0, 5350.0, 4750.0, 5600.0, 4600.0, 5300.0, 4875.0, 5550.0, 4950.0, 5400.0, 4750.0, 5650.0, 4850.0, 5200.0, 4925.0, 4875.0, 4625.0, 5250.0, 4850.0, 5600.0, 4975.0, 5500.0, 5500.0, 4700.0, 5500.0, 4575.0, 5500.0, 5000.0, 5950.0, 4650.0, 5500.0, 4375.0, 5850.0, 6000.0, 4925.0, 4850.0, 5750.0, 5200.0, 5400.0\\\\\n", "\t Chinstrap & 46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 50.8, 50.2, 17.9, 19.5, 19.2, 18.7, 19.8, 17.8, 18.2, 18.2, 18.9, 19.9, 17.8, 20.3, 17.3, 18.1, 17.1, 19.6, 20.0, 17.8, 18.6, 18.2, 17.3, 17.5, 16.6, 19.4, 17.9, 19.0, 18.4, 19.0, 17.8, 20.0, 16.6, 20.8, 16.7, 18.8, 18.6, 16.8, 18.3, 20.7, 16.6, 19.9, 19.5, 17.5, 19.1, 17.0, 17.9, 18.5, 17.9, 19.6, 18.7, 17.3, 16.4, 19.0, 17.3, 19.7, 17.3, 18.8, 16.6, 19.9, 18.8, 19.4, 19.5, 16.5, 17.0, 19.8, 18.1, 18.2, 19.0, 18.7, 3500.0, 3900.0, 3650.0, 3525.0, 3725.0, 3950.0, 3250.0, 3750.0, 4150.0, 3700.0, 3800.0, 3775.0, 3700.0, 4050.0, 3575.0, 4050.0, 3300.0, 3700.0, 3450.0, 4400.0, 3600.0, 3400.0, 2900.0, 3800.0, 3300.0, 4150.0, 3400.0, 3800.0, 3700.0, 4550.0, 3200.0, 4300.0, 3350.0, 4100.0, 3600.0, 3900.0, 3850.0, 4800.0, 2700.0, 4500.0, 3950.0, 3650.0, 3550.0, 3500.0, 3675.0, 4450.0, 3400.0, 4300.0, 3250.0, 3675.0, 3325.0, 3950.0, 3600.0, 4050.0, 3350.0, 3450.0, 3250.0, 4050.0, 3800.0, 3525.0, 3950.0, 3650.0, 3650.0, 4000.0, 3400.0, 3775.0, 4100.0, 3775.0\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 3 × 2\n", "\n", "| species <fct> | data <list> |\n", "|---|---|\n", "| Adelie | 39.1, 39.5, 40.3, 36.7, 39.3, 38.9, 39.2, 41.1, 38.6, 34.6, 36.6, 38.7, 42.5, 34.4, 46.0, 37.8, 37.7, 35.9, 38.2, 38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9, 36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36.0, 44.1, 37.0, 39.6, 41.1, 36.0, 42.3, 39.6, 40.1, 35.0, 42.0, 34.5, 41.4, 39.0, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4, 41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5, 42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3, 41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34.0, 39.6, 36.2, 40.8, 38.1, 40.3, 33.1, 43.2, 35.0, 41.0, 37.7, 37.8, 37.9, 39.7, 38.6, 38.2, 38.1, 43.2, 38.1, 45.6, 39.7, 42.2, 39.6, 42.7, 38.6, 37.3, 35.7, 41.1, 36.2, 37.7, 40.2, 41.4, 35.2, 40.6, 38.8, 41.5, 39.0, 44.1, 38.5, 43.1, 36.8, 37.5, 38.1, 41.1, 35.6, 40.2, 37.0, 39.7, 40.2, 40.6, 32.1, 40.7, 37.3, 39.0, 39.2, 36.6, 36.0, 37.8, 36.0, 41.5, 18.7, 17.4, 18.0, 19.3, 20.6, 17.8, 19.6, 17.6, 21.2, 21.1, 17.8, 19.0, 20.7, 18.4, 21.5, 18.3, 18.7, 19.2, 18.1, 17.2, 18.9, 18.6, 17.9, 18.6, 18.9, 16.7, 18.1, 17.8, 18.9, 17.0, 21.1, 20.0, 18.5, 19.3, 19.1, 18.0, 18.4, 18.5, 19.7, 16.9, 18.8, 19.0, 17.9, 21.2, 17.7, 18.9, 17.9, 19.5, 18.1, 18.6, 17.5, 18.8, 16.6, 19.1, 16.9, 21.1, 17.0, 18.2, 17.1, 18.0, 16.2, 19.1, 16.6, 19.4, 19.0, 18.4, 17.2, 18.9, 17.5, 18.5, 16.8, 19.4, 16.1, 19.1, 17.2, 17.6, 18.8, 19.4, 17.8, 20.3, 19.5, 18.6, 19.2, 18.8, 18.0, 18.1, 17.1, 18.1, 17.3, 18.9, 18.6, 18.5, 16.1, 18.5, 17.9, 20.0, 16.0, 20.0, 18.6, 18.9, 17.2, 20.0, 17.0, 19.0, 16.5, 20.3, 17.7, 19.5, 20.7, 18.3, 17.0, 20.5, 17.0, 18.6, 17.2, 19.8, 17.0, 18.5, 15.9, 19.0, 17.6, 18.3, 17.1, 18.0, 17.9, 19.2, 18.5, 18.5, 17.6, 17.5, 17.5, 20.1, 16.5, 17.9, 17.1, 17.2, 15.5, 17.0, 16.8, 18.7, 18.6, 18.4, 17.8, 18.1, 17.1, 18.5, 3750.0, 3800.0, 3250.0, 3450.0, 3650.0, 3625.0, 4675.0, 3200.0, 3800.0, 4400.0, 3700.0, 3450.0, 4500.0, 3325.0, 4200.0, 3400.0, 3600.0, 3800.0, 3950.0, 3800.0, 3800.0, 3550.0, 3200.0, 3150.0, 3950.0, 3250.0, 3900.0, 3300.0, 3900.0, 3325.0, 4150.0, 3950.0, 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45.5, 48.4, 45.8, 49.3, 42.0, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1, 47.8, 48.2, 50.0, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4, 42.6, 44.4, 44.0, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6, 45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.0, 43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 44.9, 50.8, 43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9, 13.2, 16.3, 14.1, 15.2, 14.5, 13.5, 14.6, 15.3, 13.4, 15.4, 13.7, 16.1, 13.7, 14.6, 14.6, 15.7, 13.5, 15.2, 14.5, 15.1, 14.3, 14.5, 14.5, 15.8, 13.1, 15.1, 15.0, 14.3, 15.3, 15.3, 14.2, 14.5, 17.0, 14.8, 16.3, 13.7, 17.3, 13.6, 15.7, 13.7, 16.0, 13.7, 15.0, 15.9, 13.9, 13.9, 15.9, 13.3, 15.8, 14.2, 14.1, 14.4, 15.0, 14.4, 15.4, 13.9, 15.0, 14.5, 15.3, 13.8, 14.9, 13.9, 15.7, 14.2, 16.8, 16.2, 14.2, 15.0, 15.0, 15.6, 15.6, 14.8, 15.0, 16.0, 14.2, 16.3, 13.8, 16.4, 14.5, 15.6, 14.6, 15.9, 13.8, 17.3, 14.4, 14.2, 14.0, 17.0, 15.0, 17.1, 14.5, 16.1, 14.7, 15.7, 15.8, 14.6, 14.4, 16.5, 15.0, 17.0, 15.5, 15.0, 16.1, 14.7, 15.8, 14.0, 15.1, 15.2, 15.9, 15.2, 16.3, 14.1, 16.0, 16.2, 13.7, 14.3, 15.7, 14.8, 16.1, 4500.0, 5700.0, 4450.0, 5700.0, 5400.0, 4550.0, 4800.0, 5200.0, 4400.0, 5150.0, 4650.0, 5550.0, 4650.0, 5850.0, 4200.0, 5850.0, 4150.0, 6300.0, 4800.0, 5350.0, 5700.0, 5000.0, 4400.0, 5050.0, 5000.0, 5100.0, 5650.0, 4600.0, 5550.0, 5250.0, 4700.0, 5050.0, 6050.0, 5150.0, 5400.0, 4950.0, 5250.0, 4350.0, 5350.0, 3950.0, 5700.0, 4300.0, 4750.0, 5550.0, 4900.0, 4200.0, 5400.0, 5100.0, 5300.0, 4850.0, 5300.0, 4400.0, 5000.0, 4900.0, 5050.0, 4300.0, 5000.0, 4450.0, 5550.0, 4200.0, 5300.0, 4400.0, 5650.0, 4700.0, 5700.0, 5800.0, 4700.0, 5550.0, 4750.0, 5000.0, 5100.0, 5200.0, 4700.0, 5800.0, 4600.0, 6000.0, 4750.0, 5950.0, 4625.0, 5450.0, 4725.0, 5350.0, 4750.0, 5600.0, 4600.0, 5300.0, 4875.0, 5550.0, 4950.0, 5400.0, 4750.0, 5650.0, 4850.0, 5200.0, 4925.0, 4875.0, 4625.0, 5250.0, 4850.0, 5600.0, 4975.0, 5500.0, 5500.0, 4700.0, 5500.0, 4575.0, 5500.0, 5000.0, 5950.0, 4650.0, 5500.0, 4375.0, 5850.0, 6000.0, 4925.0, 4850.0, 5750.0, 5200.0, 5400.0 |\n", "| Chinstrap | 46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 50.8, 50.2, 17.9, 19.5, 19.2, 18.7, 19.8, 17.8, 18.2, 18.2, 18.9, 19.9, 17.8, 20.3, 17.3, 18.1, 17.1, 19.6, 20.0, 17.8, 18.6, 18.2, 17.3, 17.5, 16.6, 19.4, 17.9, 19.0, 18.4, 19.0, 17.8, 20.0, 16.6, 20.8, 16.7, 18.8, 18.6, 16.8, 18.3, 20.7, 16.6, 19.9, 19.5, 17.5, 19.1, 17.0, 17.9, 18.5, 17.9, 19.6, 18.7, 17.3, 16.4, 19.0, 17.3, 19.7, 17.3, 18.8, 16.6, 19.9, 18.8, 19.4, 19.5, 16.5, 17.0, 19.8, 18.1, 18.2, 19.0, 18.7, 3500.0, 3900.0, 3650.0, 3525.0, 3725.0, 3950.0, 3250.0, 3750.0, 4150.0, 3700.0, 3800.0, 3775.0, 3700.0, 4050.0, 3575.0, 4050.0, 3300.0, 3700.0, 3450.0, 4400.0, 3600.0, 3400.0, 2900.0, 3800.0, 3300.0, 4150.0, 3400.0, 3800.0, 3700.0, 4550.0, 3200.0, 4300.0, 3350.0, 4100.0, 3600.0, 3900.0, 3850.0, 4800.0, 2700.0, 4500.0, 3950.0, 3650.0, 3550.0, 3500.0, 3675.0, 4450.0, 3400.0, 4300.0, 3250.0, 3675.0, 3325.0, 3950.0, 3600.0, 4050.0, 3350.0, 3450.0, 3250.0, 4050.0, 3800.0, 3525.0, 3950.0, 3650.0, 3650.0, 4000.0, 3400.0, 3775.0, 4100.0, 3775.0 |\n", "\n" ], "text/plain": [ " species \n", "1 Adelie \n", "2 Gentoo \n", "3 Chinstrap\n", " data \n", "1 39.1, 39.5, 40.3, 36.7, 39.3, 38.9, 39.2, 41.1, 38.6, 34.6, 36.6, 38.7, 42.5, 34.4, 46.0, 37.8, 37.7, 35.9, 38.2, 38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9, 36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36.0, 44.1, 37.0, 39.6, 41.1, 36.0, 42.3, 39.6, 40.1, 35.0, 42.0, 34.5, 41.4, 39.0, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4, 41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5, 42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3, 41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34.0, 39.6, 36.2, 40.8, 38.1, 40.3, 33.1, 43.2, 35.0, 41.0, 37.7, 37.8, 37.9, 39.7, 38.6, 38.2, 38.1, 43.2, 38.1, 45.6, 39.7, 42.2, 39.6, 42.7, 38.6, 37.3, 35.7, 41.1, 36.2, 37.7, 40.2, 41.4, 35.2, 40.6, 38.8, 41.5, 39.0, 44.1, 38.5, 43.1, 36.8, 37.5, 38.1, 41.1, 35.6, 40.2, 37.0, 39.7, 40.2, 40.6, 32.1, 40.7, 37.3, 39.0, 39.2, 36.6, 36.0, 37.8, 36.0, 41.5, 18.7, 17.4, 18.0, 19.3, 20.6, 17.8, 19.6, 17.6, 21.2, 21.1, 17.8, 19.0, 20.7, 18.4, 21.5, 18.3, 18.7, 19.2, 18.1, 17.2, 18.9, 18.6, 17.9, 18.6, 18.9, 16.7, 18.1, 17.8, 18.9, 17.0, 21.1, 20.0, 18.5, 19.3, 19.1, 18.0, 18.4, 18.5, 19.7, 16.9, 18.8, 19.0, 17.9, 21.2, 17.7, 18.9, 17.9, 19.5, 18.1, 18.6, 17.5, 18.8, 16.6, 19.1, 16.9, 21.1, 17.0, 18.2, 17.1, 18.0, 16.2, 19.1, 16.6, 19.4, 19.0, 18.4, 17.2, 18.9, 17.5, 18.5, 16.8, 19.4, 16.1, 19.1, 17.2, 17.6, 18.8, 19.4, 17.8, 20.3, 19.5, 18.6, 19.2, 18.8, 18.0, 18.1, 17.1, 18.1, 17.3, 18.9, 18.6, 18.5, 16.1, 18.5, 17.9, 20.0, 16.0, 20.0, 18.6, 18.9, 17.2, 20.0, 17.0, 19.0, 16.5, 20.3, 17.7, 19.5, 20.7, 18.3, 17.0, 20.5, 17.0, 18.6, 17.2, 19.8, 17.0, 18.5, 15.9, 19.0, 17.6, 18.3, 17.1, 18.0, 17.9, 19.2, 18.5, 18.5, 17.6, 17.5, 17.5, 20.1, 16.5, 17.9, 17.1, 17.2, 15.5, 17.0, 16.8, 18.7, 18.6, 18.4, 17.8, 18.1, 17.1, 18.5, 3750.0, 3800.0, 3250.0, 3450.0, 3650.0, 3625.0, 4675.0, 3200.0, 3800.0, 4400.0, 3700.0, 3450.0, 4500.0, 3325.0, 4200.0, 3400.0, 3600.0, 3800.0, 3950.0, 3800.0, 3800.0, 3550.0, 3200.0, 3150.0, 3950.0, 3250.0, 3900.0, 3300.0, 3900.0, 3325.0, 4150.0, 3950.0, 3550.0, 3300.0, 4650.0, 3150.0, 3900.0, 3100.0, 4400.0, 3000.0, 4600.0, 3425.0, 3450.0, 4150.0, 3500.0, 4300.0, 3450.0, 4050.0, 2900.0, 3700.0, 3550.0, 3800.0, 2850.0, 3750.0, 3150.0, 4400.0, 3600.0, 4050.0, 2850.0, 3950.0, 3350.0, 4100.0, 3050.0, 4450.0, 3600.0, 3900.0, 3550.0, 4150.0, 3700.0, 4250.0, 3700.0, 3900.0, 3550.0, 4000.0, 3200.0, 4700.0, 3800.0, 4200.0, 3350.0, 3550.0, 3800.0, 3500.0, 3950.0, 3600.0, 3550.0, 4300.0, 3400.0, 4450.0, 3300.0, 4300.0, 3700.0, 4350.0, 2900.0, 4100.0, 3725.0, 4725.0, 3075.0, 4250.0, 2925.0, 3550.0, 3750.0, 3900.0, 3175.0, 4775.0, 3825.0, 4600.0, 3200.0, 4275.0, 3900.0, 4075.0, 2900.0, 3775.0, 3350.0, 3325.0, 3150.0, 3500.0, 3450.0, 3875.0, 3050.0, 4000.0, 3275.0, 4300.0, 3050.0, 4000.0, 3325.0, 3500.0, 3500.0, 4475.0, 3425.0, 3900.0, 3175.0, 3975.0, 3400.0, 4250.0, 3400.0, 3475.0, 3050.0, 3725.0, 3000.0, 3650.0, 4250.0, 3475.0, 3450.0, 3750.0, 3700.0, 4000.0\n", "2 46.1, 50.0, 48.7, 50.0, 47.6, 46.5, 45.4, 46.7, 43.3, 46.8, 40.9, 49.0, 45.5, 48.4, 45.8, 49.3, 42.0, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1, 47.8, 48.2, 50.0, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4, 42.6, 44.4, 44.0, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6, 45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.0, 43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 44.9, 50.8, 43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9, 13.2, 16.3, 14.1, 15.2, 14.5, 13.5, 14.6, 15.3, 13.4, 15.4, 13.7, 16.1, 13.7, 14.6, 14.6, 15.7, 13.5, 15.2, 14.5, 15.1, 14.3, 14.5, 14.5, 15.8, 13.1, 15.1, 15.0, 14.3, 15.3, 15.3, 14.2, 14.5, 17.0, 14.8, 16.3, 13.7, 17.3, 13.6, 15.7, 13.7, 16.0, 13.7, 15.0, 15.9, 13.9, 13.9, 15.9, 13.3, 15.8, 14.2, 14.1, 14.4, 15.0, 14.4, 15.4, 13.9, 15.0, 14.5, 15.3, 13.8, 14.9, 13.9, 15.7, 14.2, 16.8, 16.2, 14.2, 15.0, 15.0, 15.6, 15.6, 14.8, 15.0, 16.0, 14.2, 16.3, 13.8, 16.4, 14.5, 15.6, 14.6, 15.9, 13.8, 17.3, 14.4, 14.2, 14.0, 17.0, 15.0, 17.1, 14.5, 16.1, 14.7, 15.7, 15.8, 14.6, 14.4, 16.5, 15.0, 17.0, 15.5, 15.0, 16.1, 14.7, 15.8, 14.0, 15.1, 15.2, 15.9, 15.2, 16.3, 14.1, 16.0, 16.2, 13.7, 14.3, 15.7, 14.8, 16.1, 4500.0, 5700.0, 4450.0, 5700.0, 5400.0, 4550.0, 4800.0, 5200.0, 4400.0, 5150.0, 4650.0, 5550.0, 4650.0, 5850.0, 4200.0, 5850.0, 4150.0, 6300.0, 4800.0, 5350.0, 5700.0, 5000.0, 4400.0, 5050.0, 5000.0, 5100.0, 5650.0, 4600.0, 5550.0, 5250.0, 4700.0, 5050.0, 6050.0, 5150.0, 5400.0, 4950.0, 5250.0, 4350.0, 5350.0, 3950.0, 5700.0, 4300.0, 4750.0, 5550.0, 4900.0, 4200.0, 5400.0, 5100.0, 5300.0, 4850.0, 5300.0, 4400.0, 5000.0, 4900.0, 5050.0, 4300.0, 5000.0, 4450.0, 5550.0, 4200.0, 5300.0, 4400.0, 5650.0, 4700.0, 5700.0, 5800.0, 4700.0, 5550.0, 4750.0, 5000.0, 5100.0, 5200.0, 4700.0, 5800.0, 4600.0, 6000.0, 4750.0, 5950.0, 4625.0, 5450.0, 4725.0, 5350.0, 4750.0, 5600.0, 4600.0, 5300.0, 4875.0, 5550.0, 4950.0, 5400.0, 4750.0, 5650.0, 4850.0, 5200.0, 4925.0, 4875.0, 4625.0, 5250.0, 4850.0, 5600.0, 4975.0, 5500.0, 5500.0, 4700.0, 5500.0, 4575.0, 5500.0, 5000.0, 5950.0, 4650.0, 5500.0, 4375.0, 5850.0, 6000.0, 4925.0, 4850.0, 5750.0, 5200.0, 5400.0 \n", "3 46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 50.8, 50.2, 17.9, 19.5, 19.2, 18.7, 19.8, 17.8, 18.2, 18.2, 18.9, 19.9, 17.8, 20.3, 17.3, 18.1, 17.1, 19.6, 20.0, 17.8, 18.6, 18.2, 17.3, 17.5, 16.6, 19.4, 17.9, 19.0, 18.4, 19.0, 17.8, 20.0, 16.6, 20.8, 16.7, 18.8, 18.6, 16.8, 18.3, 20.7, 16.6, 19.9, 19.5, 17.5, 19.1, 17.0, 17.9, 18.5, 17.9, 19.6, 18.7, 17.3, 16.4, 19.0, 17.3, 19.7, 17.3, 18.8, 16.6, 19.9, 18.8, 19.4, 19.5, 16.5, 17.0, 19.8, 18.1, 18.2, 19.0, 18.7, 3500.0, 3900.0, 3650.0, 3525.0, 3725.0, 3950.0, 3250.0, 3750.0, 4150.0, 3700.0, 3800.0, 3775.0, 3700.0, 4050.0, 3575.0, 4050.0, 3300.0, 3700.0, 3450.0, 4400.0, 3600.0, 3400.0, 2900.0, 3800.0, 3300.0, 4150.0, 3400.0, 3800.0, 3700.0, 4550.0, 3200.0, 4300.0, 3350.0, 4100.0, 3600.0, 3900.0, 3850.0, 4800.0, 2700.0, 4500.0, 3950.0, 3650.0, 3550.0, 3500.0, 3675.0, 4450.0, 3400.0, 4300.0, 3250.0, 3675.0, 3325.0, 3950.0, 3600.0, 4050.0, 3350.0, 3450.0, 3250.0, 4050.0, 3800.0, 3525.0, 3950.0, 3650.0, 3650.0, 4000.0, 3400.0, 3775.0, 4100.0, 3775.0 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# tidyr::nest()\n", "library(tidyverse)\n", "library(palmerpenguins)\n", "df <- penguins %>% \n", " drop_na() %>% \n", " select(species, bill_length_mm, bill_depth_mm, body_mass_g)\n", "df %>% head()\n", "\n", "tb <- df %>% \n", " tidyr::nest(data = c(bill_length_mm, bill_depth_mm, body_mass_g))\n", "tb %>% head()" ] }, { "cell_type": "markdown", "id": "76c659a9", "metadata": {}, "source": [ "`nest() `为每种species创建了一个小的`tibble`， 每个小的`tibble`对应一个species" ] }, { "cell_type": "code", "execution_count": 161, "id": "5e863193", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 6 × 3
bill_length_mmbill_depth_mmbody_mass_g
<dbl><dbl><int>
39.118.73750
39.517.43800
40.318.03250
36.719.33450
39.320.63650
38.917.83625
\n" ], "text/latex": [ "A tibble: 6 × 3\n", "\\begin{tabular}{lll}\n", " bill\\_length\\_mm & bill\\_depth\\_mm & body\\_mass\\_g\\\\\n", " & & \\\\\n", "\\hline\n", "\t 39.1 & 18.7 & 3750\\\\\n", "\t 39.5 & 17.4 & 3800\\\\\n", "\t 40.3 & 18.0 & 3250\\\\\n", "\t 36.7 & 19.3 & 3450\\\\\n", "\t 39.3 & 20.6 & 3650\\\\\n", "\t 38.9 & 17.8 & 3625\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 6 × 3\n", "\n", "| bill_length_mm <dbl> | bill_depth_mm <dbl> | body_mass_g <int> |\n", "|---|---|---|\n", "| 39.1 | 18.7 | 3750 |\n", "| 39.5 | 17.4 | 3800 |\n", "| 40.3 | 18.0 | 3250 |\n", "| 36.7 | 19.3 | 3450 |\n", "| 39.3 | 20.6 | 3650 |\n", "| 38.9 | 17.8 | 3625 |\n", "\n" ], "text/plain": [ " bill_length_mm bill_depth_mm body_mass_g\n", "1 39.1 18.7 3750 \n", "2 39.5 17.4 3800 \n", "3 40.3 18.0 3250 \n", "4 36.7 19.3 3450 \n", "5 39.3 20.6 3650 \n", "6 38.9 17.8 3625 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "'list'" ], "text/latex": [ "'list'" ], "text/markdown": [ "'list'" ], "text/plain": [ "[1] \"list\"" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "'list'" ], "text/latex": [ "'list'" ], "text/markdown": [ "'list'" ], "text/plain": [ "[1] \"list\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tb$data[[1]] %>% head()\n", "tb$data %>% typeof()\n", "tb$data %>% class()" ] }, { "cell_type": "code", "execution_count": 162, "id": "84748e7c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 3 × 2
speciesdata
<fct><list>
Adelie 39.1, 39.5, 40.3, 36.7, 39.3, 38.9, 39.2, 41.1, 38.6, 34.6, 36.6, 38.7, 42.5, 34.4, 46.0, 37.8, 37.7, 35.9, 38.2, 38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9, 36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36.0, 44.1, 37.0, 39.6, 41.1, 36.0, 42.3, 39.6, 40.1, 35.0, 42.0, 34.5, 41.4, 39.0, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4, 41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5, 42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3, 41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34.0, 39.6, 36.2, 40.8, 38.1, 40.3, 33.1, 43.2, 35.0, 41.0, 37.7, 37.8, 37.9, 39.7, 38.6, 38.2, 38.1, 43.2, 38.1, 45.6, 39.7, 42.2, 39.6, 42.7, 38.6, 37.3, 35.7, 41.1, 36.2, 37.7, 40.2, 41.4, 35.2, 40.6, 38.8, 41.5, 39.0, 44.1, 38.5, 43.1, 36.8, 37.5, 38.1, 41.1, 35.6, 40.2, 37.0, 39.7, 40.2, 40.6, 32.1, 40.7, 37.3, 39.0, 39.2, 36.6, 36.0, 37.8, 36.0, 41.5, 18.7, 17.4, 18.0, 19.3, 20.6, 17.8, 19.6, 17.6, 21.2, 21.1, 17.8, 19.0, 20.7, 18.4, 21.5, 18.3, 18.7, 19.2, 18.1, 17.2, 18.9, 18.6, 17.9, 18.6, 18.9, 16.7, 18.1, 17.8, 18.9, 17.0, 21.1, 20.0, 18.5, 19.3, 19.1, 18.0, 18.4, 18.5, 19.7, 16.9, 18.8, 19.0, 17.9, 21.2, 17.7, 18.9, 17.9, 19.5, 18.1, 18.6, 17.5, 18.8, 16.6, 19.1, 16.9, 21.1, 17.0, 18.2, 17.1, 18.0, 16.2, 19.1, 16.6, 19.4, 19.0, 18.4, 17.2, 18.9, 17.5, 18.5, 16.8, 19.4, 16.1, 19.1, 17.2, 17.6, 18.8, 19.4, 17.8, 20.3, 19.5, 18.6, 19.2, 18.8, 18.0, 18.1, 17.1, 18.1, 17.3, 18.9, 18.6, 18.5, 16.1, 18.5, 17.9, 20.0, 16.0, 20.0, 18.6, 18.9, 17.2, 20.0, 17.0, 19.0, 16.5, 20.3, 17.7, 19.5, 20.7, 18.3, 17.0, 20.5, 17.0, 18.6, 17.2, 19.8, 17.0, 18.5, 15.9, 19.0, 17.6, 18.3, 17.1, 18.0, 17.9, 19.2, 18.5, 18.5, 17.6, 17.5, 17.5, 20.1, 16.5, 17.9, 17.1, 17.2, 15.5, 17.0, 16.8, 18.7, 18.6, 18.4, 17.8, 18.1, 17.1, 18.5, 3750.0, 3800.0, 3250.0, 3450.0, 3650.0, 3625.0, 4675.0, 3200.0, 3800.0, 4400.0, 3700.0, 3450.0, 4500.0, 3325.0, 4200.0, 3400.0, 3600.0, 3800.0, 3950.0, 3800.0, 3800.0, 3550.0, 3200.0, 3150.0, 3950.0, 3250.0, 3900.0, 3300.0, 3900.0, 3325.0, 4150.0, 3950.0, 3550.0, 3300.0, 4650.0, 3150.0, 3900.0, 3100.0, 4400.0, 3000.0, 4600.0, 3425.0, 3450.0, 4150.0, 3500.0, 4300.0, 3450.0, 4050.0, 2900.0, 3700.0, 3550.0, 3800.0, 2850.0, 3750.0, 3150.0, 4400.0, 3600.0, 4050.0, 2850.0, 3950.0, 3350.0, 4100.0, 3050.0, 4450.0, 3600.0, 3900.0, 3550.0, 4150.0, 3700.0, 4250.0, 3700.0, 3900.0, 3550.0, 4000.0, 3200.0, 4700.0, 3800.0, 4200.0, 3350.0, 3550.0, 3800.0, 3500.0, 3950.0, 3600.0, 3550.0, 4300.0, 3400.0, 4450.0, 3300.0, 4300.0, 3700.0, 4350.0, 2900.0, 4100.0, 3725.0, 4725.0, 3075.0, 4250.0, 2925.0, 3550.0, 3750.0, 3900.0, 3175.0, 4775.0, 3825.0, 4600.0, 3200.0, 4275.0, 3900.0, 4075.0, 2900.0, 3775.0, 3350.0, 3325.0, 3150.0, 3500.0, 3450.0, 3875.0, 3050.0, 4000.0, 3275.0, 4300.0, 3050.0, 4000.0, 3325.0, 3500.0, 3500.0, 4475.0, 3425.0, 3900.0, 3175.0, 3975.0, 3400.0, 4250.0, 3400.0, 3475.0, 3050.0, 3725.0, 3000.0, 3650.0, 4250.0, 3475.0, 3450.0, 3750.0, 3700.0, 4000.0
Gentoo 46.1, 50.0, 48.7, 50.0, 47.6, 46.5, 45.4, 46.7, 43.3, 46.8, 40.9, 49.0, 45.5, 48.4, 45.8, 49.3, 42.0, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1, 47.8, 48.2, 50.0, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4, 42.6, 44.4, 44.0, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6, 45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.0, 43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 44.9, 50.8, 43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9, 13.2, 16.3, 14.1, 15.2, 14.5, 13.5, 14.6, 15.3, 13.4, 15.4, 13.7, 16.1, 13.7, 14.6, 14.6, 15.7, 13.5, 15.2, 14.5, 15.1, 14.3, 14.5, 14.5, 15.8, 13.1, 15.1, 15.0, 14.3, 15.3, 15.3, 14.2, 14.5, 17.0, 14.8, 16.3, 13.7, 17.3, 13.6, 15.7, 13.7, 16.0, 13.7, 15.0, 15.9, 13.9, 13.9, 15.9, 13.3, 15.8, 14.2, 14.1, 14.4, 15.0, 14.4, 15.4, 13.9, 15.0, 14.5, 15.3, 13.8, 14.9, 13.9, 15.7, 14.2, 16.8, 16.2, 14.2, 15.0, 15.0, 15.6, 15.6, 14.8, 15.0, 16.0, 14.2, 16.3, 13.8, 16.4, 14.5, 15.6, 14.6, 15.9, 13.8, 17.3, 14.4, 14.2, 14.0, 17.0, 15.0, 17.1, 14.5, 16.1, 14.7, 15.7, 15.8, 14.6, 14.4, 16.5, 15.0, 17.0, 15.5, 15.0, 16.1, 14.7, 15.8, 14.0, 15.1, 15.2, 15.9, 15.2, 16.3, 14.1, 16.0, 16.2, 13.7, 14.3, 15.7, 14.8, 16.1, 4500.0, 5700.0, 4450.0, 5700.0, 5400.0, 4550.0, 4800.0, 5200.0, 4400.0, 5150.0, 4650.0, 5550.0, 4650.0, 5850.0, 4200.0, 5850.0, 4150.0, 6300.0, 4800.0, 5350.0, 5700.0, 5000.0, 4400.0, 5050.0, 5000.0, 5100.0, 5650.0, 4600.0, 5550.0, 5250.0, 4700.0, 5050.0, 6050.0, 5150.0, 5400.0, 4950.0, 5250.0, 4350.0, 5350.0, 3950.0, 5700.0, 4300.0, 4750.0, 5550.0, 4900.0, 4200.0, 5400.0, 5100.0, 5300.0, 4850.0, 5300.0, 4400.0, 5000.0, 4900.0, 5050.0, 4300.0, 5000.0, 4450.0, 5550.0, 4200.0, 5300.0, 4400.0, 5650.0, 4700.0, 5700.0, 5800.0, 4700.0, 5550.0, 4750.0, 5000.0, 5100.0, 5200.0, 4700.0, 5800.0, 4600.0, 6000.0, 4750.0, 5950.0, 4625.0, 5450.0, 4725.0, 5350.0, 4750.0, 5600.0, 4600.0, 5300.0, 4875.0, 5550.0, 4950.0, 5400.0, 4750.0, 5650.0, 4850.0, 5200.0, 4925.0, 4875.0, 4625.0, 5250.0, 4850.0, 5600.0, 4975.0, 5500.0, 5500.0, 4700.0, 5500.0, 4575.0, 5500.0, 5000.0, 5950.0, 4650.0, 5500.0, 4375.0, 5850.0, 6000.0, 4925.0, 4850.0, 5750.0, 5200.0, 5400.0
Chinstrap46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 50.8, 50.2, 17.9, 19.5, 19.2, 18.7, 19.8, 17.8, 18.2, 18.2, 18.9, 19.9, 17.8, 20.3, 17.3, 18.1, 17.1, 19.6, 20.0, 17.8, 18.6, 18.2, 17.3, 17.5, 16.6, 19.4, 17.9, 19.0, 18.4, 19.0, 17.8, 20.0, 16.6, 20.8, 16.7, 18.8, 18.6, 16.8, 18.3, 20.7, 16.6, 19.9, 19.5, 17.5, 19.1, 17.0, 17.9, 18.5, 17.9, 19.6, 18.7, 17.3, 16.4, 19.0, 17.3, 19.7, 17.3, 18.8, 16.6, 19.9, 18.8, 19.4, 19.5, 16.5, 17.0, 19.8, 18.1, 18.2, 19.0, 18.7, 3500.0, 3900.0, 3650.0, 3525.0, 3725.0, 3950.0, 3250.0, 3750.0, 4150.0, 3700.0, 3800.0, 3775.0, 3700.0, 4050.0, 3575.0, 4050.0, 3300.0, 3700.0, 3450.0, 4400.0, 3600.0, 3400.0, 2900.0, 3800.0, 3300.0, 4150.0, 3400.0, 3800.0, 3700.0, 4550.0, 3200.0, 4300.0, 3350.0, 4100.0, 3600.0, 3900.0, 3850.0, 4800.0, 2700.0, 4500.0, 3950.0, 3650.0, 3550.0, 3500.0, 3675.0, 4450.0, 3400.0, 4300.0, 3250.0, 3675.0, 3325.0, 3950.0, 3600.0, 4050.0, 3350.0, 3450.0, 3250.0, 4050.0, 3800.0, 3525.0, 3950.0, 3650.0, 3650.0, 4000.0, 3400.0, 3775.0, 4100.0, 3775.0
\n" ], "text/latex": [ "A tibble: 3 × 2\n", "\\begin{tabular}{ll}\n", " species & data\\\\\n", " & \\\\\n", "\\hline\n", "\t Adelie & 39.1, 39.5, 40.3, 36.7, 39.3, 38.9, 39.2, 41.1, 38.6, 34.6, 36.6, 38.7, 42.5, 34.4, 46.0, 37.8, 37.7, 35.9, 38.2, 38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9, 36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36.0, 44.1, 37.0, 39.6, 41.1, 36.0, 42.3, 39.6, 40.1, 35.0, 42.0, 34.5, 41.4, 39.0, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4, 41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5, 42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3, 41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34.0, 39.6, 36.2, 40.8, 38.1, 40.3, 33.1, 43.2, 35.0, 41.0, 37.7, 37.8, 37.9, 39.7, 38.6, 38.2, 38.1, 43.2, 38.1, 45.6, 39.7, 42.2, 39.6, 42.7, 38.6, 37.3, 35.7, 41.1, 36.2, 37.7, 40.2, 41.4, 35.2, 40.6, 38.8, 41.5, 39.0, 44.1, 38.5, 43.1, 36.8, 37.5, 38.1, 41.1, 35.6, 40.2, 37.0, 39.7, 40.2, 40.6, 32.1, 40.7, 37.3, 39.0, 39.2, 36.6, 36.0, 37.8, 36.0, 41.5, 18.7, 17.4, 18.0, 19.3, 20.6, 17.8, 19.6, 17.6, 21.2, 21.1, 17.8, 19.0, 20.7, 18.4, 21.5, 18.3, 18.7, 19.2, 18.1, 17.2, 18.9, 18.6, 17.9, 18.6, 18.9, 16.7, 18.1, 17.8, 18.9, 17.0, 21.1, 20.0, 18.5, 19.3, 19.1, 18.0, 18.4, 18.5, 19.7, 16.9, 18.8, 19.0, 17.9, 21.2, 17.7, 18.9, 17.9, 19.5, 18.1, 18.6, 17.5, 18.8, 16.6, 19.1, 16.9, 21.1, 17.0, 18.2, 17.1, 18.0, 16.2, 19.1, 16.6, 19.4, 19.0, 18.4, 17.2, 18.9, 17.5, 18.5, 16.8, 19.4, 16.1, 19.1, 17.2, 17.6, 18.8, 19.4, 17.8, 20.3, 19.5, 18.6, 19.2, 18.8, 18.0, 18.1, 17.1, 18.1, 17.3, 18.9, 18.6, 18.5, 16.1, 18.5, 17.9, 20.0, 16.0, 20.0, 18.6, 18.9, 17.2, 20.0, 17.0, 19.0, 16.5, 20.3, 17.7, 19.5, 20.7, 18.3, 17.0, 20.5, 17.0, 18.6, 17.2, 19.8, 17.0, 18.5, 15.9, 19.0, 17.6, 18.3, 17.1, 18.0, 17.9, 19.2, 18.5, 18.5, 17.6, 17.5, 17.5, 20.1, 16.5, 17.9, 17.1, 17.2, 15.5, 17.0, 16.8, 18.7, 18.6, 18.4, 17.8, 18.1, 17.1, 18.5, 3750.0, 3800.0, 3250.0, 3450.0, 3650.0, 3625.0, 4675.0, 3200.0, 3800.0, 4400.0, 3700.0, 3450.0, 4500.0, 3325.0, 4200.0, 3400.0, 3600.0, 3800.0, 3950.0, 3800.0, 3800.0, 3550.0, 3200.0, 3150.0, 3950.0, 3250.0, 3900.0, 3300.0, 3900.0, 3325.0, 4150.0, 3950.0, 3550.0, 3300.0, 4650.0, 3150.0, 3900.0, 3100.0, 4400.0, 3000.0, 4600.0, 3425.0, 3450.0, 4150.0, 3500.0, 4300.0, 3450.0, 4050.0, 2900.0, 3700.0, 3550.0, 3800.0, 2850.0, 3750.0, 3150.0, 4400.0, 3600.0, 4050.0, 2850.0, 3950.0, 3350.0, 4100.0, 3050.0, 4450.0, 3600.0, 3900.0, 3550.0, 4150.0, 3700.0, 4250.0, 3700.0, 3900.0, 3550.0, 4000.0, 3200.0, 4700.0, 3800.0, 4200.0, 3350.0, 3550.0, 3800.0, 3500.0, 3950.0, 3600.0, 3550.0, 4300.0, 3400.0, 4450.0, 3300.0, 4300.0, 3700.0, 4350.0, 2900.0, 4100.0, 3725.0, 4725.0, 3075.0, 4250.0, 2925.0, 3550.0, 3750.0, 3900.0, 3175.0, 4775.0, 3825.0, 4600.0, 3200.0, 4275.0, 3900.0, 4075.0, 2900.0, 3775.0, 3350.0, 3325.0, 3150.0, 3500.0, 3450.0, 3875.0, 3050.0, 4000.0, 3275.0, 4300.0, 3050.0, 4000.0, 3325.0, 3500.0, 3500.0, 4475.0, 3425.0, 3900.0, 3175.0, 3975.0, 3400.0, 4250.0, 3400.0, 3475.0, 3050.0, 3725.0, 3000.0, 3650.0, 4250.0, 3475.0, 3450.0, 3750.0, 3700.0, 4000.0\\\\\n", "\t Gentoo & 46.1, 50.0, 48.7, 50.0, 47.6, 46.5, 45.4, 46.7, 43.3, 46.8, 40.9, 49.0, 45.5, 48.4, 45.8, 49.3, 42.0, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1, 47.8, 48.2, 50.0, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4, 42.6, 44.4, 44.0, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6, 45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.0, 43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 44.9, 50.8, 43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9, 13.2, 16.3, 14.1, 15.2, 14.5, 13.5, 14.6, 15.3, 13.4, 15.4, 13.7, 16.1, 13.7, 14.6, 14.6, 15.7, 13.5, 15.2, 14.5, 15.1, 14.3, 14.5, 14.5, 15.8, 13.1, 15.1, 15.0, 14.3, 15.3, 15.3, 14.2, 14.5, 17.0, 14.8, 16.3, 13.7, 17.3, 13.6, 15.7, 13.7, 16.0, 13.7, 15.0, 15.9, 13.9, 13.9, 15.9, 13.3, 15.8, 14.2, 14.1, 14.4, 15.0, 14.4, 15.4, 13.9, 15.0, 14.5, 15.3, 13.8, 14.9, 13.9, 15.7, 14.2, 16.8, 16.2, 14.2, 15.0, 15.0, 15.6, 15.6, 14.8, 15.0, 16.0, 14.2, 16.3, 13.8, 16.4, 14.5, 15.6, 14.6, 15.9, 13.8, 17.3, 14.4, 14.2, 14.0, 17.0, 15.0, 17.1, 14.5, 16.1, 14.7, 15.7, 15.8, 14.6, 14.4, 16.5, 15.0, 17.0, 15.5, 15.0, 16.1, 14.7, 15.8, 14.0, 15.1, 15.2, 15.9, 15.2, 16.3, 14.1, 16.0, 16.2, 13.7, 14.3, 15.7, 14.8, 16.1, 4500.0, 5700.0, 4450.0, 5700.0, 5400.0, 4550.0, 4800.0, 5200.0, 4400.0, 5150.0, 4650.0, 5550.0, 4650.0, 5850.0, 4200.0, 5850.0, 4150.0, 6300.0, 4800.0, 5350.0, 5700.0, 5000.0, 4400.0, 5050.0, 5000.0, 5100.0, 5650.0, 4600.0, 5550.0, 5250.0, 4700.0, 5050.0, 6050.0, 5150.0, 5400.0, 4950.0, 5250.0, 4350.0, 5350.0, 3950.0, 5700.0, 4300.0, 4750.0, 5550.0, 4900.0, 4200.0, 5400.0, 5100.0, 5300.0, 4850.0, 5300.0, 4400.0, 5000.0, 4900.0, 5050.0, 4300.0, 5000.0, 4450.0, 5550.0, 4200.0, 5300.0, 4400.0, 5650.0, 4700.0, 5700.0, 5800.0, 4700.0, 5550.0, 4750.0, 5000.0, 5100.0, 5200.0, 4700.0, 5800.0, 4600.0, 6000.0, 4750.0, 5950.0, 4625.0, 5450.0, 4725.0, 5350.0, 4750.0, 5600.0, 4600.0, 5300.0, 4875.0, 5550.0, 4950.0, 5400.0, 4750.0, 5650.0, 4850.0, 5200.0, 4925.0, 4875.0, 4625.0, 5250.0, 4850.0, 5600.0, 4975.0, 5500.0, 5500.0, 4700.0, 5500.0, 4575.0, 5500.0, 5000.0, 5950.0, 4650.0, 5500.0, 4375.0, 5850.0, 6000.0, 4925.0, 4850.0, 5750.0, 5200.0, 5400.0\\\\\n", "\t Chinstrap & 46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 50.8, 50.2, 17.9, 19.5, 19.2, 18.7, 19.8, 17.8, 18.2, 18.2, 18.9, 19.9, 17.8, 20.3, 17.3, 18.1, 17.1, 19.6, 20.0, 17.8, 18.6, 18.2, 17.3, 17.5, 16.6, 19.4, 17.9, 19.0, 18.4, 19.0, 17.8, 20.0, 16.6, 20.8, 16.7, 18.8, 18.6, 16.8, 18.3, 20.7, 16.6, 19.9, 19.5, 17.5, 19.1, 17.0, 17.9, 18.5, 17.9, 19.6, 18.7, 17.3, 16.4, 19.0, 17.3, 19.7, 17.3, 18.8, 16.6, 19.9, 18.8, 19.4, 19.5, 16.5, 17.0, 19.8, 18.1, 18.2, 19.0, 18.7, 3500.0, 3900.0, 3650.0, 3525.0, 3725.0, 3950.0, 3250.0, 3750.0, 4150.0, 3700.0, 3800.0, 3775.0, 3700.0, 4050.0, 3575.0, 4050.0, 3300.0, 3700.0, 3450.0, 4400.0, 3600.0, 3400.0, 2900.0, 3800.0, 3300.0, 4150.0, 3400.0, 3800.0, 3700.0, 4550.0, 3200.0, 4300.0, 3350.0, 4100.0, 3600.0, 3900.0, 3850.0, 4800.0, 2700.0, 4500.0, 3950.0, 3650.0, 3550.0, 3500.0, 3675.0, 4450.0, 3400.0, 4300.0, 3250.0, 3675.0, 3325.0, 3950.0, 3600.0, 4050.0, 3350.0, 3450.0, 3250.0, 4050.0, 3800.0, 3525.0, 3950.0, 3650.0, 3650.0, 4000.0, 3400.0, 3775.0, 4100.0, 3775.0\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 3 × 2\n", "\n", "| species <fct> | data <list> |\n", "|---|---|\n", "| Adelie | 39.1, 39.5, 40.3, 36.7, 39.3, 38.9, 39.2, 41.1, 38.6, 34.6, 36.6, 38.7, 42.5, 34.4, 46.0, 37.8, 37.7, 35.9, 38.2, 38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9, 36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36.0, 44.1, 37.0, 39.6, 41.1, 36.0, 42.3, 39.6, 40.1, 35.0, 42.0, 34.5, 41.4, 39.0, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4, 41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5, 42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3, 41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34.0, 39.6, 36.2, 40.8, 38.1, 40.3, 33.1, 43.2, 35.0, 41.0, 37.7, 37.8, 37.9, 39.7, 38.6, 38.2, 38.1, 43.2, 38.1, 45.6, 39.7, 42.2, 39.6, 42.7, 38.6, 37.3, 35.7, 41.1, 36.2, 37.7, 40.2, 41.4, 35.2, 40.6, 38.8, 41.5, 39.0, 44.1, 38.5, 43.1, 36.8, 37.5, 38.1, 41.1, 35.6, 40.2, 37.0, 39.7, 40.2, 40.6, 32.1, 40.7, 37.3, 39.0, 39.2, 36.6, 36.0, 37.8, 36.0, 41.5, 18.7, 17.4, 18.0, 19.3, 20.6, 17.8, 19.6, 17.6, 21.2, 21.1, 17.8, 19.0, 20.7, 18.4, 21.5, 18.3, 18.7, 19.2, 18.1, 17.2, 18.9, 18.6, 17.9, 18.6, 18.9, 16.7, 18.1, 17.8, 18.9, 17.0, 21.1, 20.0, 18.5, 19.3, 19.1, 18.0, 18.4, 18.5, 19.7, 16.9, 18.8, 19.0, 17.9, 21.2, 17.7, 18.9, 17.9, 19.5, 18.1, 18.6, 17.5, 18.8, 16.6, 19.1, 16.9, 21.1, 17.0, 18.2, 17.1, 18.0, 16.2, 19.1, 16.6, 19.4, 19.0, 18.4, 17.2, 18.9, 17.5, 18.5, 16.8, 19.4, 16.1, 19.1, 17.2, 17.6, 18.8, 19.4, 17.8, 20.3, 19.5, 18.6, 19.2, 18.8, 18.0, 18.1, 17.1, 18.1, 17.3, 18.9, 18.6, 18.5, 16.1, 18.5, 17.9, 20.0, 16.0, 20.0, 18.6, 18.9, 17.2, 20.0, 17.0, 19.0, 16.5, 20.3, 17.7, 19.5, 20.7, 18.3, 17.0, 20.5, 17.0, 18.6, 17.2, 19.8, 17.0, 18.5, 15.9, 19.0, 17.6, 18.3, 17.1, 18.0, 17.9, 19.2, 18.5, 18.5, 17.6, 17.5, 17.5, 20.1, 16.5, 17.9, 17.1, 17.2, 15.5, 17.0, 16.8, 18.7, 18.6, 18.4, 17.8, 18.1, 17.1, 18.5, 3750.0, 3800.0, 3250.0, 3450.0, 3650.0, 3625.0, 4675.0, 3200.0, 3800.0, 4400.0, 3700.0, 3450.0, 4500.0, 3325.0, 4200.0, 3400.0, 3600.0, 3800.0, 3950.0, 3800.0, 3800.0, 3550.0, 3200.0, 3150.0, 3950.0, 3250.0, 3900.0, 3300.0, 3900.0, 3325.0, 4150.0, 3950.0, 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45.5, 48.4, 45.8, 49.3, 42.0, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1, 47.8, 48.2, 50.0, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4, 42.6, 44.4, 44.0, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6, 45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.0, 43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 44.9, 50.8, 43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9, 13.2, 16.3, 14.1, 15.2, 14.5, 13.5, 14.6, 15.3, 13.4, 15.4, 13.7, 16.1, 13.7, 14.6, 14.6, 15.7, 13.5, 15.2, 14.5, 15.1, 14.3, 14.5, 14.5, 15.8, 13.1, 15.1, 15.0, 14.3, 15.3, 15.3, 14.2, 14.5, 17.0, 14.8, 16.3, 13.7, 17.3, 13.6, 15.7, 13.7, 16.0, 13.7, 15.0, 15.9, 13.9, 13.9, 15.9, 13.3, 15.8, 14.2, 14.1, 14.4, 15.0, 14.4, 15.4, 13.9, 15.0, 14.5, 15.3, 13.8, 14.9, 13.9, 15.7, 14.2, 16.8, 16.2, 14.2, 15.0, 15.0, 15.6, 15.6, 14.8, 15.0, 16.0, 14.2, 16.3, 13.8, 16.4, 14.5, 15.6, 14.6, 15.9, 13.8, 17.3, 14.4, 14.2, 14.0, 17.0, 15.0, 17.1, 14.5, 16.1, 14.7, 15.7, 15.8, 14.6, 14.4, 16.5, 15.0, 17.0, 15.5, 15.0, 16.1, 14.7, 15.8, 14.0, 15.1, 15.2, 15.9, 15.2, 16.3, 14.1, 16.0, 16.2, 13.7, 14.3, 15.7, 14.8, 16.1, 4500.0, 5700.0, 4450.0, 5700.0, 5400.0, 4550.0, 4800.0, 5200.0, 4400.0, 5150.0, 4650.0, 5550.0, 4650.0, 5850.0, 4200.0, 5850.0, 4150.0, 6300.0, 4800.0, 5350.0, 5700.0, 5000.0, 4400.0, 5050.0, 5000.0, 5100.0, 5650.0, 4600.0, 5550.0, 5250.0, 4700.0, 5050.0, 6050.0, 5150.0, 5400.0, 4950.0, 5250.0, 4350.0, 5350.0, 3950.0, 5700.0, 4300.0, 4750.0, 5550.0, 4900.0, 4200.0, 5400.0, 5100.0, 5300.0, 4850.0, 5300.0, 4400.0, 5000.0, 4900.0, 5050.0, 4300.0, 5000.0, 4450.0, 5550.0, 4200.0, 5300.0, 4400.0, 5650.0, 4700.0, 5700.0, 5800.0, 4700.0, 5550.0, 4750.0, 5000.0, 5100.0, 5200.0, 4700.0, 5800.0, 4600.0, 6000.0, 4750.0, 5950.0, 4625.0, 5450.0, 4725.0, 5350.0, 4750.0, 5600.0, 4600.0, 5300.0, 4875.0, 5550.0, 4950.0, 5400.0, 4750.0, 5650.0, 4850.0, 5200.0, 4925.0, 4875.0, 4625.0, 5250.0, 4850.0, 5600.0, 4975.0, 5500.0, 5500.0, 4700.0, 5500.0, 4575.0, 5500.0, 5000.0, 5950.0, 4650.0, 5500.0, 4375.0, 5850.0, 6000.0, 4925.0, 4850.0, 5750.0, 5200.0, 5400.0 |\n", "| Chinstrap | 46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 50.8, 50.2, 17.9, 19.5, 19.2, 18.7, 19.8, 17.8, 18.2, 18.2, 18.9, 19.9, 17.8, 20.3, 17.3, 18.1, 17.1, 19.6, 20.0, 17.8, 18.6, 18.2, 17.3, 17.5, 16.6, 19.4, 17.9, 19.0, 18.4, 19.0, 17.8, 20.0, 16.6, 20.8, 16.7, 18.8, 18.6, 16.8, 18.3, 20.7, 16.6, 19.9, 19.5, 17.5, 19.1, 17.0, 17.9, 18.5, 17.9, 19.6, 18.7, 17.3, 16.4, 19.0, 17.3, 19.7, 17.3, 18.8, 16.6, 19.9, 18.8, 19.4, 19.5, 16.5, 17.0, 19.8, 18.1, 18.2, 19.0, 18.7, 3500.0, 3900.0, 3650.0, 3525.0, 3725.0, 3950.0, 3250.0, 3750.0, 4150.0, 3700.0, 3800.0, 3775.0, 3700.0, 4050.0, 3575.0, 4050.0, 3300.0, 3700.0, 3450.0, 4400.0, 3600.0, 3400.0, 2900.0, 3800.0, 3300.0, 4150.0, 3400.0, 3800.0, 3700.0, 4550.0, 3200.0, 4300.0, 3350.0, 4100.0, 3600.0, 3900.0, 3850.0, 4800.0, 2700.0, 4500.0, 3950.0, 3650.0, 3550.0, 3500.0, 3675.0, 4450.0, 3400.0, 4300.0, 3250.0, 3675.0, 3325.0, 3950.0, 3600.0, 4050.0, 3350.0, 3450.0, 3250.0, 4050.0, 3800.0, 3525.0, 3950.0, 3650.0, 3650.0, 4000.0, 3400.0, 3775.0, 4100.0, 3775.0 |\n", "\n" ], "text/plain": [ " species \n", "1 Adelie \n", "2 Gentoo \n", "3 Chinstrap\n", " data \n", "1 39.1, 39.5, 40.3, 36.7, 39.3, 38.9, 39.2, 41.1, 38.6, 34.6, 36.6, 38.7, 42.5, 34.4, 46.0, 37.8, 37.7, 35.9, 38.2, 38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9, 36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36.0, 44.1, 37.0, 39.6, 41.1, 36.0, 42.3, 39.6, 40.1, 35.0, 42.0, 34.5, 41.4, 39.0, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4, 41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5, 42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3, 41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34.0, 39.6, 36.2, 40.8, 38.1, 40.3, 33.1, 43.2, 35.0, 41.0, 37.7, 37.8, 37.9, 39.7, 38.6, 38.2, 38.1, 43.2, 38.1, 45.6, 39.7, 42.2, 39.6, 42.7, 38.6, 37.3, 35.7, 41.1, 36.2, 37.7, 40.2, 41.4, 35.2, 40.6, 38.8, 41.5, 39.0, 44.1, 38.5, 43.1, 36.8, 37.5, 38.1, 41.1, 35.6, 40.2, 37.0, 39.7, 40.2, 40.6, 32.1, 40.7, 37.3, 39.0, 39.2, 36.6, 36.0, 37.8, 36.0, 41.5, 18.7, 17.4, 18.0, 19.3, 20.6, 17.8, 19.6, 17.6, 21.2, 21.1, 17.8, 19.0, 20.7, 18.4, 21.5, 18.3, 18.7, 19.2, 18.1, 17.2, 18.9, 18.6, 17.9, 18.6, 18.9, 16.7, 18.1, 17.8, 18.9, 17.0, 21.1, 20.0, 18.5, 19.3, 19.1, 18.0, 18.4, 18.5, 19.7, 16.9, 18.8, 19.0, 17.9, 21.2, 17.7, 18.9, 17.9, 19.5, 18.1, 18.6, 17.5, 18.8, 16.6, 19.1, 16.9, 21.1, 17.0, 18.2, 17.1, 18.0, 16.2, 19.1, 16.6, 19.4, 19.0, 18.4, 17.2, 18.9, 17.5, 18.5, 16.8, 19.4, 16.1, 19.1, 17.2, 17.6, 18.8, 19.4, 17.8, 20.3, 19.5, 18.6, 19.2, 18.8, 18.0, 18.1, 17.1, 18.1, 17.3, 18.9, 18.6, 18.5, 16.1, 18.5, 17.9, 20.0, 16.0, 20.0, 18.6, 18.9, 17.2, 20.0, 17.0, 19.0, 16.5, 20.3, 17.7, 19.5, 20.7, 18.3, 17.0, 20.5, 17.0, 18.6, 17.2, 19.8, 17.0, 18.5, 15.9, 19.0, 17.6, 18.3, 17.1, 18.0, 17.9, 19.2, 18.5, 18.5, 17.6, 17.5, 17.5, 20.1, 16.5, 17.9, 17.1, 17.2, 15.5, 17.0, 16.8, 18.7, 18.6, 18.4, 17.8, 18.1, 17.1, 18.5, 3750.0, 3800.0, 3250.0, 3450.0, 3650.0, 3625.0, 4675.0, 3200.0, 3800.0, 4400.0, 3700.0, 3450.0, 4500.0, 3325.0, 4200.0, 3400.0, 3600.0, 3800.0, 3950.0, 3800.0, 3800.0, 3550.0, 3200.0, 3150.0, 3950.0, 3250.0, 3900.0, 3300.0, 3900.0, 3325.0, 4150.0, 3950.0, 3550.0, 3300.0, 4650.0, 3150.0, 3900.0, 3100.0, 4400.0, 3000.0, 4600.0, 3425.0, 3450.0, 4150.0, 3500.0, 4300.0, 3450.0, 4050.0, 2900.0, 3700.0, 3550.0, 3800.0, 2850.0, 3750.0, 3150.0, 4400.0, 3600.0, 4050.0, 2850.0, 3950.0, 3350.0, 4100.0, 3050.0, 4450.0, 3600.0, 3900.0, 3550.0, 4150.0, 3700.0, 4250.0, 3700.0, 3900.0, 3550.0, 4000.0, 3200.0, 4700.0, 3800.0, 4200.0, 3350.0, 3550.0, 3800.0, 3500.0, 3950.0, 3600.0, 3550.0, 4300.0, 3400.0, 4450.0, 3300.0, 4300.0, 3700.0, 4350.0, 2900.0, 4100.0, 3725.0, 4725.0, 3075.0, 4250.0, 2925.0, 3550.0, 3750.0, 3900.0, 3175.0, 4775.0, 3825.0, 4600.0, 3200.0, 4275.0, 3900.0, 4075.0, 2900.0, 3775.0, 3350.0, 3325.0, 3150.0, 3500.0, 3450.0, 3875.0, 3050.0, 4000.0, 3275.0, 4300.0, 3050.0, 4000.0, 3325.0, 3500.0, 3500.0, 4475.0, 3425.0, 3900.0, 3175.0, 3975.0, 3400.0, 4250.0, 3400.0, 3475.0, 3050.0, 3725.0, 3000.0, 3650.0, 4250.0, 3475.0, 3450.0, 3750.0, 3700.0, 4000.0\n", "2 46.1, 50.0, 48.7, 50.0, 47.6, 46.5, 45.4, 46.7, 43.3, 46.8, 40.9, 49.0, 45.5, 48.4, 45.8, 49.3, 42.0, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1, 47.8, 48.2, 50.0, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4, 42.6, 44.4, 44.0, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6, 45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.0, 43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 44.9, 50.8, 43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9, 13.2, 16.3, 14.1, 15.2, 14.5, 13.5, 14.6, 15.3, 13.4, 15.4, 13.7, 16.1, 13.7, 14.6, 14.6, 15.7, 13.5, 15.2, 14.5, 15.1, 14.3, 14.5, 14.5, 15.8, 13.1, 15.1, 15.0, 14.3, 15.3, 15.3, 14.2, 14.5, 17.0, 14.8, 16.3, 13.7, 17.3, 13.6, 15.7, 13.7, 16.0, 13.7, 15.0, 15.9, 13.9, 13.9, 15.9, 13.3, 15.8, 14.2, 14.1, 14.4, 15.0, 14.4, 15.4, 13.9, 15.0, 14.5, 15.3, 13.8, 14.9, 13.9, 15.7, 14.2, 16.8, 16.2, 14.2, 15.0, 15.0, 15.6, 15.6, 14.8, 15.0, 16.0, 14.2, 16.3, 13.8, 16.4, 14.5, 15.6, 14.6, 15.9, 13.8, 17.3, 14.4, 14.2, 14.0, 17.0, 15.0, 17.1, 14.5, 16.1, 14.7, 15.7, 15.8, 14.6, 14.4, 16.5, 15.0, 17.0, 15.5, 15.0, 16.1, 14.7, 15.8, 14.0, 15.1, 15.2, 15.9, 15.2, 16.3, 14.1, 16.0, 16.2, 13.7, 14.3, 15.7, 14.8, 16.1, 4500.0, 5700.0, 4450.0, 5700.0, 5400.0, 4550.0, 4800.0, 5200.0, 4400.0, 5150.0, 4650.0, 5550.0, 4650.0, 5850.0, 4200.0, 5850.0, 4150.0, 6300.0, 4800.0, 5350.0, 5700.0, 5000.0, 4400.0, 5050.0, 5000.0, 5100.0, 5650.0, 4600.0, 5550.0, 5250.0, 4700.0, 5050.0, 6050.0, 5150.0, 5400.0, 4950.0, 5250.0, 4350.0, 5350.0, 3950.0, 5700.0, 4300.0, 4750.0, 5550.0, 4900.0, 4200.0, 5400.0, 5100.0, 5300.0, 4850.0, 5300.0, 4400.0, 5000.0, 4900.0, 5050.0, 4300.0, 5000.0, 4450.0, 5550.0, 4200.0, 5300.0, 4400.0, 5650.0, 4700.0, 5700.0, 5800.0, 4700.0, 5550.0, 4750.0, 5000.0, 5100.0, 5200.0, 4700.0, 5800.0, 4600.0, 6000.0, 4750.0, 5950.0, 4625.0, 5450.0, 4725.0, 5350.0, 4750.0, 5600.0, 4600.0, 5300.0, 4875.0, 5550.0, 4950.0, 5400.0, 4750.0, 5650.0, 4850.0, 5200.0, 4925.0, 4875.0, 4625.0, 5250.0, 4850.0, 5600.0, 4975.0, 5500.0, 5500.0, 4700.0, 5500.0, 4575.0, 5500.0, 5000.0, 5950.0, 4650.0, 5500.0, 4375.0, 5850.0, 6000.0, 4925.0, 4850.0, 5750.0, 5200.0, 5400.0 \n", "3 46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 50.8, 50.2, 17.9, 19.5, 19.2, 18.7, 19.8, 17.8, 18.2, 18.2, 18.9, 19.9, 17.8, 20.3, 17.3, 18.1, 17.1, 19.6, 20.0, 17.8, 18.6, 18.2, 17.3, 17.5, 16.6, 19.4, 17.9, 19.0, 18.4, 19.0, 17.8, 20.0, 16.6, 20.8, 16.7, 18.8, 18.6, 16.8, 18.3, 20.7, 16.6, 19.9, 19.5, 17.5, 19.1, 17.0, 17.9, 18.5, 17.9, 19.6, 18.7, 17.3, 16.4, 19.0, 17.3, 19.7, 17.3, 18.8, 16.6, 19.9, 18.8, 19.4, 19.5, 16.5, 17.0, 19.8, 18.1, 18.2, 19.0, 18.7, 3500.0, 3900.0, 3650.0, 3525.0, 3725.0, 3950.0, 3250.0, 3750.0, 4150.0, 3700.0, 3800.0, 3775.0, 3700.0, 4050.0, 3575.0, 4050.0, 3300.0, 3700.0, 3450.0, 4400.0, 3600.0, 3400.0, 2900.0, 3800.0, 3300.0, 4150.0, 3400.0, 3800.0, 3700.0, 4550.0, 3200.0, 4300.0, 3350.0, 4100.0, 3600.0, 3900.0, 3850.0, 4800.0, 2700.0, 4500.0, 3950.0, 3650.0, 3550.0, 3500.0, 3675.0, 4450.0, 3400.0, 4300.0, 3250.0, 3675.0, 3325.0, 3950.0, 3600.0, 4050.0, 3350.0, 3450.0, 3250.0, 4050.0, 3800.0, 3525.0, 3950.0, 3650.0, 3650.0, 4000.0, 3400.0, 3775.0, 4100.0, 3775.0 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 除了species列之外的其他列组合成list_columns\n", "df %>% \n", " nest(data = !species)" ] }, { "cell_type": "code", "execution_count": 163, "id": "8e3c4fa0", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 3 × 3
speciesdata1data2
<fct><list><list>
Adelie 39.1, 39.5, 40.3, 36.7, 39.3, 38.9, 39.2, 41.1, 38.6, 34.6, 36.6, 38.7, 42.5, 34.4, 46.0, 37.8, 37.7, 35.9, 38.2, 38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9, 36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36.0, 44.1, 37.0, 39.6, 41.1, 36.0, 42.3, 39.6, 40.1, 35.0, 42.0, 34.5, 41.4, 39.0, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4, 41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5, 42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3, 41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34.0, 39.6, 36.2, 40.8, 38.1, 40.3, 33.1, 43.2, 35.0, 41.0, 37.7, 37.8, 37.9, 39.7, 38.6, 38.2, 38.1, 43.2, 38.1, 45.6, 39.7, 42.2, 39.6, 42.7, 38.6, 37.3, 35.7, 41.1, 36.2, 37.7, 40.2, 41.4, 35.2, 40.6, 38.8, 41.5, 39.0, 44.1, 38.5, 43.1, 36.8, 37.5, 38.1, 41.1, 35.6, 40.2, 37.0, 39.7, 40.2, 40.6, 32.1, 40.7, 37.3, 39.0, 39.2, 36.6, 36.0, 37.8, 36.0, 41.5, 18.7, 17.4, 18.0, 19.3, 20.6, 17.8, 19.6, 17.6, 21.2, 21.1, 17.8, 19.0, 20.7, 18.4, 21.5, 18.3, 18.7, 19.2, 18.1, 17.2, 18.9, 18.6, 17.9, 18.6, 18.9, 16.7, 18.1, 17.8, 18.9, 17.0, 21.1, 20.0, 18.5, 19.3, 19.1, 18.0, 18.4, 18.5, 19.7, 16.9, 18.8, 19.0, 17.9, 21.2, 17.7, 18.9, 17.9, 19.5, 18.1, 18.6, 17.5, 18.8, 16.6, 19.1, 16.9, 21.1, 17.0, 18.2, 17.1, 18.0, 16.2, 19.1, 16.6, 19.4, 19.0, 18.4, 17.2, 18.9, 17.5, 18.5, 16.8, 19.4, 16.1, 19.1, 17.2, 17.6, 18.8, 19.4, 17.8, 20.3, 19.5, 18.6, 19.2, 18.8, 18.0, 18.1, 17.1, 18.1, 17.3, 18.9, 18.6, 18.5, 16.1, 18.5, 17.9, 20.0, 16.0, 20.0, 18.6, 18.9, 17.2, 20.0, 17.0, 19.0, 16.5, 20.3, 17.7, 19.5, 20.7, 18.3, 17.0, 20.5, 17.0, 18.6, 17.2, 19.8, 17.0, 18.5, 15.9, 19.0, 17.6, 18.3, 17.1, 18.0, 17.9, 19.2, 18.5, 18.5, 17.6, 17.5, 17.5, 20.1, 16.5, 17.9, 17.1, 17.2, 15.5, 17.0, 16.8, 18.7, 18.6, 18.4, 17.8, 18.1, 17.1, 18.53750, 3800, 3250, 3450, 3650, 3625, 4675, 3200, 3800, 4400, 3700, 3450, 4500, 3325, 4200, 3400, 3600, 3800, 3950, 3800, 3800, 3550, 3200, 3150, 3950, 3250, 3900, 3300, 3900, 3325, 4150, 3950, 3550, 3300, 4650, 3150, 3900, 3100, 4400, 3000, 4600, 3425, 3450, 4150, 3500, 4300, 3450, 4050, 2900, 3700, 3550, 3800, 2850, 3750, 3150, 4400, 3600, 4050, 2850, 3950, 3350, 4100, 3050, 4450, 3600, 3900, 3550, 4150, 3700, 4250, 3700, 3900, 3550, 4000, 3200, 4700, 3800, 4200, 3350, 3550, 3800, 3500, 3950, 3600, 3550, 4300, 3400, 4450, 3300, 4300, 3700, 4350, 2900, 4100, 3725, 4725, 3075, 4250, 2925, 3550, 3750, 3900, 3175, 4775, 3825, 4600, 3200, 4275, 3900, 4075, 2900, 3775, 3350, 3325, 3150, 3500, 3450, 3875, 3050, 4000, 3275, 4300, 3050, 4000, 3325, 3500, 3500, 4475, 3425, 3900, 3175, 3975, 3400, 4250, 3400, 3475, 3050, 3725, 3000, 3650, 4250, 3475, 3450, 3750, 3700, 4000
Gentoo 46.1, 50.0, 48.7, 50.0, 47.6, 46.5, 45.4, 46.7, 43.3, 46.8, 40.9, 49.0, 45.5, 48.4, 45.8, 49.3, 42.0, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1, 47.8, 48.2, 50.0, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4, 42.6, 44.4, 44.0, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6, 45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.0, 43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 44.9, 50.8, 43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9, 13.2, 16.3, 14.1, 15.2, 14.5, 13.5, 14.6, 15.3, 13.4, 15.4, 13.7, 16.1, 13.7, 14.6, 14.6, 15.7, 13.5, 15.2, 14.5, 15.1, 14.3, 14.5, 14.5, 15.8, 13.1, 15.1, 15.0, 14.3, 15.3, 15.3, 14.2, 14.5, 17.0, 14.8, 16.3, 13.7, 17.3, 13.6, 15.7, 13.7, 16.0, 13.7, 15.0, 15.9, 13.9, 13.9, 15.9, 13.3, 15.8, 14.2, 14.1, 14.4, 15.0, 14.4, 15.4, 13.9, 15.0, 14.5, 15.3, 13.8, 14.9, 13.9, 15.7, 14.2, 16.8, 16.2, 14.2, 15.0, 15.0, 15.6, 15.6, 14.8, 15.0, 16.0, 14.2, 16.3, 13.8, 16.4, 14.5, 15.6, 14.6, 15.9, 13.8, 17.3, 14.4, 14.2, 14.0, 17.0, 15.0, 17.1, 14.5, 16.1, 14.7, 15.7, 15.8, 14.6, 14.4, 16.5, 15.0, 17.0, 15.5, 15.0, 16.1, 14.7, 15.8, 14.0, 15.1, 15.2, 15.9, 15.2, 16.3, 14.1, 16.0, 16.2, 13.7, 14.3, 15.7, 14.8, 16.14500, 5700, 4450, 5700, 5400, 4550, 4800, 5200, 4400, 5150, 4650, 5550, 4650, 5850, 4200, 5850, 4150, 6300, 4800, 5350, 5700, 5000, 4400, 5050, 5000, 5100, 5650, 4600, 5550, 5250, 4700, 5050, 6050, 5150, 5400, 4950, 5250, 4350, 5350, 3950, 5700, 4300, 4750, 5550, 4900, 4200, 5400, 5100, 5300, 4850, 5300, 4400, 5000, 4900, 5050, 4300, 5000, 4450, 5550, 4200, 5300, 4400, 5650, 4700, 5700, 5800, 4700, 5550, 4750, 5000, 5100, 5200, 4700, 5800, 4600, 6000, 4750, 5950, 4625, 5450, 4725, 5350, 4750, 5600, 4600, 5300, 4875, 5550, 4950, 5400, 4750, 5650, 4850, 5200, 4925, 4875, 4625, 5250, 4850, 5600, 4975, 5500, 5500, 4700, 5500, 4575, 5500, 5000, 5950, 4650, 5500, 4375, 5850, 6000, 4925, 4850, 5750, 5200, 5400
Chinstrap46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 50.8, 50.2, 17.9, 19.5, 19.2, 18.7, 19.8, 17.8, 18.2, 18.2, 18.9, 19.9, 17.8, 20.3, 17.3, 18.1, 17.1, 19.6, 20.0, 17.8, 18.6, 18.2, 17.3, 17.5, 16.6, 19.4, 17.9, 19.0, 18.4, 19.0, 17.8, 20.0, 16.6, 20.8, 16.7, 18.8, 18.6, 16.8, 18.3, 20.7, 16.6, 19.9, 19.5, 17.5, 19.1, 17.0, 17.9, 18.5, 17.9, 19.6, 18.7, 17.3, 16.4, 19.0, 17.3, 19.7, 17.3, 18.8, 16.6, 19.9, 18.8, 19.4, 19.5, 16.5, 17.0, 19.8, 18.1, 18.2, 19.0, 18.73500, 3900, 3650, 3525, 3725, 3950, 3250, 3750, 4150, 3700, 3800, 3775, 3700, 4050, 3575, 4050, 3300, 3700, 3450, 4400, 3600, 3400, 2900, 3800, 3300, 4150, 3400, 3800, 3700, 4550, 3200, 4300, 3350, 4100, 3600, 3900, 3850, 4800, 2700, 4500, 3950, 3650, 3550, 3500, 3675, 4450, 3400, 4300, 3250, 3675, 3325, 3950, 3600, 4050, 3350, 3450, 3250, 4050, 3800, 3525, 3950, 3650, 3650, 4000, 3400, 3775, 4100, 3775
\n" ], "text/latex": [ "A tibble: 3 × 3\n", "\\begin{tabular}{lll}\n", " species & data1 & data2\\\\\n", " & & \\\\\n", "\\hline\n", "\t Adelie & 39.1, 39.5, 40.3, 36.7, 39.3, 38.9, 39.2, 41.1, 38.6, 34.6, 36.6, 38.7, 42.5, 34.4, 46.0, 37.8, 37.7, 35.9, 38.2, 38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9, 36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36.0, 44.1, 37.0, 39.6, 41.1, 36.0, 42.3, 39.6, 40.1, 35.0, 42.0, 34.5, 41.4, 39.0, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4, 41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5, 42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3, 41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34.0, 39.6, 36.2, 40.8, 38.1, 40.3, 33.1, 43.2, 35.0, 41.0, 37.7, 37.8, 37.9, 39.7, 38.6, 38.2, 38.1, 43.2, 38.1, 45.6, 39.7, 42.2, 39.6, 42.7, 38.6, 37.3, 35.7, 41.1, 36.2, 37.7, 40.2, 41.4, 35.2, 40.6, 38.8, 41.5, 39.0, 44.1, 38.5, 43.1, 36.8, 37.5, 38.1, 41.1, 35.6, 40.2, 37.0, 39.7, 40.2, 40.6, 32.1, 40.7, 37.3, 39.0, 39.2, 36.6, 36.0, 37.8, 36.0, 41.5, 18.7, 17.4, 18.0, 19.3, 20.6, 17.8, 19.6, 17.6, 21.2, 21.1, 17.8, 19.0, 20.7, 18.4, 21.5, 18.3, 18.7, 19.2, 18.1, 17.2, 18.9, 18.6, 17.9, 18.6, 18.9, 16.7, 18.1, 17.8, 18.9, 17.0, 21.1, 20.0, 18.5, 19.3, 19.1, 18.0, 18.4, 18.5, 19.7, 16.9, 18.8, 19.0, 17.9, 21.2, 17.7, 18.9, 17.9, 19.5, 18.1, 18.6, 17.5, 18.8, 16.6, 19.1, 16.9, 21.1, 17.0, 18.2, 17.1, 18.0, 16.2, 19.1, 16.6, 19.4, 19.0, 18.4, 17.2, 18.9, 17.5, 18.5, 16.8, 19.4, 16.1, 19.1, 17.2, 17.6, 18.8, 19.4, 17.8, 20.3, 19.5, 18.6, 19.2, 18.8, 18.0, 18.1, 17.1, 18.1, 17.3, 18.9, 18.6, 18.5, 16.1, 18.5, 17.9, 20.0, 16.0, 20.0, 18.6, 18.9, 17.2, 20.0, 17.0, 19.0, 16.5, 20.3, 17.7, 19.5, 20.7, 18.3, 17.0, 20.5, 17.0, 18.6, 17.2, 19.8, 17.0, 18.5, 15.9, 19.0, 17.6, 18.3, 17.1, 18.0, 17.9, 19.2, 18.5, 18.5, 17.6, 17.5, 17.5, 20.1, 16.5, 17.9, 17.1, 17.2, 15.5, 17.0, 16.8, 18.7, 18.6, 18.4, 17.8, 18.1, 17.1, 18.5 & 3750, 3800, 3250, 3450, 3650, 3625, 4675, 3200, 3800, 4400, 3700, 3450, 4500, 3325, 4200, 3400, 3600, 3800, 3950, 3800, 3800, 3550, 3200, 3150, 3950, 3250, 3900, 3300, 3900, 3325, 4150, 3950, 3550, 3300, 4650, 3150, 3900, 3100, 4400, 3000, 4600, 3425, 3450, 4150, 3500, 4300, 3450, 4050, 2900, 3700, 3550, 3800, 2850, 3750, 3150, 4400, 3600, 4050, 2850, 3950, 3350, 4100, 3050, 4450, 3600, 3900, 3550, 4150, 3700, 4250, 3700, 3900, 3550, 4000, 3200, 4700, 3800, 4200, 3350, 3550, 3800, 3500, 3950, 3600, 3550, 4300, 3400, 4450, 3300, 4300, 3700, 4350, 2900, 4100, 3725, 4725, 3075, 4250, 2925, 3550, 3750, 3900, 3175, 4775, 3825, 4600, 3200, 4275, 3900, 4075, 2900, 3775, 3350, 3325, 3150, 3500, 3450, 3875, 3050, 4000, 3275, 4300, 3050, 4000, 3325, 3500, 3500, 4475, 3425, 3900, 3175, 3975, 3400, 4250, 3400, 3475, 3050, 3725, 3000, 3650, 4250, 3475, 3450, 3750, 3700, 4000\\\\\n", "\t Gentoo & 46.1, 50.0, 48.7, 50.0, 47.6, 46.5, 45.4, 46.7, 43.3, 46.8, 40.9, 49.0, 45.5, 48.4, 45.8, 49.3, 42.0, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1, 47.8, 48.2, 50.0, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4, 42.6, 44.4, 44.0, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6, 45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.0, 43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 44.9, 50.8, 43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9, 13.2, 16.3, 14.1, 15.2, 14.5, 13.5, 14.6, 15.3, 13.4, 15.4, 13.7, 16.1, 13.7, 14.6, 14.6, 15.7, 13.5, 15.2, 14.5, 15.1, 14.3, 14.5, 14.5, 15.8, 13.1, 15.1, 15.0, 14.3, 15.3, 15.3, 14.2, 14.5, 17.0, 14.8, 16.3, 13.7, 17.3, 13.6, 15.7, 13.7, 16.0, 13.7, 15.0, 15.9, 13.9, 13.9, 15.9, 13.3, 15.8, 14.2, 14.1, 14.4, 15.0, 14.4, 15.4, 13.9, 15.0, 14.5, 15.3, 13.8, 14.9, 13.9, 15.7, 14.2, 16.8, 16.2, 14.2, 15.0, 15.0, 15.6, 15.6, 14.8, 15.0, 16.0, 14.2, 16.3, 13.8, 16.4, 14.5, 15.6, 14.6, 15.9, 13.8, 17.3, 14.4, 14.2, 14.0, 17.0, 15.0, 17.1, 14.5, 16.1, 14.7, 15.7, 15.8, 14.6, 14.4, 16.5, 15.0, 17.0, 15.5, 15.0, 16.1, 14.7, 15.8, 14.0, 15.1, 15.2, 15.9, 15.2, 16.3, 14.1, 16.0, 16.2, 13.7, 14.3, 15.7, 14.8, 16.1 & 4500, 5700, 4450, 5700, 5400, 4550, 4800, 5200, 4400, 5150, 4650, 5550, 4650, 5850, 4200, 5850, 4150, 6300, 4800, 5350, 5700, 5000, 4400, 5050, 5000, 5100, 5650, 4600, 5550, 5250, 4700, 5050, 6050, 5150, 5400, 4950, 5250, 4350, 5350, 3950, 5700, 4300, 4750, 5550, 4900, 4200, 5400, 5100, 5300, 4850, 5300, 4400, 5000, 4900, 5050, 4300, 5000, 4450, 5550, 4200, 5300, 4400, 5650, 4700, 5700, 5800, 4700, 5550, 4750, 5000, 5100, 5200, 4700, 5800, 4600, 6000, 4750, 5950, 4625, 5450, 4725, 5350, 4750, 5600, 4600, 5300, 4875, 5550, 4950, 5400, 4750, 5650, 4850, 5200, 4925, 4875, 4625, 5250, 4850, 5600, 4975, 5500, 5500, 4700, 5500, 4575, 5500, 5000, 5950, 4650, 5500, 4375, 5850, 6000, 4925, 4850, 5750, 5200, 5400\\\\\n", "\t Chinstrap & 46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 50.8, 50.2, 17.9, 19.5, 19.2, 18.7, 19.8, 17.8, 18.2, 18.2, 18.9, 19.9, 17.8, 20.3, 17.3, 18.1, 17.1, 19.6, 20.0, 17.8, 18.6, 18.2, 17.3, 17.5, 16.6, 19.4, 17.9, 19.0, 18.4, 19.0, 17.8, 20.0, 16.6, 20.8, 16.7, 18.8, 18.6, 16.8, 18.3, 20.7, 16.6, 19.9, 19.5, 17.5, 19.1, 17.0, 17.9, 18.5, 17.9, 19.6, 18.7, 17.3, 16.4, 19.0, 17.3, 19.7, 17.3, 18.8, 16.6, 19.9, 18.8, 19.4, 19.5, 16.5, 17.0, 19.8, 18.1, 18.2, 19.0, 18.7 & 3500, 3900, 3650, 3525, 3725, 3950, 3250, 3750, 4150, 3700, 3800, 3775, 3700, 4050, 3575, 4050, 3300, 3700, 3450, 4400, 3600, 3400, 2900, 3800, 3300, 4150, 3400, 3800, 3700, 4550, 3200, 4300, 3350, 4100, 3600, 3900, 3850, 4800, 2700, 4500, 3950, 3650, 3550, 3500, 3675, 4450, 3400, 4300, 3250, 3675, 3325, 3950, 3600, 4050, 3350, 3450, 3250, 4050, 3800, 3525, 3950, 3650, 3650, 4000, 3400, 3775, 4100, 3775\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 3 × 3\n", "\n", "| species <fct> | data1 <list> | data2 <list> |\n", "|---|---|---|\n", "| Adelie | 39.1, 39.5, 40.3, 36.7, 39.3, 38.9, 39.2, 41.1, 38.6, 34.6, 36.6, 38.7, 42.5, 34.4, 46.0, 37.8, 37.7, 35.9, 38.2, 38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9, 36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36.0, 44.1, 37.0, 39.6, 41.1, 36.0, 42.3, 39.6, 40.1, 35.0, 42.0, 34.5, 41.4, 39.0, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4, 41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5, 42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3, 41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34.0, 39.6, 36.2, 40.8, 38.1, 40.3, 33.1, 43.2, 35.0, 41.0, 37.7, 37.8, 37.9, 39.7, 38.6, 38.2, 38.1, 43.2, 38.1, 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50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 50.8, 50.2, 17.9, 19.5, 19.2, 18.7, 19.8, 17.8, 18.2, 18.2, 18.9, 19.9, 17.8, 20.3, 17.3, 18.1, 17.1, 19.6, 20.0, 17.8, 18.6, 18.2, 17.3, 17.5, 16.6, 19.4, 17.9, 19.0, 18.4, 19.0, 17.8, 20.0, 16.6, 20.8, 16.7, 18.8, 18.6, 16.8, 18.3, 20.7, 16.6, 19.9, 19.5, 17.5, 19.1, 17.0, 17.9, 18.5, 17.9, 19.6, 18.7, 17.3, 16.4, 19.0, 17.3, 19.7, 17.3, 18.8, 16.6, 19.9, 18.8, 19.4, 19.5, 16.5, 17.0, 19.8, 18.1, 18.2, 19.0, 18.7 \n", " data2 \n", "1 3750, 3800, 3250, 3450, 3650, 3625, 4675, 3200, 3800, 4400, 3700, 3450, 4500, 3325, 4200, 3400, 3600, 3800, 3950, 3800, 3800, 3550, 3200, 3150, 3950, 3250, 3900, 3300, 3900, 3325, 4150, 3950, 3550, 3300, 4650, 3150, 3900, 3100, 4400, 3000, 4600, 3425, 3450, 4150, 3500, 4300, 3450, 4050, 2900, 3700, 3550, 3800, 2850, 3750, 3150, 4400, 3600, 4050, 2850, 3950, 3350, 4100, 3050, 4450, 3600, 3900, 3550, 4150, 3700, 4250, 3700, 3900, 3550, 4000, 3200, 4700, 3800, 4200, 3350, 3550, 3800, 3500, 3950, 3600, 3550, 4300, 3400, 4450, 3300, 4300, 3700, 4350, 2900, 4100, 3725, 4725, 3075, 4250, 2925, 3550, 3750, 3900, 3175, 4775, 3825, 4600, 3200, 4275, 3900, 4075, 2900, 3775, 3350, 3325, 3150, 3500, 3450, 3875, 3050, 4000, 3275, 4300, 3050, 4000, 3325, 3500, 3500, 4475, 3425, 3900, 3175, 3975, 3400, 4250, 3400, 3475, 3050, 3725, 3000, 3650, 4250, 3475, 3450, 3750, 3700, 4000\n", "2 4500, 5700, 4450, 5700, 5400, 4550, 4800, 5200, 4400, 5150, 4650, 5550, 4650, 5850, 4200, 5850, 4150, 6300, 4800, 5350, 5700, 5000, 4400, 5050, 5000, 5100, 5650, 4600, 5550, 5250, 4700, 5050, 6050, 5150, 5400, 4950, 5250, 4350, 5350, 3950, 5700, 4300, 4750, 5550, 4900, 4200, 5400, 5100, 5300, 4850, 5300, 4400, 5000, 4900, 5050, 4300, 5000, 4450, 5550, 4200, 5300, 4400, 5650, 4700, 5700, 5800, 4700, 5550, 4750, 5000, 5100, 5200, 4700, 5800, 4600, 6000, 4750, 5950, 4625, 5450, 4725, 5350, 4750, 5600, 4600, 5300, 4875, 5550, 4950, 5400, 4750, 5650, 4850, 5200, 4925, 4875, 4625, 5250, 4850, 5600, 4975, 5500, 5500, 4700, 5500, 4575, 5500, 5000, 5950, 4650, 5500, 4375, 5850, 6000, 4925, 4850, 5750, 5200, 5400 \n", "3 3500, 3900, 3650, 3525, 3725, 3950, 3250, 3750, 4150, 3700, 3800, 3775, 3700, 4050, 3575, 4050, 3300, 3700, 3450, 4400, 3600, 3400, 2900, 3800, 3300, 4150, 3400, 3800, 3700, 4550, 3200, 4300, 3350, 4100, 3600, 3900, 3850, 4800, 2700, 4500, 3950, 3650, 3550, 3500, 3675, 4450, 3400, 4300, 3250, 3675, 3325, 3950, 3600, 4050, 3350, 3450, 3250, 4050, 3800, 3525, 3950, 3650, 3650, 4000, 3400, 3775, 4100, 3775 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 同时创建多个列表列\n", "df %>% \n", " nest(data1 = c(bill_length_mm, bill_depth_mm), data2 = body_mass_g)" ] }, { "cell_type": "markdown", "id": "c4810e14", "metadata": {}, "source": [ "### 1 creating\n", " ### `tidyr::summarise(list())`创建\n", " `group_by()` 和 `summarise()`组合可以将向量分组后分别压缩成单个值，事实上，`summarise()`还可以创建列表列。" ] }, { "cell_type": "code", "execution_count": 164, "id": "bfa10844", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 3 × 2
speciesdata
<fct><list>
Adelie 39.1, 39.5, 40.3, 36.7, 39.3, 38.9, 39.2, 41.1, 38.6, 34.6, 36.6, 38.7, 42.5, 34.4, 46.0, 37.8, 37.7, 35.9, 38.2, 38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9, 36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36.0, 44.1, 37.0, 39.6, 41.1, 36.0, 42.3, 39.6, 40.1, 35.0, 42.0, 34.5, 41.4, 39.0, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4, 41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5, 42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3, 41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34.0, 39.6, 36.2, 40.8, 38.1, 40.3, 33.1, 43.2, 35.0, 41.0, 37.7, 37.8, 37.9, 39.7, 38.6, 38.2, 38.1, 43.2, 38.1, 45.6, 39.7, 42.2, 39.6, 42.7, 38.6, 37.3, 35.7, 41.1, 36.2, 37.7, 40.2, 41.4, 35.2, 40.6, 38.8, 41.5, 39.0, 44.1, 38.5, 43.1, 36.8, 37.5, 38.1, 41.1, 35.6, 40.2, 37.0, 39.7, 40.2, 40.6, 32.1, 40.7, 37.3, 39.0, 39.2, 36.6, 36.0, 37.8, 36.0, 41.5
Chinstrap46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 50.8, 50.2
Gentoo 46.1, 50.0, 48.7, 50.0, 47.6, 46.5, 45.4, 46.7, 43.3, 46.8, 40.9, 49.0, 45.5, 48.4, 45.8, 49.3, 42.0, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1, 47.8, 48.2, 50.0, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4, 42.6, 44.4, 44.0, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6, 45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.0, 43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 44.9, 50.8, 43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9
\n" ], "text/latex": [ "A tibble: 3 × 2\n", "\\begin{tabular}{ll}\n", " species & data\\\\\n", " & \\\\\n", "\\hline\n", "\t Adelie & 39.1, 39.5, 40.3, 36.7, 39.3, 38.9, 39.2, 41.1, 38.6, 34.6, 36.6, 38.7, 42.5, 34.4, 46.0, 37.8, 37.7, 35.9, 38.2, 38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9, 36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36.0, 44.1, 37.0, 39.6, 41.1, 36.0, 42.3, 39.6, 40.1, 35.0, 42.0, 34.5, 41.4, 39.0, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4, 41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5, 42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3, 41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34.0, 39.6, 36.2, 40.8, 38.1, 40.3, 33.1, 43.2, 35.0, 41.0, 37.7, 37.8, 37.9, 39.7, 38.6, 38.2, 38.1, 43.2, 38.1, 45.6, 39.7, 42.2, 39.6, 42.7, 38.6, 37.3, 35.7, 41.1, 36.2, 37.7, 40.2, 41.4, 35.2, 40.6, 38.8, 41.5, 39.0, 44.1, 38.5, 43.1, 36.8, 37.5, 38.1, 41.1, 35.6, 40.2, 37.0, 39.7, 40.2, 40.6, 32.1, 40.7, 37.3, 39.0, 39.2, 36.6, 36.0, 37.8, 36.0, 41.5\\\\\n", "\t Chinstrap & 46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 50.8, 50.2\\\\\n", "\t Gentoo & 46.1, 50.0, 48.7, 50.0, 47.6, 46.5, 45.4, 46.7, 43.3, 46.8, 40.9, 49.0, 45.5, 48.4, 45.8, 49.3, 42.0, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1, 47.8, 48.2, 50.0, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4, 42.6, 44.4, 44.0, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6, 45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.0, 43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 44.9, 50.8, 43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 3 × 2\n", "\n", "| species <fct> | data <list> |\n", "|---|---|\n", "| Adelie | 39.1, 39.5, 40.3, 36.7, 39.3, 38.9, 39.2, 41.1, 38.6, 34.6, 36.6, 38.7, 42.5, 34.4, 46.0, 37.8, 37.7, 35.9, 38.2, 38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9, 36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36.0, 44.1, 37.0, 39.6, 41.1, 36.0, 42.3, 39.6, 40.1, 35.0, 42.0, 34.5, 41.4, 39.0, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4, 41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5, 42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3, 41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34.0, 39.6, 36.2, 40.8, 38.1, 40.3, 33.1, 43.2, 35.0, 41.0, 37.7, 37.8, 37.9, 39.7, 38.6, 38.2, 38.1, 43.2, 38.1, 45.6, 39.7, 42.2, 39.6, 42.7, 38.6, 37.3, 35.7, 41.1, 36.2, 37.7, 40.2, 41.4, 35.2, 40.6, 38.8, 41.5, 39.0, 44.1, 38.5, 43.1, 36.8, 37.5, 38.1, 41.1, 35.6, 40.2, 37.0, 39.7, 40.2, 40.6, 32.1, 40.7, 37.3, 39.0, 39.2, 36.6, 36.0, 37.8, 36.0, 41.5 |\n", "| Chinstrap | 46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 50.8, 50.2 |\n", "| Gentoo | 46.1, 50.0, 48.7, 50.0, 47.6, 46.5, 45.4, 46.7, 43.3, 46.8, 40.9, 49.0, 45.5, 48.4, 45.8, 49.3, 42.0, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1, 47.8, 48.2, 50.0, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4, 42.6, 44.4, 44.0, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6, 45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.0, 43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 44.9, 50.8, 43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9 |\n", "\n" ], "text/plain": [ " species \n", "1 Adelie \n", "2 Chinstrap\n", "3 Gentoo \n", " data \n", "1 39.1, 39.5, 40.3, 36.7, 39.3, 38.9, 39.2, 41.1, 38.6, 34.6, 36.6, 38.7, 42.5, 34.4, 46.0, 37.8, 37.7, 35.9, 38.2, 38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9, 36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36.0, 44.1, 37.0, 39.6, 41.1, 36.0, 42.3, 39.6, 40.1, 35.0, 42.0, 34.5, 41.4, 39.0, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4, 41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5, 42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3, 41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34.0, 39.6, 36.2, 40.8, 38.1, 40.3, 33.1, 43.2, 35.0, 41.0, 37.7, 37.8, 37.9, 39.7, 38.6, 38.2, 38.1, 43.2, 38.1, 45.6, 39.7, 42.2, 39.6, 42.7, 38.6, 37.3, 35.7, 41.1, 36.2, 37.7, 40.2, 41.4, 35.2, 40.6, 38.8, 41.5, 39.0, 44.1, 38.5, 43.1, 36.8, 37.5, 38.1, 41.1, 35.6, 40.2, 37.0, 39.7, 40.2, 40.6, 32.1, 40.7, 37.3, 39.0, 39.2, 36.6, 36.0, 37.8, 36.0, 41.5\n", "2 46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 50.8, 50.2 \n", "3 46.1, 50.0, 48.7, 50.0, 47.6, 46.5, 45.4, 46.7, 43.3, 46.8, 40.9, 49.0, 45.5, 48.4, 45.8, 49.3, 42.0, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1, 47.8, 48.2, 50.0, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4, 42.6, 44.4, 44.0, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6, 45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.0, 43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 44.9, 50.8, 43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_collpase <- df %>% \n", " group_by(species) %>% \n", " summarise(data = list(bill_length_mm))\n", "df_collpase" ] }, { "cell_type": "markdown", "id": "ddb7a9d0", "metadata": {}, "source": [ "`data`就是构建的列表列，它的每个元素都是一个向量，对应一个`species`。这种方法和`nest()`方法很相似，不同在于，`summarise()` + `list()` 构建的列表列其元素是原子型向量，而`nest()`构建的是`tibble`." ] }, { "cell_type": "code", "execution_count": 165, "id": "dac9e921", "metadata": {}, "outputs": [ { "data": { "text/html": [ "'double'" ], "text/latex": [ "'double'" ], "text/markdown": [ "'double'" ], "text/plain": [ "[1] \"double\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_collpase$data[[1]] %>% typeof()" ] }, { "cell_type": "markdown", "id": "f0c3c23c", "metadata": {}, "source": [ "`summarise()` + `list()`的方法还可以在创建列表列之前，对数据简单处理" ] }, { "cell_type": "code", "execution_count": 167, "id": "2b0c1fdf", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 3 × 2
speciesdata
<fct><list>
Adelie 32.1, 33.1, 33.5, 34.0, 34.4, 34.5, 34.6, 34.6, 35.0, 35.0, 35.1, 35.2, 35.3, 35.5, 35.5, 35.6, 35.7, 35.7, 35.7, 35.9, 35.9, 36.0, 36.0, 36.0, 36.0, 36.2, 36.2, 36.2, 36.3, 36.4, 36.4, 36.5, 36.5, 36.6, 36.6, 36.7, 36.7, 36.8, 36.9, 37.0, 37.0, 37.2, 37.2, 37.3, 37.3, 37.3, 37.5, 37.6, 37.6, 37.6, 37.7, 37.7, 37.7, 37.8, 37.8, 37.8, 37.9, 37.9, 38.1, 38.1, 38.1, 38.1, 38.2, 38.2, 38.3, 38.5, 38.6, 38.6, 38.6, 38.7, 38.8, 38.8, 38.8, 38.9, 38.9, 39.0, 39.0, 39.0, 39.1, 39.2, 39.2, 39.2, 39.3, 39.5, 39.5, 39.5, 39.6, 39.6, 39.6, 39.6, 39.6, 39.7, 39.7, 39.7, 39.7, 39.8, 40.1, 40.2, 40.2, 40.2, 40.3, 40.3, 40.5, 40.5, 40.6, 40.6, 40.6, 40.6, 40.7, 40.8, 40.8, 40.9, 40.9, 41.0, 41.1, 41.1, 41.1, 41.1, 41.1, 41.1, 41.1, 41.3, 41.3, 41.4, 41.4, 41.5, 41.5, 41.6, 41.8, 42.0, 42.1, 42.2, 42.2, 42.3, 42.5, 42.7, 42.8, 42.9, 43.1, 43.2, 43.2, 44.1, 44.1, 45.6, 45.8, 46.0
Chinstrap40.9, 42.4, 42.5, 42.5, 43.2, 43.5, 45.2, 45.2, 45.4, 45.5, 45.6, 45.7, 45.7, 45.9, 46.0, 46.1, 46.2, 46.4, 46.4, 46.5, 46.6, 46.7, 46.8, 46.9, 47.0, 47.5, 47.6, 48.1, 48.5, 49.0, 49.0, 49.2, 49.3, 49.5, 49.6, 49.7, 49.8, 50.0, 50.1, 50.2, 50.2, 50.3, 50.5, 50.5, 50.6, 50.7, 50.8, 50.8, 50.9, 50.9, 51.0, 51.3, 51.3, 51.3, 51.4, 51.5, 51.7, 51.9, 52.0, 52.0, 52.0, 52.2, 52.7, 52.8, 53.5, 54.2, 55.8, 58.0
Gentoo 40.9, 41.7, 42.0, 42.6, 42.7, 42.8, 42.9, 43.2, 43.3, 43.3, 43.4, 43.5, 43.5, 43.6, 43.8, 44.0, 44.4, 44.5, 44.9, 44.9, 45.0, 45.1, 45.1, 45.1, 45.2, 45.2, 45.2, 45.2, 45.3, 45.3, 45.4, 45.5, 45.5, 45.5, 45.5, 45.7, 45.8, 45.8, 46.1, 46.1, 46.2, 46.2, 46.2, 46.3, 46.4, 46.4, 46.5, 46.5, 46.5, 46.5, 46.6, 46.7, 46.8, 46.8, 46.8, 46.9, 47.2, 47.2, 47.3, 47.4, 47.5, 47.5, 47.5, 47.6, 47.7, 47.8, 48.1, 48.2, 48.2, 48.4, 48.4, 48.4, 48.5, 48.5, 48.6, 48.7, 48.7, 48.7, 48.8, 49.0, 49.1, 49.1, 49.1, 49.2, 49.3, 49.4, 49.5, 49.5, 49.6, 49.6, 49.8, 49.8, 49.9, 50.0, 50.0, 50.0, 50.0, 50.1, 50.2, 50.4, 50.4, 50.5, 50.5, 50.5, 50.7, 50.8, 50.8, 51.1, 51.1, 51.3, 51.5, 52.1, 52.2, 52.5, 53.4, 54.3, 55.1, 55.9, 59.6
\n" ], "text/latex": [ "A tibble: 3 × 2\n", "\\begin{tabular}{ll}\n", " species & data\\\\\n", " & \\\\\n", "\\hline\n", "\t Adelie & 32.1, 33.1, 33.5, 34.0, 34.4, 34.5, 34.6, 34.6, 35.0, 35.0, 35.1, 35.2, 35.3, 35.5, 35.5, 35.6, 35.7, 35.7, 35.7, 35.9, 35.9, 36.0, 36.0, 36.0, 36.0, 36.2, 36.2, 36.2, 36.3, 36.4, 36.4, 36.5, 36.5, 36.6, 36.6, 36.7, 36.7, 36.8, 36.9, 37.0, 37.0, 37.2, 37.2, 37.3, 37.3, 37.3, 37.5, 37.6, 37.6, 37.6, 37.7, 37.7, 37.7, 37.8, 37.8, 37.8, 37.9, 37.9, 38.1, 38.1, 38.1, 38.1, 38.2, 38.2, 38.3, 38.5, 38.6, 38.6, 38.6, 38.7, 38.8, 38.8, 38.8, 38.9, 38.9, 39.0, 39.0, 39.0, 39.1, 39.2, 39.2, 39.2, 39.3, 39.5, 39.5, 39.5, 39.6, 39.6, 39.6, 39.6, 39.6, 39.7, 39.7, 39.7, 39.7, 39.8, 40.1, 40.2, 40.2, 40.2, 40.3, 40.3, 40.5, 40.5, 40.6, 40.6, 40.6, 40.6, 40.7, 40.8, 40.8, 40.9, 40.9, 41.0, 41.1, 41.1, 41.1, 41.1, 41.1, 41.1, 41.1, 41.3, 41.3, 41.4, 41.4, 41.5, 41.5, 41.6, 41.8, 42.0, 42.1, 42.2, 42.2, 42.3, 42.5, 42.7, 42.8, 42.9, 43.1, 43.2, 43.2, 44.1, 44.1, 45.6, 45.8, 46.0\\\\\n", "\t Chinstrap & 40.9, 42.4, 42.5, 42.5, 43.2, 43.5, 45.2, 45.2, 45.4, 45.5, 45.6, 45.7, 45.7, 45.9, 46.0, 46.1, 46.2, 46.4, 46.4, 46.5, 46.6, 46.7, 46.8, 46.9, 47.0, 47.5, 47.6, 48.1, 48.5, 49.0, 49.0, 49.2, 49.3, 49.5, 49.6, 49.7, 49.8, 50.0, 50.1, 50.2, 50.2, 50.3, 50.5, 50.5, 50.6, 50.7, 50.8, 50.8, 50.9, 50.9, 51.0, 51.3, 51.3, 51.3, 51.4, 51.5, 51.7, 51.9, 52.0, 52.0, 52.0, 52.2, 52.7, 52.8, 53.5, 54.2, 55.8, 58.0\\\\\n", "\t Gentoo & 40.9, 41.7, 42.0, 42.6, 42.7, 42.8, 42.9, 43.2, 43.3, 43.3, 43.4, 43.5, 43.5, 43.6, 43.8, 44.0, 44.4, 44.5, 44.9, 44.9, 45.0, 45.1, 45.1, 45.1, 45.2, 45.2, 45.2, 45.2, 45.3, 45.3, 45.4, 45.5, 45.5, 45.5, 45.5, 45.7, 45.8, 45.8, 46.1, 46.1, 46.2, 46.2, 46.2, 46.3, 46.4, 46.4, 46.5, 46.5, 46.5, 46.5, 46.6, 46.7, 46.8, 46.8, 46.8, 46.9, 47.2, 47.2, 47.3, 47.4, 47.5, 47.5, 47.5, 47.6, 47.7, 47.8, 48.1, 48.2, 48.2, 48.4, 48.4, 48.4, 48.5, 48.5, 48.6, 48.7, 48.7, 48.7, 48.8, 49.0, 49.1, 49.1, 49.1, 49.2, 49.3, 49.4, 49.5, 49.5, 49.6, 49.6, 49.8, 49.8, 49.9, 50.0, 50.0, 50.0, 50.0, 50.1, 50.2, 50.4, 50.4, 50.5, 50.5, 50.5, 50.7, 50.8, 50.8, 51.1, 51.1, 51.3, 51.5, 52.1, 52.2, 52.5, 53.4, 54.3, 55.1, 55.9, 59.6\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 3 × 2\n", "\n", "| species <fct> | data <list> |\n", "|---|---|\n", "| Adelie | 32.1, 33.1, 33.5, 34.0, 34.4, 34.5, 34.6, 34.6, 35.0, 35.0, 35.1, 35.2, 35.3, 35.5, 35.5, 35.6, 35.7, 35.7, 35.7, 35.9, 35.9, 36.0, 36.0, 36.0, 36.0, 36.2, 36.2, 36.2, 36.3, 36.4, 36.4, 36.5, 36.5, 36.6, 36.6, 36.7, 36.7, 36.8, 36.9, 37.0, 37.0, 37.2, 37.2, 37.3, 37.3, 37.3, 37.5, 37.6, 37.6, 37.6, 37.7, 37.7, 37.7, 37.8, 37.8, 37.8, 37.9, 37.9, 38.1, 38.1, 38.1, 38.1, 38.2, 38.2, 38.3, 38.5, 38.6, 38.6, 38.6, 38.7, 38.8, 38.8, 38.8, 38.9, 38.9, 39.0, 39.0, 39.0, 39.1, 39.2, 39.2, 39.2, 39.3, 39.5, 39.5, 39.5, 39.6, 39.6, 39.6, 39.6, 39.6, 39.7, 39.7, 39.7, 39.7, 39.8, 40.1, 40.2, 40.2, 40.2, 40.3, 40.3, 40.5, 40.5, 40.6, 40.6, 40.6, 40.6, 40.7, 40.8, 40.8, 40.9, 40.9, 41.0, 41.1, 41.1, 41.1, 41.1, 41.1, 41.1, 41.1, 41.3, 41.3, 41.4, 41.4, 41.5, 41.5, 41.6, 41.8, 42.0, 42.1, 42.2, 42.2, 42.3, 42.5, 42.7, 42.8, 42.9, 43.1, 43.2, 43.2, 44.1, 44.1, 45.6, 45.8, 46.0 |\n", "| Chinstrap | 40.9, 42.4, 42.5, 42.5, 43.2, 43.5, 45.2, 45.2, 45.4, 45.5, 45.6, 45.7, 45.7, 45.9, 46.0, 46.1, 46.2, 46.4, 46.4, 46.5, 46.6, 46.7, 46.8, 46.9, 47.0, 47.5, 47.6, 48.1, 48.5, 49.0, 49.0, 49.2, 49.3, 49.5, 49.6, 49.7, 49.8, 50.0, 50.1, 50.2, 50.2, 50.3, 50.5, 50.5, 50.6, 50.7, 50.8, 50.8, 50.9, 50.9, 51.0, 51.3, 51.3, 51.3, 51.4, 51.5, 51.7, 51.9, 52.0, 52.0, 52.0, 52.2, 52.7, 52.8, 53.5, 54.2, 55.8, 58.0 |\n", "| Gentoo | 40.9, 41.7, 42.0, 42.6, 42.7, 42.8, 42.9, 43.2, 43.3, 43.3, 43.4, 43.5, 43.5, 43.6, 43.8, 44.0, 44.4, 44.5, 44.9, 44.9, 45.0, 45.1, 45.1, 45.1, 45.2, 45.2, 45.2, 45.2, 45.3, 45.3, 45.4, 45.5, 45.5, 45.5, 45.5, 45.7, 45.8, 45.8, 46.1, 46.1, 46.2, 46.2, 46.2, 46.3, 46.4, 46.4, 46.5, 46.5, 46.5, 46.5, 46.6, 46.7, 46.8, 46.8, 46.8, 46.9, 47.2, 47.2, 47.3, 47.4, 47.5, 47.5, 47.5, 47.6, 47.7, 47.8, 48.1, 48.2, 48.2, 48.4, 48.4, 48.4, 48.5, 48.5, 48.6, 48.7, 48.7, 48.7, 48.8, 49.0, 49.1, 49.1, 49.1, 49.2, 49.3, 49.4, 49.5, 49.5, 49.6, 49.6, 49.8, 49.8, 49.9, 50.0, 50.0, 50.0, 50.0, 50.1, 50.2, 50.4, 50.4, 50.5, 50.5, 50.5, 50.7, 50.8, 50.8, 51.1, 51.1, 51.3, 51.5, 52.1, 52.2, 52.5, 53.4, 54.3, 55.1, 55.9, 59.6 |\n", "\n" ], "text/plain": [ " species \n", "1 Adelie \n", "2 Chinstrap\n", "3 Gentoo \n", " data \n", "1 32.1, 33.1, 33.5, 34.0, 34.4, 34.5, 34.6, 34.6, 35.0, 35.0, 35.1, 35.2, 35.3, 35.5, 35.5, 35.6, 35.7, 35.7, 35.7, 35.9, 35.9, 36.0, 36.0, 36.0, 36.0, 36.2, 36.2, 36.2, 36.3, 36.4, 36.4, 36.5, 36.5, 36.6, 36.6, 36.7, 36.7, 36.8, 36.9, 37.0, 37.0, 37.2, 37.2, 37.3, 37.3, 37.3, 37.5, 37.6, 37.6, 37.6, 37.7, 37.7, 37.7, 37.8, 37.8, 37.8, 37.9, 37.9, 38.1, 38.1, 38.1, 38.1, 38.2, 38.2, 38.3, 38.5, 38.6, 38.6, 38.6, 38.7, 38.8, 38.8, 38.8, 38.9, 38.9, 39.0, 39.0, 39.0, 39.1, 39.2, 39.2, 39.2, 39.3, 39.5, 39.5, 39.5, 39.6, 39.6, 39.6, 39.6, 39.6, 39.7, 39.7, 39.7, 39.7, 39.8, 40.1, 40.2, 40.2, 40.2, 40.3, 40.3, 40.5, 40.5, 40.6, 40.6, 40.6, 40.6, 40.7, 40.8, 40.8, 40.9, 40.9, 41.0, 41.1, 41.1, 41.1, 41.1, 41.1, 41.1, 41.1, 41.3, 41.3, 41.4, 41.4, 41.5, 41.5, 41.6, 41.8, 42.0, 42.1, 42.2, 42.2, 42.3, 42.5, 42.7, 42.8, 42.9, 43.1, 43.2, 43.2, 44.1, 44.1, 45.6, 45.8, 46.0\n", "2 40.9, 42.4, 42.5, 42.5, 43.2, 43.5, 45.2, 45.2, 45.4, 45.5, 45.6, 45.7, 45.7, 45.9, 46.0, 46.1, 46.2, 46.4, 46.4, 46.5, 46.6, 46.7, 46.8, 46.9, 47.0, 47.5, 47.6, 48.1, 48.5, 49.0, 49.0, 49.2, 49.3, 49.5, 49.6, 49.7, 49.8, 50.0, 50.1, 50.2, 50.2, 50.3, 50.5, 50.5, 50.6, 50.7, 50.8, 50.8, 50.9, 50.9, 51.0, 51.3, 51.3, 51.3, 51.4, 51.5, 51.7, 51.9, 52.0, 52.0, 52.0, 52.2, 52.7, 52.8, 53.5, 54.2, 55.8, 58.0 \n", "3 40.9, 41.7, 42.0, 42.6, 42.7, 42.8, 42.9, 43.2, 43.3, 43.3, 43.4, 43.5, 43.5, 43.6, 43.8, 44.0, 44.4, 44.5, 44.9, 44.9, 45.0, 45.1, 45.1, 45.1, 45.2, 45.2, 45.2, 45.2, 45.3, 45.3, 45.4, 45.5, 45.5, 45.5, 45.5, 45.7, 45.8, 45.8, 46.1, 46.1, 46.2, 46.2, 46.2, 46.3, 46.4, 46.4, 46.5, 46.5, 46.5, 46.5, 46.6, 46.7, 46.8, 46.8, 46.8, 46.9, 47.2, 47.2, 47.3, 47.4, 47.5, 47.5, 47.5, 47.6, 47.7, 47.8, 48.1, 48.2, 48.2, 48.4, 48.4, 48.4, 48.5, 48.5, 48.6, 48.7, 48.7, 48.7, 48.8, 49.0, 49.1, 49.1, 49.1, 49.2, 49.3, 49.4, 49.5, 49.5, 49.6, 49.6, 49.8, 49.8, 49.9, 50.0, 50.0, 50.0, 50.0, 50.1, 50.2, 50.4, 50.4, 50.5, 50.5, 50.5, 50.7, 50.8, 50.8, 51.1, 51.1, 51.3, 51.5, 52.1, 52.2, 52.5, 53.4, 54.3, 55.1, 55.9, 59.6 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 3 × 2
speciesdata
<fct><list>
Adelie 46.0, 45.8, 45.6
Chinstrap46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 48.5, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 54.2, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 49.6, 50.8, 50.2
Gentoo 46.1, 50.0, 48.7, 50.0, 47.6, 46.5, 45.4, 46.7, 46.8, 49.0, 45.5, 48.4, 45.8, 49.3, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 46.1, 47.8, 48.2, 50.0, 47.3, 45.1, 59.6, 49.1, 48.4, 48.7, 49.6, 45.3, 49.6, 50.5, 45.5, 50.5, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.5, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 50.8, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 53.4, 48.1, 50.5, 49.8, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9
\n" ], "text/latex": [ "A tibble: 3 × 2\n", "\\begin{tabular}{ll}\n", " species & data\\\\\n", " & \\\\\n", "\\hline\n", "\t Adelie & 46.0, 45.8, 45.6\\\\\n", "\t Chinstrap & 46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 48.5, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 54.2, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 49.6, 50.8, 50.2\\\\\n", "\t Gentoo & 46.1, 50.0, 48.7, 50.0, 47.6, 46.5, 45.4, 46.7, 46.8, 49.0, 45.5, 48.4, 45.8, 49.3, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 46.1, 47.8, 48.2, 50.0, 47.3, 45.1, 59.6, 49.1, 48.4, 48.7, 49.6, 45.3, 49.6, 50.5, 45.5, 50.5, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.5, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 50.8, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 53.4, 48.1, 50.5, 49.8, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 3 × 2\n", "\n", "| species <fct> | data <list> |\n", "|---|---|\n", "| Adelie | 46.0, 45.8, 45.6 |\n", "| Chinstrap | 46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 48.5, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 54.2, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 49.6, 50.8, 50.2 |\n", "| Gentoo | 46.1, 50.0, 48.7, 50.0, 47.6, 46.5, 45.4, 46.7, 46.8, 49.0, 45.5, 48.4, 45.8, 49.3, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 46.1, 47.8, 48.2, 50.0, 47.3, 45.1, 59.6, 49.1, 48.4, 48.7, 49.6, 45.3, 49.6, 50.5, 45.5, 50.5, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.5, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 50.8, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 53.4, 48.1, 50.5, 49.8, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9 |\n", "\n" ], "text/plain": [ " species \n", "1 Adelie \n", "2 Chinstrap\n", "3 Gentoo \n", " data \n", "1 46.0, 45.8, 45.6 \n", "2 46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 48.5, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 54.2, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 49.6, 50.8, 50.2 \n", "3 46.1, 50.0, 48.7, 50.0, 47.6, 46.5, 45.4, 46.7, 46.8, 49.0, 45.5, 48.4, 45.8, 49.3, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 46.1, 47.8, 48.2, 50.0, 47.3, 45.1, 59.6, 49.1, 48.4, 48.7, 49.6, 45.3, 49.6, 50.5, 45.5, 50.5, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.5, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 50.8, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 53.4, 48.1, 50.5, 49.8, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 排序\n", "df %>% \n", " group_by(species) %>% \n", " summarise(data = list(sort(bill_length_mm)))\n", "\n", "# 筛选\n", "df %>% \n", " group_by(species) %>% \n", " summarise(data = list(bill_length_mm[bill_length_mm > 45]))" ] }, { "cell_type": "markdown", "id": "18c3c933", "metadata": {}, "source": [ "### 1 creating\n", " ### `dplyr::mutate()`创建\n", "- 第三种方法是用`rowwise()` + `mutate()`，比如，下面为每个岛屿(island) 创建一个与该岛企鹅数量等长的随机数向量，简单点说，这个岛屿上企鹅有多少只，那么随机数的个数就有多少个。\n", "- `rowwise()`对数据后续的操作按行进行" ] }, { "cell_type": "code", "execution_count": 182, "id": "fe76866b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 3 × 2
speciesn_num
<fct><int>
Adelie 146
Chinstrap 68
Gentoo 119
\n" ], "text/latex": [ "A tibble: 3 × 2\n", "\\begin{tabular}{ll}\n", " species & n\\_num\\\\\n", " & \\\\\n", "\\hline\n", "\t Adelie & 146\\\\\n", "\t Chinstrap & 68\\\\\n", "\t Gentoo & 119\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 3 × 2\n", "\n", "| species <fct> | n_num <int> |\n", "|---|---|\n", "| Adelie | 146 |\n", "| Chinstrap | 68 |\n", "| Gentoo | 119 |\n", "\n" ], "text/plain": [ " species n_num\n", "1 Adelie 146 \n", "2 Chinstrap 68 \n", "3 Gentoo 119 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "penguins %>% \n", " drop_na() %>% \n", " group_by(species) %>% \n", " summarise(\n", " n_num = n()\n", " ) %>% \n", " \n", " rowwise() %>% \n", " mutate(random = list(rnorm(n = n_num))) %>% \n", " ungroup()" ] }, { "cell_type": "markdown", "id": "f9e51215", "metadata": {}, "source": [ "### 2 Unnesting\n", "用`unnest(cols = )`函数可以把列表列转换成常规列的形式,也就是还原成正常样式" ] }, { "cell_type": "code", "execution_count": 169, "id": "3e7eb657", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 3 × 2
speciesdata
<fct><list>
Adelie 39.1, 39.5, 40.3, 36.7, 39.3, 38.9, 39.2, 41.1, 38.6, 34.6, 36.6, 38.7, 42.5, 34.4, 46.0, 37.8, 37.7, 35.9, 38.2, 38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9, 36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36.0, 44.1, 37.0, 39.6, 41.1, 36.0, 42.3, 39.6, 40.1, 35.0, 42.0, 34.5, 41.4, 39.0, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4, 41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5, 42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3, 41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34.0, 39.6, 36.2, 40.8, 38.1, 40.3, 33.1, 43.2, 35.0, 41.0, 37.7, 37.8, 37.9, 39.7, 38.6, 38.2, 38.1, 43.2, 38.1, 45.6, 39.7, 42.2, 39.6, 42.7, 38.6, 37.3, 35.7, 41.1, 36.2, 37.7, 40.2, 41.4, 35.2, 40.6, 38.8, 41.5, 39.0, 44.1, 38.5, 43.1, 36.8, 37.5, 38.1, 41.1, 35.6, 40.2, 37.0, 39.7, 40.2, 40.6, 32.1, 40.7, 37.3, 39.0, 39.2, 36.6, 36.0, 37.8, 36.0, 41.5, 18.7, 17.4, 18.0, 19.3, 20.6, 17.8, 19.6, 17.6, 21.2, 21.1, 17.8, 19.0, 20.7, 18.4, 21.5, 18.3, 18.7, 19.2, 18.1, 17.2, 18.9, 18.6, 17.9, 18.6, 18.9, 16.7, 18.1, 17.8, 18.9, 17.0, 21.1, 20.0, 18.5, 19.3, 19.1, 18.0, 18.4, 18.5, 19.7, 16.9, 18.8, 19.0, 17.9, 21.2, 17.7, 18.9, 17.9, 19.5, 18.1, 18.6, 17.5, 18.8, 16.6, 19.1, 16.9, 21.1, 17.0, 18.2, 17.1, 18.0, 16.2, 19.1, 16.6, 19.4, 19.0, 18.4, 17.2, 18.9, 17.5, 18.5, 16.8, 19.4, 16.1, 19.1, 17.2, 17.6, 18.8, 19.4, 17.8, 20.3, 19.5, 18.6, 19.2, 18.8, 18.0, 18.1, 17.1, 18.1, 17.3, 18.9, 18.6, 18.5, 16.1, 18.5, 17.9, 20.0, 16.0, 20.0, 18.6, 18.9, 17.2, 20.0, 17.0, 19.0, 16.5, 20.3, 17.7, 19.5, 20.7, 18.3, 17.0, 20.5, 17.0, 18.6, 17.2, 19.8, 17.0, 18.5, 15.9, 19.0, 17.6, 18.3, 17.1, 18.0, 17.9, 19.2, 18.5, 18.5, 17.6, 17.5, 17.5, 20.1, 16.5, 17.9, 17.1, 17.2, 15.5, 17.0, 16.8, 18.7, 18.6, 18.4, 17.8, 18.1, 17.1, 18.5, 3750.0, 3800.0, 3250.0, 3450.0, 3650.0, 3625.0, 4675.0, 3200.0, 3800.0, 4400.0, 3700.0, 3450.0, 4500.0, 3325.0, 4200.0, 3400.0, 3600.0, 3800.0, 3950.0, 3800.0, 3800.0, 3550.0, 3200.0, 3150.0, 3950.0, 3250.0, 3900.0, 3300.0, 3900.0, 3325.0, 4150.0, 3950.0, 3550.0, 3300.0, 4650.0, 3150.0, 3900.0, 3100.0, 4400.0, 3000.0, 4600.0, 3425.0, 3450.0, 4150.0, 3500.0, 4300.0, 3450.0, 4050.0, 2900.0, 3700.0, 3550.0, 3800.0, 2850.0, 3750.0, 3150.0, 4400.0, 3600.0, 4050.0, 2850.0, 3950.0, 3350.0, 4100.0, 3050.0, 4450.0, 3600.0, 3900.0, 3550.0, 4150.0, 3700.0, 4250.0, 3700.0, 3900.0, 3550.0, 4000.0, 3200.0, 4700.0, 3800.0, 4200.0, 3350.0, 3550.0, 3800.0, 3500.0, 3950.0, 3600.0, 3550.0, 4300.0, 3400.0, 4450.0, 3300.0, 4300.0, 3700.0, 4350.0, 2900.0, 4100.0, 3725.0, 4725.0, 3075.0, 4250.0, 2925.0, 3550.0, 3750.0, 3900.0, 3175.0, 4775.0, 3825.0, 4600.0, 3200.0, 4275.0, 3900.0, 4075.0, 2900.0, 3775.0, 3350.0, 3325.0, 3150.0, 3500.0, 3450.0, 3875.0, 3050.0, 4000.0, 3275.0, 4300.0, 3050.0, 4000.0, 3325.0, 3500.0, 3500.0, 4475.0, 3425.0, 3900.0, 3175.0, 3975.0, 3400.0, 4250.0, 3400.0, 3475.0, 3050.0, 3725.0, 3000.0, 3650.0, 4250.0, 3475.0, 3450.0, 3750.0, 3700.0, 4000.0
Gentoo 46.1, 50.0, 48.7, 50.0, 47.6, 46.5, 45.4, 46.7, 43.3, 46.8, 40.9, 49.0, 45.5, 48.4, 45.8, 49.3, 42.0, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1, 47.8, 48.2, 50.0, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4, 42.6, 44.4, 44.0, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6, 45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.0, 43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 44.9, 50.8, 43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9, 13.2, 16.3, 14.1, 15.2, 14.5, 13.5, 14.6, 15.3, 13.4, 15.4, 13.7, 16.1, 13.7, 14.6, 14.6, 15.7, 13.5, 15.2, 14.5, 15.1, 14.3, 14.5, 14.5, 15.8, 13.1, 15.1, 15.0, 14.3, 15.3, 15.3, 14.2, 14.5, 17.0, 14.8, 16.3, 13.7, 17.3, 13.6, 15.7, 13.7, 16.0, 13.7, 15.0, 15.9, 13.9, 13.9, 15.9, 13.3, 15.8, 14.2, 14.1, 14.4, 15.0, 14.4, 15.4, 13.9, 15.0, 14.5, 15.3, 13.8, 14.9, 13.9, 15.7, 14.2, 16.8, 16.2, 14.2, 15.0, 15.0, 15.6, 15.6, 14.8, 15.0, 16.0, 14.2, 16.3, 13.8, 16.4, 14.5, 15.6, 14.6, 15.9, 13.8, 17.3, 14.4, 14.2, 14.0, 17.0, 15.0, 17.1, 14.5, 16.1, 14.7, 15.7, 15.8, 14.6, 14.4, 16.5, 15.0, 17.0, 15.5, 15.0, 16.1, 14.7, 15.8, 14.0, 15.1, 15.2, 15.9, 15.2, 16.3, 14.1, 16.0, 16.2, 13.7, 14.3, 15.7, 14.8, 16.1, 4500.0, 5700.0, 4450.0, 5700.0, 5400.0, 4550.0, 4800.0, 5200.0, 4400.0, 5150.0, 4650.0, 5550.0, 4650.0, 5850.0, 4200.0, 5850.0, 4150.0, 6300.0, 4800.0, 5350.0, 5700.0, 5000.0, 4400.0, 5050.0, 5000.0, 5100.0, 5650.0, 4600.0, 5550.0, 5250.0, 4700.0, 5050.0, 6050.0, 5150.0, 5400.0, 4950.0, 5250.0, 4350.0, 5350.0, 3950.0, 5700.0, 4300.0, 4750.0, 5550.0, 4900.0, 4200.0, 5400.0, 5100.0, 5300.0, 4850.0, 5300.0, 4400.0, 5000.0, 4900.0, 5050.0, 4300.0, 5000.0, 4450.0, 5550.0, 4200.0, 5300.0, 4400.0, 5650.0, 4700.0, 5700.0, 5800.0, 4700.0, 5550.0, 4750.0, 5000.0, 5100.0, 5200.0, 4700.0, 5800.0, 4600.0, 6000.0, 4750.0, 5950.0, 4625.0, 5450.0, 4725.0, 5350.0, 4750.0, 5600.0, 4600.0, 5300.0, 4875.0, 5550.0, 4950.0, 5400.0, 4750.0, 5650.0, 4850.0, 5200.0, 4925.0, 4875.0, 4625.0, 5250.0, 4850.0, 5600.0, 4975.0, 5500.0, 5500.0, 4700.0, 5500.0, 4575.0, 5500.0, 5000.0, 5950.0, 4650.0, 5500.0, 4375.0, 5850.0, 6000.0, 4925.0, 4850.0, 5750.0, 5200.0, 5400.0
Chinstrap46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 50.8, 50.2, 17.9, 19.5, 19.2, 18.7, 19.8, 17.8, 18.2, 18.2, 18.9, 19.9, 17.8, 20.3, 17.3, 18.1, 17.1, 19.6, 20.0, 17.8, 18.6, 18.2, 17.3, 17.5, 16.6, 19.4, 17.9, 19.0, 18.4, 19.0, 17.8, 20.0, 16.6, 20.8, 16.7, 18.8, 18.6, 16.8, 18.3, 20.7, 16.6, 19.9, 19.5, 17.5, 19.1, 17.0, 17.9, 18.5, 17.9, 19.6, 18.7, 17.3, 16.4, 19.0, 17.3, 19.7, 17.3, 18.8, 16.6, 19.9, 18.8, 19.4, 19.5, 16.5, 17.0, 19.8, 18.1, 18.2, 19.0, 18.7, 3500.0, 3900.0, 3650.0, 3525.0, 3725.0, 3950.0, 3250.0, 3750.0, 4150.0, 3700.0, 3800.0, 3775.0, 3700.0, 4050.0, 3575.0, 4050.0, 3300.0, 3700.0, 3450.0, 4400.0, 3600.0, 3400.0, 2900.0, 3800.0, 3300.0, 4150.0, 3400.0, 3800.0, 3700.0, 4550.0, 3200.0, 4300.0, 3350.0, 4100.0, 3600.0, 3900.0, 3850.0, 4800.0, 2700.0, 4500.0, 3950.0, 3650.0, 3550.0, 3500.0, 3675.0, 4450.0, 3400.0, 4300.0, 3250.0, 3675.0, 3325.0, 3950.0, 3600.0, 4050.0, 3350.0, 3450.0, 3250.0, 4050.0, 3800.0, 3525.0, 3950.0, 3650.0, 3650.0, 4000.0, 3400.0, 3775.0, 4100.0, 3775.0
\n" ], "text/latex": [ "A tibble: 3 × 2\n", "\\begin{tabular}{ll}\n", " species & data\\\\\n", " & \\\\\n", "\\hline\n", "\t Adelie & 39.1, 39.5, 40.3, 36.7, 39.3, 38.9, 39.2, 41.1, 38.6, 34.6, 36.6, 38.7, 42.5, 34.4, 46.0, 37.8, 37.7, 35.9, 38.2, 38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9, 36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36.0, 44.1, 37.0, 39.6, 41.1, 36.0, 42.3, 39.6, 40.1, 35.0, 42.0, 34.5, 41.4, 39.0, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4, 41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5, 42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3, 41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34.0, 39.6, 36.2, 40.8, 38.1, 40.3, 33.1, 43.2, 35.0, 41.0, 37.7, 37.8, 37.9, 39.7, 38.6, 38.2, 38.1, 43.2, 38.1, 45.6, 39.7, 42.2, 39.6, 42.7, 38.6, 37.3, 35.7, 41.1, 36.2, 37.7, 40.2, 41.4, 35.2, 40.6, 38.8, 41.5, 39.0, 44.1, 38.5, 43.1, 36.8, 37.5, 38.1, 41.1, 35.6, 40.2, 37.0, 39.7, 40.2, 40.6, 32.1, 40.7, 37.3, 39.0, 39.2, 36.6, 36.0, 37.8, 36.0, 41.5, 18.7, 17.4, 18.0, 19.3, 20.6, 17.8, 19.6, 17.6, 21.2, 21.1, 17.8, 19.0, 20.7, 18.4, 21.5, 18.3, 18.7, 19.2, 18.1, 17.2, 18.9, 18.6, 17.9, 18.6, 18.9, 16.7, 18.1, 17.8, 18.9, 17.0, 21.1, 20.0, 18.5, 19.3, 19.1, 18.0, 18.4, 18.5, 19.7, 16.9, 18.8, 19.0, 17.9, 21.2, 17.7, 18.9, 17.9, 19.5, 18.1, 18.6, 17.5, 18.8, 16.6, 19.1, 16.9, 21.1, 17.0, 18.2, 17.1, 18.0, 16.2, 19.1, 16.6, 19.4, 19.0, 18.4, 17.2, 18.9, 17.5, 18.5, 16.8, 19.4, 16.1, 19.1, 17.2, 17.6, 18.8, 19.4, 17.8, 20.3, 19.5, 18.6, 19.2, 18.8, 18.0, 18.1, 17.1, 18.1, 17.3, 18.9, 18.6, 18.5, 16.1, 18.5, 17.9, 20.0, 16.0, 20.0, 18.6, 18.9, 17.2, 20.0, 17.0, 19.0, 16.5, 20.3, 17.7, 19.5, 20.7, 18.3, 17.0, 20.5, 17.0, 18.6, 17.2, 19.8, 17.0, 18.5, 15.9, 19.0, 17.6, 18.3, 17.1, 18.0, 17.9, 19.2, 18.5, 18.5, 17.6, 17.5, 17.5, 20.1, 16.5, 17.9, 17.1, 17.2, 15.5, 17.0, 16.8, 18.7, 18.6, 18.4, 17.8, 18.1, 17.1, 18.5, 3750.0, 3800.0, 3250.0, 3450.0, 3650.0, 3625.0, 4675.0, 3200.0, 3800.0, 4400.0, 3700.0, 3450.0, 4500.0, 3325.0, 4200.0, 3400.0, 3600.0, 3800.0, 3950.0, 3800.0, 3800.0, 3550.0, 3200.0, 3150.0, 3950.0, 3250.0, 3900.0, 3300.0, 3900.0, 3325.0, 4150.0, 3950.0, 3550.0, 3300.0, 4650.0, 3150.0, 3900.0, 3100.0, 4400.0, 3000.0, 4600.0, 3425.0, 3450.0, 4150.0, 3500.0, 4300.0, 3450.0, 4050.0, 2900.0, 3700.0, 3550.0, 3800.0, 2850.0, 3750.0, 3150.0, 4400.0, 3600.0, 4050.0, 2850.0, 3950.0, 3350.0, 4100.0, 3050.0, 4450.0, 3600.0, 3900.0, 3550.0, 4150.0, 3700.0, 4250.0, 3700.0, 3900.0, 3550.0, 4000.0, 3200.0, 4700.0, 3800.0, 4200.0, 3350.0, 3550.0, 3800.0, 3500.0, 3950.0, 3600.0, 3550.0, 4300.0, 3400.0, 4450.0, 3300.0, 4300.0, 3700.0, 4350.0, 2900.0, 4100.0, 3725.0, 4725.0, 3075.0, 4250.0, 2925.0, 3550.0, 3750.0, 3900.0, 3175.0, 4775.0, 3825.0, 4600.0, 3200.0, 4275.0, 3900.0, 4075.0, 2900.0, 3775.0, 3350.0, 3325.0, 3150.0, 3500.0, 3450.0, 3875.0, 3050.0, 4000.0, 3275.0, 4300.0, 3050.0, 4000.0, 3325.0, 3500.0, 3500.0, 4475.0, 3425.0, 3900.0, 3175.0, 3975.0, 3400.0, 4250.0, 3400.0, 3475.0, 3050.0, 3725.0, 3000.0, 3650.0, 4250.0, 3475.0, 3450.0, 3750.0, 3700.0, 4000.0\\\\\n", "\t Gentoo & 46.1, 50.0, 48.7, 50.0, 47.6, 46.5, 45.4, 46.7, 43.3, 46.8, 40.9, 49.0, 45.5, 48.4, 45.8, 49.3, 42.0, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1, 47.8, 48.2, 50.0, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4, 42.6, 44.4, 44.0, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6, 45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.0, 43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 44.9, 50.8, 43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9, 13.2, 16.3, 14.1, 15.2, 14.5, 13.5, 14.6, 15.3, 13.4, 15.4, 13.7, 16.1, 13.7, 14.6, 14.6, 15.7, 13.5, 15.2, 14.5, 15.1, 14.3, 14.5, 14.5, 15.8, 13.1, 15.1, 15.0, 14.3, 15.3, 15.3, 14.2, 14.5, 17.0, 14.8, 16.3, 13.7, 17.3, 13.6, 15.7, 13.7, 16.0, 13.7, 15.0, 15.9, 13.9, 13.9, 15.9, 13.3, 15.8, 14.2, 14.1, 14.4, 15.0, 14.4, 15.4, 13.9, 15.0, 14.5, 15.3, 13.8, 14.9, 13.9, 15.7, 14.2, 16.8, 16.2, 14.2, 15.0, 15.0, 15.6, 15.6, 14.8, 15.0, 16.0, 14.2, 16.3, 13.8, 16.4, 14.5, 15.6, 14.6, 15.9, 13.8, 17.3, 14.4, 14.2, 14.0, 17.0, 15.0, 17.1, 14.5, 16.1, 14.7, 15.7, 15.8, 14.6, 14.4, 16.5, 15.0, 17.0, 15.5, 15.0, 16.1, 14.7, 15.8, 14.0, 15.1, 15.2, 15.9, 15.2, 16.3, 14.1, 16.0, 16.2, 13.7, 14.3, 15.7, 14.8, 16.1, 4500.0, 5700.0, 4450.0, 5700.0, 5400.0, 4550.0, 4800.0, 5200.0, 4400.0, 5150.0, 4650.0, 5550.0, 4650.0, 5850.0, 4200.0, 5850.0, 4150.0, 6300.0, 4800.0, 5350.0, 5700.0, 5000.0, 4400.0, 5050.0, 5000.0, 5100.0, 5650.0, 4600.0, 5550.0, 5250.0, 4700.0, 5050.0, 6050.0, 5150.0, 5400.0, 4950.0, 5250.0, 4350.0, 5350.0, 3950.0, 5700.0, 4300.0, 4750.0, 5550.0, 4900.0, 4200.0, 5400.0, 5100.0, 5300.0, 4850.0, 5300.0, 4400.0, 5000.0, 4900.0, 5050.0, 4300.0, 5000.0, 4450.0, 5550.0, 4200.0, 5300.0, 4400.0, 5650.0, 4700.0, 5700.0, 5800.0, 4700.0, 5550.0, 4750.0, 5000.0, 5100.0, 5200.0, 4700.0, 5800.0, 4600.0, 6000.0, 4750.0, 5950.0, 4625.0, 5450.0, 4725.0, 5350.0, 4750.0, 5600.0, 4600.0, 5300.0, 4875.0, 5550.0, 4950.0, 5400.0, 4750.0, 5650.0, 4850.0, 5200.0, 4925.0, 4875.0, 4625.0, 5250.0, 4850.0, 5600.0, 4975.0, 5500.0, 5500.0, 4700.0, 5500.0, 4575.0, 5500.0, 5000.0, 5950.0, 4650.0, 5500.0, 4375.0, 5850.0, 6000.0, 4925.0, 4850.0, 5750.0, 5200.0, 5400.0\\\\\n", "\t Chinstrap & 46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 50.8, 50.2, 17.9, 19.5, 19.2, 18.7, 19.8, 17.8, 18.2, 18.2, 18.9, 19.9, 17.8, 20.3, 17.3, 18.1, 17.1, 19.6, 20.0, 17.8, 18.6, 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4400.0, 3600.0, 4050.0, 2850.0, 3950.0, 3350.0, 4100.0, 3050.0, 4450.0, 3600.0, 3900.0, 3550.0, 4150.0, 3700.0, 4250.0, 3700.0, 3900.0, 3550.0, 4000.0, 3200.0, 4700.0, 3800.0, 4200.0, 3350.0, 3550.0, 3800.0, 3500.0, 3950.0, 3600.0, 3550.0, 4300.0, 3400.0, 4450.0, 3300.0, 4300.0, 3700.0, 4350.0, 2900.0, 4100.0, 3725.0, 4725.0, 3075.0, 4250.0, 2925.0, 3550.0, 3750.0, 3900.0, 3175.0, 4775.0, 3825.0, 4600.0, 3200.0, 4275.0, 3900.0, 4075.0, 2900.0, 3775.0, 3350.0, 3325.0, 3150.0, 3500.0, 3450.0, 3875.0, 3050.0, 4000.0, 3275.0, 4300.0, 3050.0, 4000.0, 3325.0, 3500.0, 3500.0, 4475.0, 3425.0, 3900.0, 3175.0, 3975.0, 3400.0, 4250.0, 3400.0, 3475.0, 3050.0, 3725.0, 3000.0, 3650.0, 4250.0, 3475.0, 3450.0, 3750.0, 3700.0, 4000.0\n", "2 46.1, 50.0, 48.7, 50.0, 47.6, 46.5, 45.4, 46.7, 43.3, 46.8, 40.9, 49.0, 45.5, 48.4, 45.8, 49.3, 42.0, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1, 47.8, 48.2, 50.0, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4, 42.6, 44.4, 44.0, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6, 45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.0, 43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 44.9, 50.8, 43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9, 13.2, 16.3, 14.1, 15.2, 14.5, 13.5, 14.6, 15.3, 13.4, 15.4, 13.7, 16.1, 13.7, 14.6, 14.6, 15.7, 13.5, 15.2, 14.5, 15.1, 14.3, 14.5, 14.5, 15.8, 13.1, 15.1, 15.0, 14.3, 15.3, 15.3, 14.2, 14.5, 17.0, 14.8, 16.3, 13.7, 17.3, 13.6, 15.7, 13.7, 16.0, 13.7, 15.0, 15.9, 13.9, 13.9, 15.9, 13.3, 15.8, 14.2, 14.1, 14.4, 15.0, 14.4, 15.4, 13.9, 15.0, 14.5, 15.3, 13.8, 14.9, 13.9, 15.7, 14.2, 16.8, 16.2, 14.2, 15.0, 15.0, 15.6, 15.6, 14.8, 15.0, 16.0, 14.2, 16.3, 13.8, 16.4, 14.5, 15.6, 14.6, 15.9, 13.8, 17.3, 14.4, 14.2, 14.0, 17.0, 15.0, 17.1, 14.5, 16.1, 14.7, 15.7, 15.8, 14.6, 14.4, 16.5, 15.0, 17.0, 15.5, 15.0, 16.1, 14.7, 15.8, 14.0, 15.1, 15.2, 15.9, 15.2, 16.3, 14.1, 16.0, 16.2, 13.7, 14.3, 15.7, 14.8, 16.1, 4500.0, 5700.0, 4450.0, 5700.0, 5400.0, 4550.0, 4800.0, 5200.0, 4400.0, 5150.0, 4650.0, 5550.0, 4650.0, 5850.0, 4200.0, 5850.0, 4150.0, 6300.0, 4800.0, 5350.0, 5700.0, 5000.0, 4400.0, 5050.0, 5000.0, 5100.0, 5650.0, 4600.0, 5550.0, 5250.0, 4700.0, 5050.0, 6050.0, 5150.0, 5400.0, 4950.0, 5250.0, 4350.0, 5350.0, 3950.0, 5700.0, 4300.0, 4750.0, 5550.0, 4900.0, 4200.0, 5400.0, 5100.0, 5300.0, 4850.0, 5300.0, 4400.0, 5000.0, 4900.0, 5050.0, 4300.0, 5000.0, 4450.0, 5550.0, 4200.0, 5300.0, 4400.0, 5650.0, 4700.0, 5700.0, 5800.0, 4700.0, 5550.0, 4750.0, 5000.0, 5100.0, 5200.0, 4700.0, 5800.0, 4600.0, 6000.0, 4750.0, 5950.0, 4625.0, 5450.0, 4725.0, 5350.0, 4750.0, 5600.0, 4600.0, 5300.0, 4875.0, 5550.0, 4950.0, 5400.0, 4750.0, 5650.0, 4850.0, 5200.0, 4925.0, 4875.0, 4625.0, 5250.0, 4850.0, 5600.0, 4975.0, 5500.0, 5500.0, 4700.0, 5500.0, 4575.0, 5500.0, 5000.0, 5950.0, 4650.0, 5500.0, 4375.0, 5850.0, 6000.0, 4925.0, 4850.0, 5750.0, 5200.0, 5400.0 \n", "3 46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 50.8, 50.2, 17.9, 19.5, 19.2, 18.7, 19.8, 17.8, 18.2, 18.2, 18.9, 19.9, 17.8, 20.3, 17.3, 18.1, 17.1, 19.6, 20.0, 17.8, 18.6, 18.2, 17.3, 17.5, 16.6, 19.4, 17.9, 19.0, 18.4, 19.0, 17.8, 20.0, 16.6, 20.8, 16.7, 18.8, 18.6, 16.8, 18.3, 20.7, 16.6, 19.9, 19.5, 17.5, 19.1, 17.0, 17.9, 18.5, 17.9, 19.6, 18.7, 17.3, 16.4, 19.0, 17.3, 19.7, 17.3, 18.8, 16.6, 19.9, 18.8, 19.4, 19.5, 16.5, 17.0, 19.8, 18.1, 18.2, 19.0, 18.7, 3500.0, 3900.0, 3650.0, 3525.0, 3725.0, 3950.0, 3250.0, 3750.0, 4150.0, 3700.0, 3800.0, 3775.0, 3700.0, 4050.0, 3575.0, 4050.0, 3300.0, 3700.0, 3450.0, 4400.0, 3600.0, 3400.0, 2900.0, 3800.0, 3300.0, 4150.0, 3400.0, 3800.0, 3700.0, 4550.0, 3200.0, 4300.0, 3350.0, 4100.0, 3600.0, 3900.0, 3850.0, 4800.0, 2700.0, 4500.0, 3950.0, 3650.0, 3550.0, 3500.0, 3675.0, 4450.0, 3400.0, 4300.0, 3250.0, 3675.0, 3325.0, 3950.0, 3600.0, 4050.0, 3350.0, 3450.0, 3250.0, 4050.0, 3800.0, 3525.0, 3950.0, 3650.0, 3650.0, 4000.0, 3400.0, 3775.0, 4100.0, 3775.0 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tb" ] }, { "cell_type": "code", "execution_count": 171, "id": "ad45a571", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 6 × 4
speciesbill_length_mmbill_depth_mmbody_mass_g
<fct><dbl><dbl><int>
Adelie39.118.73750
Adelie39.517.43800
Adelie40.318.03250
Adelie36.719.33450
Adelie39.320.63650
Adelie38.917.83625
\n" ], "text/latex": [ "A tibble: 6 × 4\n", "\\begin{tabular}{llll}\n", " species & bill\\_length\\_mm & bill\\_depth\\_mm & body\\_mass\\_g\\\\\n", " & & & \\\\\n", "\\hline\n", "\t Adelie & 39.1 & 18.7 & 3750\\\\\n", "\t Adelie & 39.5 & 17.4 & 3800\\\\\n", "\t Adelie & 40.3 & 18.0 & 3250\\\\\n", "\t Adelie & 36.7 & 19.3 & 3450\\\\\n", "\t Adelie & 39.3 & 20.6 & 3650\\\\\n", "\t Adelie & 38.9 & 17.8 & 3625\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 6 × 4\n", "\n", "| species <fct> | bill_length_mm <dbl> | bill_depth_mm <dbl> | body_mass_g <int> |\n", "|---|---|---|---|\n", "| Adelie | 39.1 | 18.7 | 3750 |\n", "| Adelie | 39.5 | 17.4 | 3800 |\n", "| Adelie | 40.3 | 18.0 | 3250 |\n", "| Adelie | 36.7 | 19.3 | 3450 |\n", "| Adelie | 39.3 | 20.6 | 3650 |\n", "| Adelie | 38.9 | 17.8 | 3625 |\n", "\n" ], "text/plain": [ " species bill_length_mm bill_depth_mm body_mass_g\n", "1 Adelie 39.1 18.7 3750 \n", "2 Adelie 39.5 17.4 3800 \n", "3 Adelie 40.3 18.0 3250 \n", "4 Adelie 36.7 19.3 3450 \n", "5 Adelie 39.3 20.6 3650 \n", "6 Adelie 38.9 17.8 3625 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tb %>% \n", " unnest(cols = data) %>% \n", " head()" ] }, { "cell_type": "markdown", "id": "891cd861", "metadata": {}, "source": [ "### Manipulating\n", "操控列表列是一件有趣的事情，我们常常会借助于行方向的操作(`rowwise`)。比如找出每个岛屿企鹅的数量，我们需要对`data`列表列的元素依次迭代，" ] }, { "cell_type": "code", "execution_count": 184, "id": "c22accb9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A rowwise_df: 3 × 3
speciesdatanum_species
<fct><list><int>
Adelie 39.1, 39.5, 40.3, 36.7, 39.3, 38.9, 39.2, 41.1, 38.6, 34.6, 36.6, 38.7, 42.5, 34.4, 46.0, 37.8, 37.7, 35.9, 38.2, 38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9, 36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36.0, 44.1, 37.0, 39.6, 41.1, 36.0, 42.3, 39.6, 40.1, 35.0, 42.0, 34.5, 41.4, 39.0, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4, 41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5, 42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3, 41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34.0, 39.6, 36.2, 40.8, 38.1, 40.3, 33.1, 43.2, 35.0, 41.0, 37.7, 37.8, 37.9, 39.7, 38.6, 38.2, 38.1, 43.2, 38.1, 45.6, 39.7, 42.2, 39.6, 42.7, 38.6, 37.3, 35.7, 41.1, 36.2, 37.7, 40.2, 41.4, 35.2, 40.6, 38.8, 41.5, 39.0, 44.1, 38.5, 43.1, 36.8, 37.5, 38.1, 41.1, 35.6, 40.2, 37.0, 39.7, 40.2, 40.6, 32.1, 40.7, 37.3, 39.0, 39.2, 36.6, 36.0, 37.8, 36.0, 41.5, 18.7, 17.4, 18.0, 19.3, 20.6, 17.8, 19.6, 17.6, 21.2, 21.1, 17.8, 19.0, 20.7, 18.4, 21.5, 18.3, 18.7, 19.2, 18.1, 17.2, 18.9, 18.6, 17.9, 18.6, 18.9, 16.7, 18.1, 17.8, 18.9, 17.0, 21.1, 20.0, 18.5, 19.3, 19.1, 18.0, 18.4, 18.5, 19.7, 16.9, 18.8, 19.0, 17.9, 21.2, 17.7, 18.9, 17.9, 19.5, 18.1, 18.6, 17.5, 18.8, 16.6, 19.1, 16.9, 21.1, 17.0, 18.2, 17.1, 18.0, 16.2, 19.1, 16.6, 19.4, 19.0, 18.4, 17.2, 18.9, 17.5, 18.5, 16.8, 19.4, 16.1, 19.1, 17.2, 17.6, 18.8, 19.4, 17.8, 20.3, 19.5, 18.6, 19.2, 18.8, 18.0, 18.1, 17.1, 18.1, 17.3, 18.9, 18.6, 18.5, 16.1, 18.5, 17.9, 20.0, 16.0, 20.0, 18.6, 18.9, 17.2, 20.0, 17.0, 19.0, 16.5, 20.3, 17.7, 19.5, 20.7, 18.3, 17.0, 20.5, 17.0, 18.6, 17.2, 19.8, 17.0, 18.5, 15.9, 19.0, 17.6, 18.3, 17.1, 18.0, 17.9, 19.2, 18.5, 18.5, 17.6, 17.5, 17.5, 20.1, 16.5, 17.9, 17.1, 17.2, 15.5, 17.0, 16.8, 18.7, 18.6, 18.4, 17.8, 18.1, 17.1, 18.5, 3750.0, 3800.0, 3250.0, 3450.0, 3650.0, 3625.0, 4675.0, 3200.0, 3800.0, 4400.0, 3700.0, 3450.0, 4500.0, 3325.0, 4200.0, 3400.0, 3600.0, 3800.0, 3950.0, 3800.0, 3800.0, 3550.0, 3200.0, 3150.0, 3950.0, 3250.0, 3900.0, 3300.0, 3900.0, 3325.0, 4150.0, 3950.0, 3550.0, 3300.0, 4650.0, 3150.0, 3900.0, 3100.0, 4400.0, 3000.0, 4600.0, 3425.0, 3450.0, 4150.0, 3500.0, 4300.0, 3450.0, 4050.0, 2900.0, 3700.0, 3550.0, 3800.0, 2850.0, 3750.0, 3150.0, 4400.0, 3600.0, 4050.0, 2850.0, 3950.0, 3350.0, 4100.0, 3050.0, 4450.0, 3600.0, 3900.0, 3550.0, 4150.0, 3700.0, 4250.0, 3700.0, 3900.0, 3550.0, 4000.0, 3200.0, 4700.0, 3800.0, 4200.0, 3350.0, 3550.0, 3800.0, 3500.0, 3950.0, 3600.0, 3550.0, 4300.0, 3400.0, 4450.0, 3300.0, 4300.0, 3700.0, 4350.0, 2900.0, 4100.0, 3725.0, 4725.0, 3075.0, 4250.0, 2925.0, 3550.0, 3750.0, 3900.0, 3175.0, 4775.0, 3825.0, 4600.0, 3200.0, 4275.0, 3900.0, 4075.0, 2900.0, 3775.0, 3350.0, 3325.0, 3150.0, 3500.0, 3450.0, 3875.0, 3050.0, 4000.0, 3275.0, 4300.0, 3050.0, 4000.0, 3325.0, 3500.0, 3500.0, 4475.0, 3425.0, 3900.0, 3175.0, 3975.0, 3400.0, 4250.0, 3400.0, 3475.0, 3050.0, 3725.0, 3000.0, 3650.0, 4250.0, 3475.0, 3450.0, 3750.0, 3700.0, 4000.0146
Gentoo 46.1, 50.0, 48.7, 50.0, 47.6, 46.5, 45.4, 46.7, 43.3, 46.8, 40.9, 49.0, 45.5, 48.4, 45.8, 49.3, 42.0, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1, 47.8, 48.2, 50.0, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4, 42.6, 44.4, 44.0, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6, 45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.0, 43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 44.9, 50.8, 43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9, 13.2, 16.3, 14.1, 15.2, 14.5, 13.5, 14.6, 15.3, 13.4, 15.4, 13.7, 16.1, 13.7, 14.6, 14.6, 15.7, 13.5, 15.2, 14.5, 15.1, 14.3, 14.5, 14.5, 15.8, 13.1, 15.1, 15.0, 14.3, 15.3, 15.3, 14.2, 14.5, 17.0, 14.8, 16.3, 13.7, 17.3, 13.6, 15.7, 13.7, 16.0, 13.7, 15.0, 15.9, 13.9, 13.9, 15.9, 13.3, 15.8, 14.2, 14.1, 14.4, 15.0, 14.4, 15.4, 13.9, 15.0, 14.5, 15.3, 13.8, 14.9, 13.9, 15.7, 14.2, 16.8, 16.2, 14.2, 15.0, 15.0, 15.6, 15.6, 14.8, 15.0, 16.0, 14.2, 16.3, 13.8, 16.4, 14.5, 15.6, 14.6, 15.9, 13.8, 17.3, 14.4, 14.2, 14.0, 17.0, 15.0, 17.1, 14.5, 16.1, 14.7, 15.7, 15.8, 14.6, 14.4, 16.5, 15.0, 17.0, 15.5, 15.0, 16.1, 14.7, 15.8, 14.0, 15.1, 15.2, 15.9, 15.2, 16.3, 14.1, 16.0, 16.2, 13.7, 14.3, 15.7, 14.8, 16.1, 4500.0, 5700.0, 4450.0, 5700.0, 5400.0, 4550.0, 4800.0, 5200.0, 4400.0, 5150.0, 4650.0, 5550.0, 4650.0, 5850.0, 4200.0, 5850.0, 4150.0, 6300.0, 4800.0, 5350.0, 5700.0, 5000.0, 4400.0, 5050.0, 5000.0, 5100.0, 5650.0, 4600.0, 5550.0, 5250.0, 4700.0, 5050.0, 6050.0, 5150.0, 5400.0, 4950.0, 5250.0, 4350.0, 5350.0, 3950.0, 5700.0, 4300.0, 4750.0, 5550.0, 4900.0, 4200.0, 5400.0, 5100.0, 5300.0, 4850.0, 5300.0, 4400.0, 5000.0, 4900.0, 5050.0, 4300.0, 5000.0, 4450.0, 5550.0, 4200.0, 5300.0, 4400.0, 5650.0, 4700.0, 5700.0, 5800.0, 4700.0, 5550.0, 4750.0, 5000.0, 5100.0, 5200.0, 4700.0, 5800.0, 4600.0, 6000.0, 4750.0, 5950.0, 4625.0, 5450.0, 4725.0, 5350.0, 4750.0, 5600.0, 4600.0, 5300.0, 4875.0, 5550.0, 4950.0, 5400.0, 4750.0, 5650.0, 4850.0, 5200.0, 4925.0, 4875.0, 4625.0, 5250.0, 4850.0, 5600.0, 4975.0, 5500.0, 5500.0, 4700.0, 5500.0, 4575.0, 5500.0, 5000.0, 5950.0, 4650.0, 5500.0, 4375.0, 5850.0, 6000.0, 4925.0, 4850.0, 5750.0, 5200.0, 5400.0119
Chinstrap46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 50.8, 50.2, 17.9, 19.5, 19.2, 18.7, 19.8, 17.8, 18.2, 18.2, 18.9, 19.9, 17.8, 20.3, 17.3, 18.1, 17.1, 19.6, 20.0, 17.8, 18.6, 18.2, 17.3, 17.5, 16.6, 19.4, 17.9, 19.0, 18.4, 19.0, 17.8, 20.0, 16.6, 20.8, 16.7, 18.8, 18.6, 16.8, 18.3, 20.7, 16.6, 19.9, 19.5, 17.5, 19.1, 17.0, 17.9, 18.5, 17.9, 19.6, 18.7, 17.3, 16.4, 19.0, 17.3, 19.7, 17.3, 18.8, 16.6, 19.9, 18.8, 19.4, 19.5, 16.5, 17.0, 19.8, 18.1, 18.2, 19.0, 18.7, 3500.0, 3900.0, 3650.0, 3525.0, 3725.0, 3950.0, 3250.0, 3750.0, 4150.0, 3700.0, 3800.0, 3775.0, 3700.0, 4050.0, 3575.0, 4050.0, 3300.0, 3700.0, 3450.0, 4400.0, 3600.0, 3400.0, 2900.0, 3800.0, 3300.0, 4150.0, 3400.0, 3800.0, 3700.0, 4550.0, 3200.0, 4300.0, 3350.0, 4100.0, 3600.0, 3900.0, 3850.0, 4800.0, 2700.0, 4500.0, 3950.0, 3650.0, 3550.0, 3500.0, 3675.0, 4450.0, 3400.0, 4300.0, 3250.0, 3675.0, 3325.0, 3950.0, 3600.0, 4050.0, 3350.0, 3450.0, 3250.0, 4050.0, 3800.0, 3525.0, 3950.0, 3650.0, 3650.0, 4000.0, 3400.0, 3775.0, 4100.0, 3775.0 68
\n" ], "text/latex": [ "A rowwise\\_df: 3 × 3\n", "\\begin{tabular}{lll}\n", " species & data & num\\_species\\\\\n", " & & \\\\\n", "\\hline\n", "\t Adelie & 39.1, 39.5, 40.3, 36.7, 39.3, 38.9, 39.2, 41.1, 38.6, 34.6, 36.6, 38.7, 42.5, 34.4, 46.0, 37.8, 37.7, 35.9, 38.2, 38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9, 36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36.0, 44.1, 37.0, 39.6, 41.1, 36.0, 42.3, 39.6, 40.1, 35.0, 42.0, 34.5, 41.4, 39.0, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4, 41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5, 42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3, 41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34.0, 39.6, 36.2, 40.8, 38.1, 40.3, 33.1, 43.2, 35.0, 41.0, 37.7, 37.8, 37.9, 39.7, 38.6, 38.2, 38.1, 43.2, 38.1, 45.6, 39.7, 42.2, 39.6, 42.7, 38.6, 37.3, 35.7, 41.1, 36.2, 37.7, 40.2, 41.4, 35.2, 40.6, 38.8, 41.5, 39.0, 44.1, 38.5, 43.1, 36.8, 37.5, 38.1, 41.1, 35.6, 40.2, 37.0, 39.7, 40.2, 40.6, 32.1, 40.7, 37.3, 39.0, 39.2, 36.6, 36.0, 37.8, 36.0, 41.5, 18.7, 17.4, 18.0, 19.3, 20.6, 17.8, 19.6, 17.6, 21.2, 21.1, 17.8, 19.0, 20.7, 18.4, 21.5, 18.3, 18.7, 19.2, 18.1, 17.2, 18.9, 18.6, 17.9, 18.6, 18.9, 16.7, 18.1, 17.8, 18.9, 17.0, 21.1, 20.0, 18.5, 19.3, 19.1, 18.0, 18.4, 18.5, 19.7, 16.9, 18.8, 19.0, 17.9, 21.2, 17.7, 18.9, 17.9, 19.5, 18.1, 18.6, 17.5, 18.8, 16.6, 19.1, 16.9, 21.1, 17.0, 18.2, 17.1, 18.0, 16.2, 19.1, 16.6, 19.4, 19.0, 18.4, 17.2, 18.9, 17.5, 18.5, 16.8, 19.4, 16.1, 19.1, 17.2, 17.6, 18.8, 19.4, 17.8, 20.3, 19.5, 18.6, 19.2, 18.8, 18.0, 18.1, 17.1, 18.1, 17.3, 18.9, 18.6, 18.5, 16.1, 18.5, 17.9, 20.0, 16.0, 20.0, 18.6, 18.9, 17.2, 20.0, 17.0, 19.0, 16.5, 20.3, 17.7, 19.5, 20.7, 18.3, 17.0, 20.5, 17.0, 18.6, 17.2, 19.8, 17.0, 18.5, 15.9, 19.0, 17.6, 18.3, 17.1, 18.0, 17.9, 19.2, 18.5, 18.5, 17.6, 17.5, 17.5, 20.1, 16.5, 17.9, 17.1, 17.2, 15.5, 17.0, 16.8, 18.7, 18.6, 18.4, 17.8, 18.1, 17.1, 18.5, 3750.0, 3800.0, 3250.0, 3450.0, 3650.0, 3625.0, 4675.0, 3200.0, 3800.0, 4400.0, 3700.0, 3450.0, 4500.0, 3325.0, 4200.0, 3400.0, 3600.0, 3800.0, 3950.0, 3800.0, 3800.0, 3550.0, 3200.0, 3150.0, 3950.0, 3250.0, 3900.0, 3300.0, 3900.0, 3325.0, 4150.0, 3950.0, 3550.0, 3300.0, 4650.0, 3150.0, 3900.0, 3100.0, 4400.0, 3000.0, 4600.0, 3425.0, 3450.0, 4150.0, 3500.0, 4300.0, 3450.0, 4050.0, 2900.0, 3700.0, 3550.0, 3800.0, 2850.0, 3750.0, 3150.0, 4400.0, 3600.0, 4050.0, 2850.0, 3950.0, 3350.0, 4100.0, 3050.0, 4450.0, 3600.0, 3900.0, 3550.0, 4150.0, 3700.0, 4250.0, 3700.0, 3900.0, 3550.0, 4000.0, 3200.0, 4700.0, 3800.0, 4200.0, 3350.0, 3550.0, 3800.0, 3500.0, 3950.0, 3600.0, 3550.0, 4300.0, 3400.0, 4450.0, 3300.0, 4300.0, 3700.0, 4350.0, 2900.0, 4100.0, 3725.0, 4725.0, 3075.0, 4250.0, 2925.0, 3550.0, 3750.0, 3900.0, 3175.0, 4775.0, 3825.0, 4600.0, 3200.0, 4275.0, 3900.0, 4075.0, 2900.0, 3775.0, 3350.0, 3325.0, 3150.0, 3500.0, 3450.0, 3875.0, 3050.0, 4000.0, 3275.0, 4300.0, 3050.0, 4000.0, 3325.0, 3500.0, 3500.0, 4475.0, 3425.0, 3900.0, 3175.0, 3975.0, 3400.0, 4250.0, 3400.0, 3475.0, 3050.0, 3725.0, 3000.0, 3650.0, 4250.0, 3475.0, 3450.0, 3750.0, 3700.0, 4000.0 & 146\\\\\n", "\t Gentoo & 46.1, 50.0, 48.7, 50.0, 47.6, 46.5, 45.4, 46.7, 43.3, 46.8, 40.9, 49.0, 45.5, 48.4, 45.8, 49.3, 42.0, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1, 47.8, 48.2, 50.0, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4, 42.6, 44.4, 44.0, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6, 45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.0, 43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 44.9, 50.8, 43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9, 13.2, 16.3, 14.1, 15.2, 14.5, 13.5, 14.6, 15.3, 13.4, 15.4, 13.7, 16.1, 13.7, 14.6, 14.6, 15.7, 13.5, 15.2, 14.5, 15.1, 14.3, 14.5, 14.5, 15.8, 13.1, 15.1, 15.0, 14.3, 15.3, 15.3, 14.2, 14.5, 17.0, 14.8, 16.3, 13.7, 17.3, 13.6, 15.7, 13.7, 16.0, 13.7, 15.0, 15.9, 13.9, 13.9, 15.9, 13.3, 15.8, 14.2, 14.1, 14.4, 15.0, 14.4, 15.4, 13.9, 15.0, 14.5, 15.3, 13.8, 14.9, 13.9, 15.7, 14.2, 16.8, 16.2, 14.2, 15.0, 15.0, 15.6, 15.6, 14.8, 15.0, 16.0, 14.2, 16.3, 13.8, 16.4, 14.5, 15.6, 14.6, 15.9, 13.8, 17.3, 14.4, 14.2, 14.0, 17.0, 15.0, 17.1, 14.5, 16.1, 14.7, 15.7, 15.8, 14.6, 14.4, 16.5, 15.0, 17.0, 15.5, 15.0, 16.1, 14.7, 15.8, 14.0, 15.1, 15.2, 15.9, 15.2, 16.3, 14.1, 16.0, 16.2, 13.7, 14.3, 15.7, 14.8, 16.1, 4500.0, 5700.0, 4450.0, 5700.0, 5400.0, 4550.0, 4800.0, 5200.0, 4400.0, 5150.0, 4650.0, 5550.0, 4650.0, 5850.0, 4200.0, 5850.0, 4150.0, 6300.0, 4800.0, 5350.0, 5700.0, 5000.0, 4400.0, 5050.0, 5000.0, 5100.0, 5650.0, 4600.0, 5550.0, 5250.0, 4700.0, 5050.0, 6050.0, 5150.0, 5400.0, 4950.0, 5250.0, 4350.0, 5350.0, 3950.0, 5700.0, 4300.0, 4750.0, 5550.0, 4900.0, 4200.0, 5400.0, 5100.0, 5300.0, 4850.0, 5300.0, 4400.0, 5000.0, 4900.0, 5050.0, 4300.0, 5000.0, 4450.0, 5550.0, 4200.0, 5300.0, 4400.0, 5650.0, 4700.0, 5700.0, 5800.0, 4700.0, 5550.0, 4750.0, 5000.0, 5100.0, 5200.0, 4700.0, 5800.0, 4600.0, 6000.0, 4750.0, 5950.0, 4625.0, 5450.0, 4725.0, 5350.0, 4750.0, 5600.0, 4600.0, 5300.0, 4875.0, 5550.0, 4950.0, 5400.0, 4750.0, 5650.0, 4850.0, 5200.0, 4925.0, 4875.0, 4625.0, 5250.0, 4850.0, 5600.0, 4975.0, 5500.0, 5500.0, 4700.0, 5500.0, 4575.0, 5500.0, 5000.0, 5950.0, 4650.0, 5500.0, 4375.0, 5850.0, 6000.0, 4925.0, 4850.0, 5750.0, 5200.0, 5400.0 & 119\\\\\n", "\t Chinstrap & 46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 50.8, 50.2, 17.9, 19.5, 19.2, 18.7, 19.8, 17.8, 18.2, 18.2, 18.9, 19.9, 17.8, 20.3, 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38.2, 38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9, 36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36.0, 44.1, 37.0, 39.6, 41.1, 36.0, 42.3, 39.6, 40.1, 35.0, 42.0, 34.5, 41.4, 39.0, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4, 41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5, 42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3, 41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34.0, 39.6, 36.2, 40.8, 38.1, 40.3, 33.1, 43.2, 35.0, 41.0, 37.7, 37.8, 37.9, 39.7, 38.6, 38.2, 38.1, 43.2, 38.1, 45.6, 39.7, 42.2, 39.6, 42.7, 38.6, 37.3, 35.7, 41.1, 36.2, 37.7, 40.2, 41.4, 35.2, 40.6, 38.8, 41.5, 39.0, 44.1, 38.5, 43.1, 36.8, 37.5, 38.1, 41.1, 35.6, 40.2, 37.0, 39.7, 40.2, 40.6, 32.1, 40.7, 37.3, 39.0, 39.2, 36.6, 36.0, 37.8, 36.0, 41.5, 18.7, 17.4, 18.0, 19.3, 20.6, 17.8, 19.6, 17.6, 21.2, 21.1, 17.8, 19.0, 20.7, 18.4, 21.5, 18.3, 18.7, 19.2, 18.1, 17.2, 18.9, 18.6, 17.9, 18.6, 18.9, 16.7, 18.1, 17.8, 18.9, 17.0, 21.1, 20.0, 18.5, 19.3, 19.1, 18.0, 18.4, 18.5, 19.7, 16.9, 18.8, 19.0, 17.9, 21.2, 17.7, 18.9, 17.9, 19.5, 18.1, 18.6, 17.5, 18.8, 16.6, 19.1, 16.9, 21.1, 17.0, 18.2, 17.1, 18.0, 16.2, 19.1, 16.6, 19.4, 19.0, 18.4, 17.2, 18.9, 17.5, 18.5, 16.8, 19.4, 16.1, 19.1, 17.2, 17.6, 18.8, 19.4, 17.8, 20.3, 19.5, 18.6, 19.2, 18.8, 18.0, 18.1, 17.1, 18.1, 17.3, 18.9, 18.6, 18.5, 16.1, 18.5, 17.9, 20.0, 16.0, 20.0, 18.6, 18.9, 17.2, 20.0, 17.0, 19.0, 16.5, 20.3, 17.7, 19.5, 20.7, 18.3, 17.0, 20.5, 17.0, 18.6, 17.2, 19.8, 17.0, 18.5, 15.9, 19.0, 17.6, 18.3, 17.1, 18.0, 17.9, 19.2, 18.5, 18.5, 17.6, 17.5, 17.5, 20.1, 16.5, 17.9, 17.1, 17.2, 15.5, 17.0, 16.8, 18.7, 18.6, 18.4, 17.8, 18.1, 17.1, 18.5, 3750.0, 3800.0, 3250.0, 3450.0, 3650.0, 3625.0, 4675.0, 3200.0, 3800.0, 4400.0, 3700.0, 3450.0, 4500.0, 3325.0, 4200.0, 3400.0, 3600.0, 3800.0, 3950.0, 3800.0, 3800.0, 3550.0, 3200.0, 3150.0, 3950.0, 3250.0, 3900.0, 3300.0, 3900.0, 3325.0, 4150.0, 3950.0, 3550.0, 3300.0, 4650.0, 3150.0, 3900.0, 3100.0, 4400.0, 3000.0, 4600.0, 3425.0, 3450.0, 4150.0, 3500.0, 4300.0, 3450.0, 4050.0, 2900.0, 3700.0, 3550.0, 3800.0, 2850.0, 3750.0, 3150.0, 4400.0, 3600.0, 4050.0, 2850.0, 3950.0, 3350.0, 4100.0, 3050.0, 4450.0, 3600.0, 3900.0, 3550.0, 4150.0, 3700.0, 4250.0, 3700.0, 3900.0, 3550.0, 4000.0, 3200.0, 4700.0, 3800.0, 4200.0, 3350.0, 3550.0, 3800.0, 3500.0, 3950.0, 3600.0, 3550.0, 4300.0, 3400.0, 4450.0, 3300.0, 4300.0, 3700.0, 4350.0, 2900.0, 4100.0, 3725.0, 4725.0, 3075.0, 4250.0, 2925.0, 3550.0, 3750.0, 3900.0, 3175.0, 4775.0, 3825.0, 4600.0, 3200.0, 4275.0, 3900.0, 4075.0, 2900.0, 3775.0, 3350.0, 3325.0, 3150.0, 3500.0, 3450.0, 3875.0, 3050.0, 4000.0, 3275.0, 4300.0, 3050.0, 4000.0, 3325.0, 3500.0, 3500.0, 4475.0, 3425.0, 3900.0, 3175.0, 3975.0, 3400.0, 4250.0, 3400.0, 3475.0, 3050.0, 3725.0, 3000.0, 3650.0, 4250.0, 3475.0, 3450.0, 3750.0, 3700.0, 4000.0\n", "2 46.1, 50.0, 48.7, 50.0, 47.6, 46.5, 45.4, 46.7, 43.3, 46.8, 40.9, 49.0, 45.5, 48.4, 45.8, 49.3, 42.0, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1, 47.8, 48.2, 50.0, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4, 42.6, 44.4, 44.0, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6, 45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.0, 43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 44.9, 50.8, 43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9, 13.2, 16.3, 14.1, 15.2, 14.5, 13.5, 14.6, 15.3, 13.4, 15.4, 13.7, 16.1, 13.7, 14.6, 14.6, 15.7, 13.5, 15.2, 14.5, 15.1, 14.3, 14.5, 14.5, 15.8, 13.1, 15.1, 15.0, 14.3, 15.3, 15.3, 14.2, 14.5, 17.0, 14.8, 16.3, 13.7, 17.3, 13.6, 15.7, 13.7, 16.0, 13.7, 15.0, 15.9, 13.9, 13.9, 15.9, 13.3, 15.8, 14.2, 14.1, 14.4, 15.0, 14.4, 15.4, 13.9, 15.0, 14.5, 15.3, 13.8, 14.9, 13.9, 15.7, 14.2, 16.8, 16.2, 14.2, 15.0, 15.0, 15.6, 15.6, 14.8, 15.0, 16.0, 14.2, 16.3, 13.8, 16.4, 14.5, 15.6, 14.6, 15.9, 13.8, 17.3, 14.4, 14.2, 14.0, 17.0, 15.0, 17.1, 14.5, 16.1, 14.7, 15.7, 15.8, 14.6, 14.4, 16.5, 15.0, 17.0, 15.5, 15.0, 16.1, 14.7, 15.8, 14.0, 15.1, 15.2, 15.9, 15.2, 16.3, 14.1, 16.0, 16.2, 13.7, 14.3, 15.7, 14.8, 16.1, 4500.0, 5700.0, 4450.0, 5700.0, 5400.0, 4550.0, 4800.0, 5200.0, 4400.0, 5150.0, 4650.0, 5550.0, 4650.0, 5850.0, 4200.0, 5850.0, 4150.0, 6300.0, 4800.0, 5350.0, 5700.0, 5000.0, 4400.0, 5050.0, 5000.0, 5100.0, 5650.0, 4600.0, 5550.0, 5250.0, 4700.0, 5050.0, 6050.0, 5150.0, 5400.0, 4950.0, 5250.0, 4350.0, 5350.0, 3950.0, 5700.0, 4300.0, 4750.0, 5550.0, 4900.0, 4200.0, 5400.0, 5100.0, 5300.0, 4850.0, 5300.0, 4400.0, 5000.0, 4900.0, 5050.0, 4300.0, 5000.0, 4450.0, 5550.0, 4200.0, 5300.0, 4400.0, 5650.0, 4700.0, 5700.0, 5800.0, 4700.0, 5550.0, 4750.0, 5000.0, 5100.0, 5200.0, 4700.0, 5800.0, 4600.0, 6000.0, 4750.0, 5950.0, 4625.0, 5450.0, 4725.0, 5350.0, 4750.0, 5600.0, 4600.0, 5300.0, 4875.0, 5550.0, 4950.0, 5400.0, 4750.0, 5650.0, 4850.0, 5200.0, 4925.0, 4875.0, 4625.0, 5250.0, 4850.0, 5600.0, 4975.0, 5500.0, 5500.0, 4700.0, 5500.0, 4575.0, 5500.0, 5000.0, 5950.0, 4650.0, 5500.0, 4375.0, 5850.0, 6000.0, 4925.0, 4850.0, 5750.0, 5200.0, 5400.0 \n", "3 46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 50.8, 50.2, 17.9, 19.5, 19.2, 18.7, 19.8, 17.8, 18.2, 18.2, 18.9, 19.9, 17.8, 20.3, 17.3, 18.1, 17.1, 19.6, 20.0, 17.8, 18.6, 18.2, 17.3, 17.5, 16.6, 19.4, 17.9, 19.0, 18.4, 19.0, 17.8, 20.0, 16.6, 20.8, 16.7, 18.8, 18.6, 16.8, 18.3, 20.7, 16.6, 19.9, 19.5, 17.5, 19.1, 17.0, 17.9, 18.5, 17.9, 19.6, 18.7, 17.3, 16.4, 19.0, 17.3, 19.7, 17.3, 18.8, 16.6, 19.9, 18.8, 19.4, 19.5, 16.5, 17.0, 19.8, 18.1, 18.2, 19.0, 18.7, 3500.0, 3900.0, 3650.0, 3525.0, 3725.0, 3950.0, 3250.0, 3750.0, 4150.0, 3700.0, 3800.0, 3775.0, 3700.0, 4050.0, 3575.0, 4050.0, 3300.0, 3700.0, 3450.0, 4400.0, 3600.0, 3400.0, 2900.0, 3800.0, 3300.0, 4150.0, 3400.0, 3800.0, 3700.0, 4550.0, 3200.0, 4300.0, 3350.0, 4100.0, 3600.0, 3900.0, 3850.0, 4800.0, 2700.0, 4500.0, 3950.0, 3650.0, 3550.0, 3500.0, 3675.0, 4450.0, 3400.0, 4300.0, 3250.0, 3675.0, 3325.0, 3950.0, 3600.0, 4050.0, 3350.0, 3450.0, 3250.0, 4050.0, 3800.0, 3525.0, 3950.0, 3650.0, 3650.0, 4000.0, 3400.0, 3775.0, 4100.0, 3775.0 \n", " num_species\n", "1 146 \n", "2 119 \n", "3 68 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tb %>% \n", " rowwise() %>% \n", " mutate(num_species = nrow(data))" ] }, { "cell_type": "code", "execution_count": 185, "id": "8ecd5c7f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A rowwise_df: 3 × 3
speciesdatacorr_coef
<fct><list><dbl>
Adelie 39.1, 39.5, 40.3, 36.7, 39.3, 38.9, 39.2, 41.1, 38.6, 34.6, 36.6, 38.7, 42.5, 34.4, 46.0, 37.8, 37.7, 35.9, 38.2, 38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9, 36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36.0, 44.1, 37.0, 39.6, 41.1, 36.0, 42.3, 39.6, 40.1, 35.0, 42.0, 34.5, 41.4, 39.0, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4, 41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5, 42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3, 41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34.0, 39.6, 36.2, 40.8, 38.1, 40.3, 33.1, 43.2, 35.0, 41.0, 37.7, 37.8, 37.9, 39.7, 38.6, 38.2, 38.1, 43.2, 38.1, 45.6, 39.7, 42.2, 39.6, 42.7, 38.6, 37.3, 35.7, 41.1, 36.2, 37.7, 40.2, 41.4, 35.2, 40.6, 38.8, 41.5, 39.0, 44.1, 38.5, 43.1, 36.8, 37.5, 38.1, 41.1, 35.6, 40.2, 37.0, 39.7, 40.2, 40.6, 32.1, 40.7, 37.3, 39.0, 39.2, 36.6, 36.0, 37.8, 36.0, 41.5, 18.7, 17.4, 18.0, 19.3, 20.6, 17.8, 19.6, 17.6, 21.2, 21.1, 17.8, 19.0, 20.7, 18.4, 21.5, 18.3, 18.7, 19.2, 18.1, 17.2, 18.9, 18.6, 17.9, 18.6, 18.9, 16.7, 18.1, 17.8, 18.9, 17.0, 21.1, 20.0, 18.5, 19.3, 19.1, 18.0, 18.4, 18.5, 19.7, 16.9, 18.8, 19.0, 17.9, 21.2, 17.7, 18.9, 17.9, 19.5, 18.1, 18.6, 17.5, 18.8, 16.6, 19.1, 16.9, 21.1, 17.0, 18.2, 17.1, 18.0, 16.2, 19.1, 16.6, 19.4, 19.0, 18.4, 17.2, 18.9, 17.5, 18.5, 16.8, 19.4, 16.1, 19.1, 17.2, 17.6, 18.8, 19.4, 17.8, 20.3, 19.5, 18.6, 19.2, 18.8, 18.0, 18.1, 17.1, 18.1, 17.3, 18.9, 18.6, 18.5, 16.1, 18.5, 17.9, 20.0, 16.0, 20.0, 18.6, 18.9, 17.2, 20.0, 17.0, 19.0, 16.5, 20.3, 17.7, 19.5, 20.7, 18.3, 17.0, 20.5, 17.0, 18.6, 17.2, 19.8, 17.0, 18.5, 15.9, 19.0, 17.6, 18.3, 17.1, 18.0, 17.9, 19.2, 18.5, 18.5, 17.6, 17.5, 17.5, 20.1, 16.5, 17.9, 17.1, 17.2, 15.5, 17.0, 16.8, 18.7, 18.6, 18.4, 17.8, 18.1, 17.1, 18.5, 3750.0, 3800.0, 3250.0, 3450.0, 3650.0, 3625.0, 4675.0, 3200.0, 3800.0, 4400.0, 3700.0, 3450.0, 4500.0, 3325.0, 4200.0, 3400.0, 3600.0, 3800.0, 3950.0, 3800.0, 3800.0, 3550.0, 3200.0, 3150.0, 3950.0, 3250.0, 3900.0, 3300.0, 3900.0, 3325.0, 4150.0, 3950.0, 3550.0, 3300.0, 4650.0, 3150.0, 3900.0, 3100.0, 4400.0, 3000.0, 4600.0, 3425.0, 3450.0, 4150.0, 3500.0, 4300.0, 3450.0, 4050.0, 2900.0, 3700.0, 3550.0, 3800.0, 2850.0, 3750.0, 3150.0, 4400.0, 3600.0, 4050.0, 2850.0, 3950.0, 3350.0, 4100.0, 3050.0, 4450.0, 3600.0, 3900.0, 3550.0, 4150.0, 3700.0, 4250.0, 3700.0, 3900.0, 3550.0, 4000.0, 3200.0, 4700.0, 3800.0, 4200.0, 3350.0, 3550.0, 3800.0, 3500.0, 3950.0, 3600.0, 3550.0, 4300.0, 3400.0, 4450.0, 3300.0, 4300.0, 3700.0, 4350.0, 2900.0, 4100.0, 3725.0, 4725.0, 3075.0, 4250.0, 2925.0, 3550.0, 3750.0, 3900.0, 3175.0, 4775.0, 3825.0, 4600.0, 3200.0, 4275.0, 3900.0, 4075.0, 2900.0, 3775.0, 3350.0, 3325.0, 3150.0, 3500.0, 3450.0, 3875.0, 3050.0, 4000.0, 3275.0, 4300.0, 3050.0, 4000.0, 3325.0, 3500.0, 3500.0, 4475.0, 3425.0, 3900.0, 3175.0, 3975.0, 3400.0, 4250.0, 3400.0, 3475.0, 3050.0, 3725.0, 3000.0, 3650.0, 4250.0, 3475.0, 3450.0, 3750.0, 3700.0, 4000.00.3858132
Gentoo 46.1, 50.0, 48.7, 50.0, 47.6, 46.5, 45.4, 46.7, 43.3, 46.8, 40.9, 49.0, 45.5, 48.4, 45.8, 49.3, 42.0, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1, 47.8, 48.2, 50.0, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4, 42.6, 44.4, 44.0, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6, 45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.0, 43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 44.9, 50.8, 43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9, 13.2, 16.3, 14.1, 15.2, 14.5, 13.5, 14.6, 15.3, 13.4, 15.4, 13.7, 16.1, 13.7, 14.6, 14.6, 15.7, 13.5, 15.2, 14.5, 15.1, 14.3, 14.5, 14.5, 15.8, 13.1, 15.1, 15.0, 14.3, 15.3, 15.3, 14.2, 14.5, 17.0, 14.8, 16.3, 13.7, 17.3, 13.6, 15.7, 13.7, 16.0, 13.7, 15.0, 15.9, 13.9, 13.9, 15.9, 13.3, 15.8, 14.2, 14.1, 14.4, 15.0, 14.4, 15.4, 13.9, 15.0, 14.5, 15.3, 13.8, 14.9, 13.9, 15.7, 14.2, 16.8, 16.2, 14.2, 15.0, 15.0, 15.6, 15.6, 14.8, 15.0, 16.0, 14.2, 16.3, 13.8, 16.4, 14.5, 15.6, 14.6, 15.9, 13.8, 17.3, 14.4, 14.2, 14.0, 17.0, 15.0, 17.1, 14.5, 16.1, 14.7, 15.7, 15.8, 14.6, 14.4, 16.5, 15.0, 17.0, 15.5, 15.0, 16.1, 14.7, 15.8, 14.0, 15.1, 15.2, 15.9, 15.2, 16.3, 14.1, 16.0, 16.2, 13.7, 14.3, 15.7, 14.8, 16.1, 4500.0, 5700.0, 4450.0, 5700.0, 5400.0, 4550.0, 4800.0, 5200.0, 4400.0, 5150.0, 4650.0, 5550.0, 4650.0, 5850.0, 4200.0, 5850.0, 4150.0, 6300.0, 4800.0, 5350.0, 5700.0, 5000.0, 4400.0, 5050.0, 5000.0, 5100.0, 5650.0, 4600.0, 5550.0, 5250.0, 4700.0, 5050.0, 6050.0, 5150.0, 5400.0, 4950.0, 5250.0, 4350.0, 5350.0, 3950.0, 5700.0, 4300.0, 4750.0, 5550.0, 4900.0, 4200.0, 5400.0, 5100.0, 5300.0, 4850.0, 5300.0, 4400.0, 5000.0, 4900.0, 5050.0, 4300.0, 5000.0, 4450.0, 5550.0, 4200.0, 5300.0, 4400.0, 5650.0, 4700.0, 5700.0, 5800.0, 4700.0, 5550.0, 4750.0, 5000.0, 5100.0, 5200.0, 4700.0, 5800.0, 4600.0, 6000.0, 4750.0, 5950.0, 4625.0, 5450.0, 4725.0, 5350.0, 4750.0, 5600.0, 4600.0, 5300.0, 4875.0, 5550.0, 4950.0, 5400.0, 4750.0, 5650.0, 4850.0, 5200.0, 4925.0, 4875.0, 4625.0, 5250.0, 4850.0, 5600.0, 4975.0, 5500.0, 5500.0, 4700.0, 5500.0, 4575.0, 5500.0, 5000.0, 5950.0, 4650.0, 5500.0, 4375.0, 5850.0, 6000.0, 4925.0, 4850.0, 5750.0, 5200.0, 5400.00.6540233
Chinstrap46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 50.8, 50.2, 17.9, 19.5, 19.2, 18.7, 19.8, 17.8, 18.2, 18.2, 18.9, 19.9, 17.8, 20.3, 17.3, 18.1, 17.1, 19.6, 20.0, 17.8, 18.6, 18.2, 17.3, 17.5, 16.6, 19.4, 17.9, 19.0, 18.4, 19.0, 17.8, 20.0, 16.6, 20.8, 16.7, 18.8, 18.6, 16.8, 18.3, 20.7, 16.6, 19.9, 19.5, 17.5, 19.1, 17.0, 17.9, 18.5, 17.9, 19.6, 18.7, 17.3, 16.4, 19.0, 17.3, 19.7, 17.3, 18.8, 16.6, 19.9, 18.8, 19.4, 19.5, 16.5, 17.0, 19.8, 18.1, 18.2, 19.0, 18.7, 3500.0, 3900.0, 3650.0, 3525.0, 3725.0, 3950.0, 3250.0, 3750.0, 4150.0, 3700.0, 3800.0, 3775.0, 3700.0, 4050.0, 3575.0, 4050.0, 3300.0, 3700.0, 3450.0, 4400.0, 3600.0, 3400.0, 2900.0, 3800.0, 3300.0, 4150.0, 3400.0, 3800.0, 3700.0, 4550.0, 3200.0, 4300.0, 3350.0, 4100.0, 3600.0, 3900.0, 3850.0, 4800.0, 2700.0, 4500.0, 3950.0, 3650.0, 3550.0, 3500.0, 3675.0, 4450.0, 3400.0, 4300.0, 3250.0, 3675.0, 3325.0, 3950.0, 3600.0, 4050.0, 3350.0, 3450.0, 3250.0, 4050.0, 3800.0, 3525.0, 3950.0, 3650.0, 3650.0, 4000.0, 3400.0, 3775.0, 4100.0, 3775.00.6535362
\n" ], "text/latex": [ "A rowwise\\_df: 3 × 3\n", "\\begin{tabular}{lll}\n", " species & data & corr\\_coef\\\\\n", " & & \\\\\n", "\\hline\n", "\t Adelie & 39.1, 39.5, 40.3, 36.7, 39.3, 38.9, 39.2, 41.1, 38.6, 34.6, 36.6, 38.7, 42.5, 34.4, 46.0, 37.8, 37.7, 35.9, 38.2, 38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9, 36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36.0, 44.1, 37.0, 39.6, 41.1, 36.0, 42.3, 39.6, 40.1, 35.0, 42.0, 34.5, 41.4, 39.0, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4, 41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5, 42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3, 41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34.0, 39.6, 36.2, 40.8, 38.1, 40.3, 33.1, 43.2, 35.0, 41.0, 37.7, 37.8, 37.9, 39.7, 38.6, 38.2, 38.1, 43.2, 38.1, 45.6, 39.7, 42.2, 39.6, 42.7, 38.6, 37.3, 35.7, 41.1, 36.2, 37.7, 40.2, 41.4, 35.2, 40.6, 38.8, 41.5, 39.0, 44.1, 38.5, 43.1, 36.8, 37.5, 38.1, 41.1, 35.6, 40.2, 37.0, 39.7, 40.2, 40.6, 32.1, 40.7, 37.3, 39.0, 39.2, 36.6, 36.0, 37.8, 36.0, 41.5, 18.7, 17.4, 18.0, 19.3, 20.6, 17.8, 19.6, 17.6, 21.2, 21.1, 17.8, 19.0, 20.7, 18.4, 21.5, 18.3, 18.7, 19.2, 18.1, 17.2, 18.9, 18.6, 17.9, 18.6, 18.9, 16.7, 18.1, 17.8, 18.9, 17.0, 21.1, 20.0, 18.5, 19.3, 19.1, 18.0, 18.4, 18.5, 19.7, 16.9, 18.8, 19.0, 17.9, 21.2, 17.7, 18.9, 17.9, 19.5, 18.1, 18.6, 17.5, 18.8, 16.6, 19.1, 16.9, 21.1, 17.0, 18.2, 17.1, 18.0, 16.2, 19.1, 16.6, 19.4, 19.0, 18.4, 17.2, 18.9, 17.5, 18.5, 16.8, 19.4, 16.1, 19.1, 17.2, 17.6, 18.8, 19.4, 17.8, 20.3, 19.5, 18.6, 19.2, 18.8, 18.0, 18.1, 17.1, 18.1, 17.3, 18.9, 18.6, 18.5, 16.1, 18.5, 17.9, 20.0, 16.0, 20.0, 18.6, 18.9, 17.2, 20.0, 17.0, 19.0, 16.5, 20.3, 17.7, 19.5, 20.7, 18.3, 17.0, 20.5, 17.0, 18.6, 17.2, 19.8, 17.0, 18.5, 15.9, 19.0, 17.6, 18.3, 17.1, 18.0, 17.9, 19.2, 18.5, 18.5, 17.6, 17.5, 17.5, 20.1, 16.5, 17.9, 17.1, 17.2, 15.5, 17.0, 16.8, 18.7, 18.6, 18.4, 17.8, 18.1, 17.1, 18.5, 3750.0, 3800.0, 3250.0, 3450.0, 3650.0, 3625.0, 4675.0, 3200.0, 3800.0, 4400.0, 3700.0, 3450.0, 4500.0, 3325.0, 4200.0, 3400.0, 3600.0, 3800.0, 3950.0, 3800.0, 3800.0, 3550.0, 3200.0, 3150.0, 3950.0, 3250.0, 3900.0, 3300.0, 3900.0, 3325.0, 4150.0, 3950.0, 3550.0, 3300.0, 4650.0, 3150.0, 3900.0, 3100.0, 4400.0, 3000.0, 4600.0, 3425.0, 3450.0, 4150.0, 3500.0, 4300.0, 3450.0, 4050.0, 2900.0, 3700.0, 3550.0, 3800.0, 2850.0, 3750.0, 3150.0, 4400.0, 3600.0, 4050.0, 2850.0, 3950.0, 3350.0, 4100.0, 3050.0, 4450.0, 3600.0, 3900.0, 3550.0, 4150.0, 3700.0, 4250.0, 3700.0, 3900.0, 3550.0, 4000.0, 3200.0, 4700.0, 3800.0, 4200.0, 3350.0, 3550.0, 3800.0, 3500.0, 3950.0, 3600.0, 3550.0, 4300.0, 3400.0, 4450.0, 3300.0, 4300.0, 3700.0, 4350.0, 2900.0, 4100.0, 3725.0, 4725.0, 3075.0, 4250.0, 2925.0, 3550.0, 3750.0, 3900.0, 3175.0, 4775.0, 3825.0, 4600.0, 3200.0, 4275.0, 3900.0, 4075.0, 2900.0, 3775.0, 3350.0, 3325.0, 3150.0, 3500.0, 3450.0, 3875.0, 3050.0, 4000.0, 3275.0, 4300.0, 3050.0, 4000.0, 3325.0, 3500.0, 3500.0, 4475.0, 3425.0, 3900.0, 3175.0, 3975.0, 3400.0, 4250.0, 3400.0, 3475.0, 3050.0, 3725.0, 3000.0, 3650.0, 4250.0, 3475.0, 3450.0, 3750.0, 3700.0, 4000.0 & 0.3858132\\\\\n", "\t Gentoo & 46.1, 50.0, 48.7, 50.0, 47.6, 46.5, 45.4, 46.7, 43.3, 46.8, 40.9, 49.0, 45.5, 48.4, 45.8, 49.3, 42.0, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1, 47.8, 48.2, 50.0, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4, 42.6, 44.4, 44.0, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6, 45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.0, 43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 44.9, 50.8, 43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9, 13.2, 16.3, 14.1, 15.2, 14.5, 13.5, 14.6, 15.3, 13.4, 15.4, 13.7, 16.1, 13.7, 14.6, 14.6, 15.7, 13.5, 15.2, 14.5, 15.1, 14.3, 14.5, 14.5, 15.8, 13.1, 15.1, 15.0, 14.3, 15.3, 15.3, 14.2, 14.5, 17.0, 14.8, 16.3, 13.7, 17.3, 13.6, 15.7, 13.7, 16.0, 13.7, 15.0, 15.9, 13.9, 13.9, 15.9, 13.3, 15.8, 14.2, 14.1, 14.4, 15.0, 14.4, 15.4, 13.9, 15.0, 14.5, 15.3, 13.8, 14.9, 13.9, 15.7, 14.2, 16.8, 16.2, 14.2, 15.0, 15.0, 15.6, 15.6, 14.8, 15.0, 16.0, 14.2, 16.3, 13.8, 16.4, 14.5, 15.6, 14.6, 15.9, 13.8, 17.3, 14.4, 14.2, 14.0, 17.0, 15.0, 17.1, 14.5, 16.1, 14.7, 15.7, 15.8, 14.6, 14.4, 16.5, 15.0, 17.0, 15.5, 15.0, 16.1, 14.7, 15.8, 14.0, 15.1, 15.2, 15.9, 15.2, 16.3, 14.1, 16.0, 16.2, 13.7, 14.3, 15.7, 14.8, 16.1, 4500.0, 5700.0, 4450.0, 5700.0, 5400.0, 4550.0, 4800.0, 5200.0, 4400.0, 5150.0, 4650.0, 5550.0, 4650.0, 5850.0, 4200.0, 5850.0, 4150.0, 6300.0, 4800.0, 5350.0, 5700.0, 5000.0, 4400.0, 5050.0, 5000.0, 5100.0, 5650.0, 4600.0, 5550.0, 5250.0, 4700.0, 5050.0, 6050.0, 5150.0, 5400.0, 4950.0, 5250.0, 4350.0, 5350.0, 3950.0, 5700.0, 4300.0, 4750.0, 5550.0, 4900.0, 4200.0, 5400.0, 5100.0, 5300.0, 4850.0, 5300.0, 4400.0, 5000.0, 4900.0, 5050.0, 4300.0, 5000.0, 4450.0, 5550.0, 4200.0, 5300.0, 4400.0, 5650.0, 4700.0, 5700.0, 5800.0, 4700.0, 5550.0, 4750.0, 5000.0, 5100.0, 5200.0, 4700.0, 5800.0, 4600.0, 6000.0, 4750.0, 5950.0, 4625.0, 5450.0, 4725.0, 5350.0, 4750.0, 5600.0, 4600.0, 5300.0, 4875.0, 5550.0, 4950.0, 5400.0, 4750.0, 5650.0, 4850.0, 5200.0, 4925.0, 4875.0, 4625.0, 5250.0, 4850.0, 5600.0, 4975.0, 5500.0, 5500.0, 4700.0, 5500.0, 4575.0, 5500.0, 5000.0, 5950.0, 4650.0, 5500.0, 4375.0, 5850.0, 6000.0, 4925.0, 4850.0, 5750.0, 5200.0, 5400.0 & 0.6540233\\\\\n", "\t Chinstrap & 46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 50.8, 50.2, 17.9, 19.5, 19.2, 18.7, 19.8, 17.8, 18.2, 18.2, 18.9, 19.9, 17.8, 20.3, 17.3, 18.1, 17.1, 19.6, 20.0, 17.8, 18.6, 18.2, 17.3, 17.5, 16.6, 19.4, 17.9, 19.0, 18.4, 19.0, 17.8, 20.0, 16.6, 20.8, 16.7, 18.8, 18.6, 16.8, 18.3, 20.7, 16.6, 19.9, 19.5, 17.5, 19.1, 17.0, 17.9, 18.5, 17.9, 19.6, 18.7, 17.3, 16.4, 19.0, 17.3, 19.7, 17.3, 18.8, 16.6, 19.9, 18.8, 19.4, 19.5, 16.5, 17.0, 19.8, 18.1, 18.2, 19.0, 18.7, 3500.0, 3900.0, 3650.0, 3525.0, 3725.0, 3950.0, 3250.0, 3750.0, 4150.0, 3700.0, 3800.0, 3775.0, 3700.0, 4050.0, 3575.0, 4050.0, 3300.0, 3700.0, 3450.0, 4400.0, 3600.0, 3400.0, 2900.0, 3800.0, 3300.0, 4150.0, 3400.0, 3800.0, 3700.0, 4550.0, 3200.0, 4300.0, 3350.0, 4100.0, 3600.0, 3900.0, 3850.0, 4800.0, 2700.0, 4500.0, 3950.0, 3650.0, 3550.0, 3500.0, 3675.0, 4450.0, 3400.0, 4300.0, 3250.0, 3675.0, 3325.0, 3950.0, 3600.0, 4050.0, 3350.0, 3450.0, 3250.0, 4050.0, 3800.0, 3525.0, 3950.0, 3650.0, 3650.0, 4000.0, 3400.0, 3775.0, 4100.0, 3775.0 & 0.6535362\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A rowwise_df: 3 × 3\n", "\n", "| species <fct> | data <list> | corr_coef <dbl> |\n", "|---|---|---|\n", "| Adelie | 39.1, 39.5, 40.3, 36.7, 39.3, 38.9, 39.2, 41.1, 38.6, 34.6, 36.6, 38.7, 42.5, 34.4, 46.0, 37.8, 37.7, 35.9, 38.2, 38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9, 36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36.0, 44.1, 37.0, 39.6, 41.1, 36.0, 42.3, 39.6, 40.1, 35.0, 42.0, 34.5, 41.4, 39.0, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4, 41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5, 42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3, 41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34.0, 39.6, 36.2, 40.8, 38.1, 40.3, 33.1, 43.2, 35.0, 41.0, 37.7, 37.8, 37.9, 39.7, 38.6, 38.2, 38.1, 43.2, 38.1, 45.6, 39.7, 42.2, 39.6, 42.7, 38.6, 37.3, 35.7, 41.1, 36.2, 37.7, 40.2, 41.4, 35.2, 40.6, 38.8, 41.5, 39.0, 44.1, 38.5, 43.1, 36.8, 37.5, 38.1, 41.1, 35.6, 40.2, 37.0, 39.7, 40.2, 40.6, 32.1, 40.7, 37.3, 39.0, 39.2, 36.6, 36.0, 37.8, 36.0, 41.5, 18.7, 17.4, 18.0, 19.3, 20.6, 17.8, 19.6, 17.6, 21.2, 21.1, 17.8, 19.0, 20.7, 18.4, 21.5, 18.3, 18.7, 19.2, 18.1, 17.2, 18.9, 18.6, 17.9, 18.6, 18.9, 16.7, 18.1, 17.8, 18.9, 17.0, 21.1, 20.0, 18.5, 19.3, 19.1, 18.0, 18.4, 18.5, 19.7, 16.9, 18.8, 19.0, 17.9, 21.2, 17.7, 18.9, 17.9, 19.5, 18.1, 18.6, 17.5, 18.8, 16.6, 19.1, 16.9, 21.1, 17.0, 18.2, 17.1, 18.0, 16.2, 19.1, 16.6, 19.4, 19.0, 18.4, 17.2, 18.9, 17.5, 18.5, 16.8, 19.4, 16.1, 19.1, 17.2, 17.6, 18.8, 19.4, 17.8, 20.3, 19.5, 18.6, 19.2, 18.8, 18.0, 18.1, 17.1, 18.1, 17.3, 18.9, 18.6, 18.5, 16.1, 18.5, 17.9, 20.0, 16.0, 20.0, 18.6, 18.9, 17.2, 20.0, 17.0, 19.0, 16.5, 20.3, 17.7, 19.5, 20.7, 18.3, 17.0, 20.5, 17.0, 18.6, 17.2, 19.8, 17.0, 18.5, 15.9, 19.0, 17.6, 18.3, 17.1, 18.0, 17.9, 19.2, 18.5, 18.5, 17.6, 17.5, 17.5, 20.1, 16.5, 17.9, 17.1, 17.2, 15.5, 17.0, 16.8, 18.7, 18.6, 18.4, 17.8, 18.1, 17.1, 18.5, 3750.0, 3800.0, 3250.0, 3450.0, 3650.0, 3625.0, 4675.0, 3200.0, 3800.0, 4400.0, 3700.0, 3450.0, 4500.0, 3325.0, 4200.0, 3400.0, 3600.0, 3800.0, 3950.0, 3800.0, 3800.0, 3550.0, 3200.0, 3150.0, 3950.0, 3250.0, 3900.0, 3300.0, 3900.0, 3325.0, 4150.0, 3950.0, 3550.0, 3300.0, 4650.0, 3150.0, 3900.0, 3100.0, 4400.0, 3000.0, 4600.0, 3425.0, 3450.0, 4150.0, 3500.0, 4300.0, 3450.0, 4050.0, 2900.0, 3700.0, 3550.0, 3800.0, 2850.0, 3750.0, 3150.0, 4400.0, 3600.0, 4050.0, 2850.0, 3950.0, 3350.0, 4100.0, 3050.0, 4450.0, 3600.0, 3900.0, 3550.0, 4150.0, 3700.0, 4250.0, 3700.0, 3900.0, 3550.0, 4000.0, 3200.0, 4700.0, 3800.0, 4200.0, 3350.0, 3550.0, 3800.0, 3500.0, 3950.0, 3600.0, 3550.0, 4300.0, 3400.0, 4450.0, 3300.0, 4300.0, 3700.0, 4350.0, 2900.0, 4100.0, 3725.0, 4725.0, 3075.0, 4250.0, 2925.0, 3550.0, 3750.0, 3900.0, 3175.0, 4775.0, 3825.0, 4600.0, 3200.0, 4275.0, 3900.0, 4075.0, 2900.0, 3775.0, 3350.0, 3325.0, 3150.0, 3500.0, 3450.0, 3875.0, 3050.0, 4000.0, 3275.0, 4300.0, 3050.0, 4000.0, 3325.0, 3500.0, 3500.0, 4475.0, 3425.0, 3900.0, 3175.0, 3975.0, 3400.0, 4250.0, 3400.0, 3475.0, 3050.0, 3725.0, 3000.0, 3650.0, 4250.0, 3475.0, 3450.0, 3750.0, 3700.0, 4000.0 | 0.3858132 |\n", "| Gentoo | 46.1, 50.0, 48.7, 50.0, 47.6, 46.5, 45.4, 46.7, 43.3, 46.8, 40.9, 49.0, 45.5, 48.4, 45.8, 49.3, 42.0, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1, 47.8, 48.2, 50.0, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4, 42.6, 44.4, 44.0, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6, 45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.0, 43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 44.9, 50.8, 43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9, 13.2, 16.3, 14.1, 15.2, 14.5, 13.5, 14.6, 15.3, 13.4, 15.4, 13.7, 16.1, 13.7, 14.6, 14.6, 15.7, 13.5, 15.2, 14.5, 15.1, 14.3, 14.5, 14.5, 15.8, 13.1, 15.1, 15.0, 14.3, 15.3, 15.3, 14.2, 14.5, 17.0, 14.8, 16.3, 13.7, 17.3, 13.6, 15.7, 13.7, 16.0, 13.7, 15.0, 15.9, 13.9, 13.9, 15.9, 13.3, 15.8, 14.2, 14.1, 14.4, 15.0, 14.4, 15.4, 13.9, 15.0, 14.5, 15.3, 13.8, 14.9, 13.9, 15.7, 14.2, 16.8, 16.2, 14.2, 15.0, 15.0, 15.6, 15.6, 14.8, 15.0, 16.0, 14.2, 16.3, 13.8, 16.4, 14.5, 15.6, 14.6, 15.9, 13.8, 17.3, 14.4, 14.2, 14.0, 17.0, 15.0, 17.1, 14.5, 16.1, 14.7, 15.7, 15.8, 14.6, 14.4, 16.5, 15.0, 17.0, 15.5, 15.0, 16.1, 14.7, 15.8, 14.0, 15.1, 15.2, 15.9, 15.2, 16.3, 14.1, 16.0, 16.2, 13.7, 14.3, 15.7, 14.8, 16.1, 4500.0, 5700.0, 4450.0, 5700.0, 5400.0, 4550.0, 4800.0, 5200.0, 4400.0, 5150.0, 4650.0, 5550.0, 4650.0, 5850.0, 4200.0, 5850.0, 4150.0, 6300.0, 4800.0, 5350.0, 5700.0, 5000.0, 4400.0, 5050.0, 5000.0, 5100.0, 5650.0, 4600.0, 5550.0, 5250.0, 4700.0, 5050.0, 6050.0, 5150.0, 5400.0, 4950.0, 5250.0, 4350.0, 5350.0, 3950.0, 5700.0, 4300.0, 4750.0, 5550.0, 4900.0, 4200.0, 5400.0, 5100.0, 5300.0, 4850.0, 5300.0, 4400.0, 5000.0, 4900.0, 5050.0, 4300.0, 5000.0, 4450.0, 5550.0, 4200.0, 5300.0, 4400.0, 5650.0, 4700.0, 5700.0, 5800.0, 4700.0, 5550.0, 4750.0, 5000.0, 5100.0, 5200.0, 4700.0, 5800.0, 4600.0, 6000.0, 4750.0, 5950.0, 4625.0, 5450.0, 4725.0, 5350.0, 4750.0, 5600.0, 4600.0, 5300.0, 4875.0, 5550.0, 4950.0, 5400.0, 4750.0, 5650.0, 4850.0, 5200.0, 4925.0, 4875.0, 4625.0, 5250.0, 4850.0, 5600.0, 4975.0, 5500.0, 5500.0, 4700.0, 5500.0, 4575.0, 5500.0, 5000.0, 5950.0, 4650.0, 5500.0, 4375.0, 5850.0, 6000.0, 4925.0, 4850.0, 5750.0, 5200.0, 5400.0 | 0.6540233 |\n", "| Chinstrap | 46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 50.8, 50.2, 17.9, 19.5, 19.2, 18.7, 19.8, 17.8, 18.2, 18.2, 18.9, 19.9, 17.8, 20.3, 17.3, 18.1, 17.1, 19.6, 20.0, 17.8, 18.6, 18.2, 17.3, 17.5, 16.6, 19.4, 17.9, 19.0, 18.4, 19.0, 17.8, 20.0, 16.6, 20.8, 16.7, 18.8, 18.6, 16.8, 18.3, 20.7, 16.6, 19.9, 19.5, 17.5, 19.1, 17.0, 17.9, 18.5, 17.9, 19.6, 18.7, 17.3, 16.4, 19.0, 17.3, 19.7, 17.3, 18.8, 16.6, 19.9, 18.8, 19.4, 19.5, 16.5, 17.0, 19.8, 18.1, 18.2, 19.0, 18.7, 3500.0, 3900.0, 3650.0, 3525.0, 3725.0, 3950.0, 3250.0, 3750.0, 4150.0, 3700.0, 3800.0, 3775.0, 3700.0, 4050.0, 3575.0, 4050.0, 3300.0, 3700.0, 3450.0, 4400.0, 3600.0, 3400.0, 2900.0, 3800.0, 3300.0, 4150.0, 3400.0, 3800.0, 3700.0, 4550.0, 3200.0, 4300.0, 3350.0, 4100.0, 3600.0, 3900.0, 3850.0, 4800.0, 2700.0, 4500.0, 3950.0, 3650.0, 3550.0, 3500.0, 3675.0, 4450.0, 3400.0, 4300.0, 3250.0, 3675.0, 3325.0, 3950.0, 3600.0, 4050.0, 3350.0, 3450.0, 3250.0, 4050.0, 3800.0, 3525.0, 3950.0, 3650.0, 3650.0, 4000.0, 3400.0, 3775.0, 4100.0, 3775.0 | 0.6535362 |\n", "\n" ], "text/plain": [ " species \n", "1 Adelie \n", "2 Gentoo \n", "3 Chinstrap\n", " data \n", "1 39.1, 39.5, 40.3, 36.7, 39.3, 38.9, 39.2, 41.1, 38.6, 34.6, 36.6, 38.7, 42.5, 34.4, 46.0, 37.8, 37.7, 35.9, 38.2, 38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9, 36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36.0, 44.1, 37.0, 39.6, 41.1, 36.0, 42.3, 39.6, 40.1, 35.0, 42.0, 34.5, 41.4, 39.0, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4, 41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5, 42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3, 41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34.0, 39.6, 36.2, 40.8, 38.1, 40.3, 33.1, 43.2, 35.0, 41.0, 37.7, 37.8, 37.9, 39.7, 38.6, 38.2, 38.1, 43.2, 38.1, 45.6, 39.7, 42.2, 39.6, 42.7, 38.6, 37.3, 35.7, 41.1, 36.2, 37.7, 40.2, 41.4, 35.2, 40.6, 38.8, 41.5, 39.0, 44.1, 38.5, 43.1, 36.8, 37.5, 38.1, 41.1, 35.6, 40.2, 37.0, 39.7, 40.2, 40.6, 32.1, 40.7, 37.3, 39.0, 39.2, 36.6, 36.0, 37.8, 36.0, 41.5, 18.7, 17.4, 18.0, 19.3, 20.6, 17.8, 19.6, 17.6, 21.2, 21.1, 17.8, 19.0, 20.7, 18.4, 21.5, 18.3, 18.7, 19.2, 18.1, 17.2, 18.9, 18.6, 17.9, 18.6, 18.9, 16.7, 18.1, 17.8, 18.9, 17.0, 21.1, 20.0, 18.5, 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4600.0, 3425.0, 3450.0, 4150.0, 3500.0, 4300.0, 3450.0, 4050.0, 2900.0, 3700.0, 3550.0, 3800.0, 2850.0, 3750.0, 3150.0, 4400.0, 3600.0, 4050.0, 2850.0, 3950.0, 3350.0, 4100.0, 3050.0, 4450.0, 3600.0, 3900.0, 3550.0, 4150.0, 3700.0, 4250.0, 3700.0, 3900.0, 3550.0, 4000.0, 3200.0, 4700.0, 3800.0, 4200.0, 3350.0, 3550.0, 3800.0, 3500.0, 3950.0, 3600.0, 3550.0, 4300.0, 3400.0, 4450.0, 3300.0, 4300.0, 3700.0, 4350.0, 2900.0, 4100.0, 3725.0, 4725.0, 3075.0, 4250.0, 2925.0, 3550.0, 3750.0, 3900.0, 3175.0, 4775.0, 3825.0, 4600.0, 3200.0, 4275.0, 3900.0, 4075.0, 2900.0, 3775.0, 3350.0, 3325.0, 3150.0, 3500.0, 3450.0, 3875.0, 3050.0, 4000.0, 3275.0, 4300.0, 3050.0, 4000.0, 3325.0, 3500.0, 3500.0, 4475.0, 3425.0, 3900.0, 3175.0, 3975.0, 3400.0, 4250.0, 3400.0, 3475.0, 3050.0, 3725.0, 3000.0, 3650.0, 4250.0, 3475.0, 3450.0, 3750.0, 3700.0, 4000.0\n", "2 46.1, 50.0, 48.7, 50.0, 47.6, 46.5, 45.4, 46.7, 43.3, 46.8, 40.9, 49.0, 45.5, 48.4, 45.8, 49.3, 42.0, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1, 47.8, 48.2, 50.0, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4, 42.6, 44.4, 44.0, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6, 45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.0, 43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 44.9, 50.8, 43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9, 13.2, 16.3, 14.1, 15.2, 14.5, 13.5, 14.6, 15.3, 13.4, 15.4, 13.7, 16.1, 13.7, 14.6, 14.6, 15.7, 13.5, 15.2, 14.5, 15.1, 14.3, 14.5, 14.5, 15.8, 13.1, 15.1, 15.0, 14.3, 15.3, 15.3, 14.2, 14.5, 17.0, 14.8, 16.3, 13.7, 17.3, 13.6, 15.7, 13.7, 16.0, 13.7, 15.0, 15.9, 13.9, 13.9, 15.9, 13.3, 15.8, 14.2, 14.1, 14.4, 15.0, 14.4, 15.4, 13.9, 15.0, 14.5, 15.3, 13.8, 14.9, 13.9, 15.7, 14.2, 16.8, 16.2, 14.2, 15.0, 15.0, 15.6, 15.6, 14.8, 15.0, 16.0, 14.2, 16.3, 13.8, 16.4, 14.5, 15.6, 14.6, 15.9, 13.8, 17.3, 14.4, 14.2, 14.0, 17.0, 15.0, 17.1, 14.5, 16.1, 14.7, 15.7, 15.8, 14.6, 14.4, 16.5, 15.0, 17.0, 15.5, 15.0, 16.1, 14.7, 15.8, 14.0, 15.1, 15.2, 15.9, 15.2, 16.3, 14.1, 16.0, 16.2, 13.7, 14.3, 15.7, 14.8, 16.1, 4500.0, 5700.0, 4450.0, 5700.0, 5400.0, 4550.0, 4800.0, 5200.0, 4400.0, 5150.0, 4650.0, 5550.0, 4650.0, 5850.0, 4200.0, 5850.0, 4150.0, 6300.0, 4800.0, 5350.0, 5700.0, 5000.0, 4400.0, 5050.0, 5000.0, 5100.0, 5650.0, 4600.0, 5550.0, 5250.0, 4700.0, 5050.0, 6050.0, 5150.0, 5400.0, 4950.0, 5250.0, 4350.0, 5350.0, 3950.0, 5700.0, 4300.0, 4750.0, 5550.0, 4900.0, 4200.0, 5400.0, 5100.0, 5300.0, 4850.0, 5300.0, 4400.0, 5000.0, 4900.0, 5050.0, 4300.0, 5000.0, 4450.0, 5550.0, 4200.0, 5300.0, 4400.0, 5650.0, 4700.0, 5700.0, 5800.0, 4700.0, 5550.0, 4750.0, 5000.0, 5100.0, 5200.0, 4700.0, 5800.0, 4600.0, 6000.0, 4750.0, 5950.0, 4625.0, 5450.0, 4725.0, 5350.0, 4750.0, 5600.0, 4600.0, 5300.0, 4875.0, 5550.0, 4950.0, 5400.0, 4750.0, 5650.0, 4850.0, 5200.0, 4925.0, 4875.0, 4625.0, 5250.0, 4850.0, 5600.0, 4975.0, 5500.0, 5500.0, 4700.0, 5500.0, 4575.0, 5500.0, 5000.0, 5950.0, 4650.0, 5500.0, 4375.0, 5850.0, 6000.0, 4925.0, 4850.0, 5750.0, 5200.0, 5400.0 \n", "3 46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 50.8, 50.2, 17.9, 19.5, 19.2, 18.7, 19.8, 17.8, 18.2, 18.2, 18.9, 19.9, 17.8, 20.3, 17.3, 18.1, 17.1, 19.6, 20.0, 17.8, 18.6, 18.2, 17.3, 17.5, 16.6, 19.4, 17.9, 19.0, 18.4, 19.0, 17.8, 20.0, 16.6, 20.8, 16.7, 18.8, 18.6, 16.8, 18.3, 20.7, 16.6, 19.9, 19.5, 17.5, 19.1, 17.0, 17.9, 18.5, 17.9, 19.6, 18.7, 17.3, 16.4, 19.0, 17.3, 19.7, 17.3, 18.8, 16.6, 19.9, 18.8, 19.4, 19.5, 16.5, 17.0, 19.8, 18.1, 18.2, 19.0, 18.7, 3500.0, 3900.0, 3650.0, 3525.0, 3725.0, 3950.0, 3250.0, 3750.0, 4150.0, 3700.0, 3800.0, 3775.0, 3700.0, 4050.0, 3575.0, 4050.0, 3300.0, 3700.0, 3450.0, 4400.0, 3600.0, 3400.0, 2900.0, 3800.0, 3300.0, 4150.0, 3400.0, 3800.0, 3700.0, 4550.0, 3200.0, 4300.0, 3350.0, 4100.0, 3600.0, 3900.0, 3850.0, 4800.0, 2700.0, 4500.0, 3950.0, 3650.0, 3550.0, 3500.0, 3675.0, 4450.0, 3400.0, 4300.0, 3250.0, 3675.0, 3325.0, 3950.0, 3600.0, 4050.0, 3350.0, 3450.0, 3250.0, 4050.0, 3800.0, 3525.0, 3950.0, 3650.0, 3650.0, 4000.0, 3400.0, 3775.0, 4100.0, 3775.0 \n", " corr_coef\n", "1 0.3858132\n", "2 0.6540233\n", "3 0.6535362" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 求每组下企鹅嘴峰长度与嘴峰厚度的相关系数\n", "tb %>% \n", " rowwise() %>% \n", " mutate(corr_coef = cor(data$bill_length_mm, data$bill_depth_mm))" ] }, { "cell_type": "markdown", "id": "59987a37", "metadata": {}, "source": [ "# 函数式编程1——`purrr`\n", "`R`常用的数据结构是向量、矩阵、列表和数据框\n", "![image.png](image/data_structure1.png)\n", "\n", "他们构造起来，很多相似性。" ] }, { "cell_type": "code", "execution_count": null, "id": "36ee2000", "metadata": {}, "outputs": [], "source": [ " list(a = 1, b = \"a\") # list\n", " c(a = 1, b = 2) # named vector\n", " data.frame(a = 1, b = 2) # data frame\n", " tibble(a = 1, b = 2) # tibble" ] }, { "cell_type": "markdown", "id": "a6a44fad", "metadata": {}, "source": [ "## 向量化运算" ] }, { "cell_type": "code", "execution_count": 189, "id": "d2619664", "metadata": {}, "outputs": [], "source": [ "a <- c(2, 4, 3, 1, 5, 7)" ] }, { "cell_type": "markdown", "id": "48a886f6", "metadata": {}, "source": [ "用`for()`循环，让向量的每个元素乘以`2`" ] }, { "cell_type": "code", "execution_count": 190, "id": "19bdfe3e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] 4\n", "[1] 8\n", "[1] 6\n", "[1] 2\n", "[1] 10\n", "[1] 14\n" ] } ], "source": [ "for (i in 1:length(a)){\n", " print(a[i] * 2)\n", "}" ] }, { "cell_type": "markdown", "id": "478c888b", "metadata": {}, "source": [ "事实上，R语言是支持向量化（将运算符或者函数作用在向量的每一个元素上），可以用向量化代替循环" ] }, { "cell_type": "code", "execution_count": 191, "id": "d64f4d74", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
  1. 4
  2. 8
  3. 6
  4. 2
  5. 10
  6. 14
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 4\n", "\\item 8\n", "\\item 6\n", "\\item 2\n", "\\item 10\n", "\\item 14\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 4\n", "2. 8\n", "3. 6\n", "4. 2\n", "5. 10\n", "6. 14\n", "\n", "\n" ], "text/plain": [ "[1] 4 8 6 2 10 14" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "a * 2" ] }, { "cell_type": "markdown", "id": "08533f5e", "metadata": {}, "source": [ "再比如，找出向量a中元素大于2的所有值" ] }, { "cell_type": "code", "execution_count": 194, "id": "2f5bc715", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] 4\n", "[1] 3\n", "[1] 5\n", "[1] 7\n" ] } ], "source": [ "for (i in 1:length(a)){\n", " if (a[i] > 2)\n", " {print(a[i])}\n", "}" ] }, { "cell_type": "markdown", "id": "13496bc4", "metadata": {}, "source": [ "用向量化的运算，可以轻松实现" ] }, { "cell_type": "code", "execution_count": 195, "id": "2f2a9df3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
  1. 4
  2. 3
  3. 5
  4. 7
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 4\n", "\\item 3\n", "\\item 5\n", "\\item 7\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 4\n", "2. 3\n", "3. 5\n", "4. 7\n", "\n", "\n" ], "text/plain": [ "[1] 4 3 5 7" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "a[a > 2]" ] }, { "cell_type": "markdown", "id": "ee06774b", "metadata": {}, "source": [ "## 列表" ] }, { "cell_type": "code", "execution_count": 197, "id": "96e4fbc1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\t
$num
\n", "\t\t
\n", "
  1. 8
  2. 9
\n", "
\n", "\t
$log
\n", "\t\t
TRUE
\n", "\t
$cha
\n", "\t\t
\n", "
  1. 'a'
  2. 'b'
  3. 'c'
\n", "
\n", "
\n" ], "text/latex": [ "\\begin{description}\n", "\\item[\\$num] \\begin{enumerate*}\n", "\\item 8\n", "\\item 9\n", "\\end{enumerate*}\n", "\n", "\\item[\\$log] TRUE\n", "\\item[\\$cha] \\begin{enumerate*}\n", "\\item 'a'\n", "\\item 'b'\n", "\\item 'c'\n", "\\end{enumerate*}\n", "\n", "\\end{description}\n" ], "text/markdown": [ "$num\n", ": 1. 8\n", "2. 9\n", "\n", "\n", "\n", "$log\n", ": TRUE\n", "$cha\n", ": 1. 'a'\n", "2. 'b'\n", "3. 'c'\n", "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "$num\n", "[1] 8 9\n", "\n", "$log\n", "[1] TRUE\n", "\n", "$cha\n", "[1] \"a\" \"b\" \"c\"\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "a_list <- list(\n", " num = c(8,9),\n", " log = TRUE,\n", " cha = c(\"a\", \"b\", \"c\")\n", ")\n", "a_list" ] }, { "cell_type": "markdown", "id": "1f4b2c85", "metadata": {}, "source": [ "**要想访问某个元素，可以这样**" ] }, { "cell_type": "code", "execution_count": 199, "id": "b70842cf", "metadata": {}, "outputs": [ { "data": { "text/html": [ "$log = TRUE" ], "text/latex": [ "\\textbf{\\$log} = TRUE" ], "text/markdown": [ "**$log** = TRUE" ], "text/plain": [ "$log\n", "[1] TRUE\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "a_list[\"log\"]" ] }, { "cell_type": "markdown", "id": "6e587d0b", "metadata": {}, "source": [ "注意返回结果，第一行是`$log`，说明返回的结果仍然是列表, 相比`a_list`来说，`a_list[\"log\"]`是只包含一个元素的列表。" ] }, { "cell_type": "markdown", "id": "13983b5f", "metadata": {}, "source": [ "将`log`元素里面的向量提取出来，就得用两个`[[`或者`$`" ] }, { "cell_type": "code", "execution_count": 202, "id": "1caffab8", "metadata": {}, "outputs": [ { "data": { "text/html": [ "TRUE" ], "text/latex": [ "TRUE" ], "text/markdown": [ "TRUE" ], "text/plain": [ "[1] TRUE" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "TRUE" ], "text/latex": [ "TRUE" ], "text/markdown": [ "TRUE" ], "text/plain": [ "[1] TRUE" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "a_list[[\"log\"]]\n", "a_list$log" ] }, { "cell_type": "markdown", "id": "67fdcd7c", "metadata": {}, "source": [ "![image.png](image/list_subset.png)" ] }, { "cell_type": "markdown", "id": "2446986d", "metadata": {}, "source": [ "在tidyverse里，还可以用" ] }, { "cell_type": "code", "execution_count": 207, "id": "a21082cb", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
  1. 8
  2. 9
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 8\n", "\\item 9\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 8\n", "2. 9\n", "\n", "\n" ], "text/plain": [ "[1] 8 9" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "
  1. 8
  2. 9
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 8\n", "\\item 9\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 8\n", "2. 9\n", "\n", "\n" ], "text/plain": [ "[1] 8 9" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "a_list %>% pluck(1)\n", "a_list %>% pluck(\"num\")" ] }, { "cell_type": "markdown", "id": "28fd23c1", "metadata": {}, "source": [ "## 列表 vs 向量" ] }, { "cell_type": "code", "execution_count": 211, "id": "39f7e006", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
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  2. 1
  3. 0
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\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 2\n", "\\item 1\n", "\\item 0\n", "\\item 1\n", "\\item 2\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 2\n", "2. 1\n", "3. 0\n", "4. 1\n", "5. 2\n", "\n", "\n" ], "text/plain": [ "[1] 2 1 0 1 2" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "v <- c(-2, -1, 0, 1, 2)\n", "abs(v)" ] }, { "cell_type": "markdown", "id": "231de49e", "metadata": {}, "source": [ "如果是列表形式，`abs`函数应用到列表中就会报错" ] }, { "cell_type": "code", "execution_count": 212, "id": "c362608c", "metadata": {}, "outputs": [ { "ename": "ERROR", "evalue": "Error in abs(lst): 数学函数中用了非数值参数\n", "output_type": "error", "traceback": [ "Error in abs(lst): 数学函数中用了非数值参数\nTraceback:\n" ] } ], "source": [ "lst <- list(-2, -1, 0, 1, 2)\n", "abs(lst)" ] }, { "cell_type": "markdown", "id": "4c098529", "metadata": {}, "source": [ "用在向量的函数用在`list`上，往往行不通。" ] }, { "cell_type": "code", "execution_count": 213, "id": "d0799905", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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A tibble: 1 × 5
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<dbl><dbl><dbl><dbl><dbl>
73.677.875.174.277.8
\n" ], "text/latex": [ "A tibble: 1 × 5\n", "\\begin{tabular}{lllll}\n", " student1 & student2 & student3 & student4 & student5\\\\\n", " & & & & \\\\\n", "\\hline\n", "\t 73.6 & 77.8 & 75.1 & 74.2 & 77.8\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 1 × 5\n", "\n", "| student1 <dbl> | student2 <dbl> | student3 <dbl> | student4 <dbl> | student5 <dbl> |\n", "|---|---|---|---|---|\n", "| 73.6 | 77.8 | 75.1 | 74.2 | 77.8 |\n", "\n" ], "text/plain": [ " student1 student2 student3 student4 student5\n", "1 73.6 77.8 75.1 74.2 77.8 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "exams %>% map_df(mean)" ] }, { "cell_type": "code", "execution_count": 226, "id": "21064c71", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\n", "
A tibble: 1 × 5
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73.677.875.174.277.8
\n" ], "text/latex": [ "A tibble: 1 × 5\n", "\\begin{tabular}{lllll}\n", " student1 & student2 & student3 & student4 & student5\\\\\n", " & & & & \\\\\n", "\\hline\n", "\t 73.6 & 77.8 & 75.1 & 74.2 & 77.8\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 1 × 5\n", "\n", "| student1 <dbl> | student2 <dbl> | student3 <dbl> | student4 <dbl> | student5 <dbl> |\n", "|---|---|---|---|---|\n", "| 73.6 | 77.8 | 75.1 | 74.2 | 77.8 |\n", "\n" ], "text/plain": [ " student1 student2 student3 student4 student5\n", "1 73.6 77.8 75.1 74.2 77.8 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "exams %>% \n", " data.frame() %>% \n", " map_df(mean)" ] }, { "cell_type": "markdown", "id": "e1416566", "metadata": {}, "source": [ "### 3 `map`函数小结\n", "![image.png](image/map_function1.png)\n", "- `map`函数第一个参数是向量或列表（数据框是列表的一种特殊形式，因此数据框也是可以的）![image-2.png](image/map_fun.png)\n", "- 第二个参数是函数，这个函数会应用到列表的每一个元素，比如这里`map`函数执行过程如下![image-3.png](image/map_function2.png)\n" ] }, { "cell_type": "markdown", "id": "94c3585c", "metadata": {}, "source": [ "我们也可以根据需要，让`map`返回我们需要的数据格式, `purrr`也提供了方便的函数，具体如下![image.png](image/map_function3.png)" ] }, { "cell_type": "code", "execution_count": 227, "id": "594689cc", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\n", "
A tibble: 1 × 5
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363.1556103.7333116.1267.9556233.5111
\n" ], "text/latex": [ "A tibble: 1 × 5\n", "\\begin{tabular}{lllll}\n", " student1 & student2 & student3 & student4 & student5\\\\\n", " & & & & \\\\\n", "\\hline\n", "\t 363.1556 & 103.7333 & 116.1 & 267.9556 & 233.5111\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 1 × 5\n", "\n", "| student1 <dbl> | student2 <dbl> | student3 <dbl> | student4 <dbl> | student5 <dbl> |\n", "|---|---|---|---|---|\n", "| 363.1556 | 103.7333 | 116.1 | 267.9556 | 233.5111 |\n", "\n" ], "text/plain": [ " student1 student2 student3 student4 student5\n", "1 363.1556 103.7333 116.1 267.9556 233.5111" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "exams %>% map_df(var) # 方差" ] }, { "cell_type": "markdown", "id": "17f3c504", "metadata": {}, "source": [ "### 4 额外参数\n", "- 在`sort()`函数里添加参数 `decreasing = TRUE`改为降序\n", "- `map`很人性化，可以让函数的参数直接跟随在函数名之后,`map()`会自动的传递给函数。 ![image.png](image/map-extra-arg.png)" ] }, { "cell_type": "code", "execution_count": 229, "id": "1bbf8f33", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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  6. 72
  7. 71
  8. 59
  9. 51
  10. 50
\n", "
\n", "\t
$student5
\n", "\t\t
\n", "
  1. 99
  2. 94
  3. 89
  4. 80
  5. 79
  6. 79
  7. 79
  8. 73
  9. 54
  10. 52
\n", "
\n", "
\n" ], "text/latex": [ "\\begin{description}\n", "\\item[\\$student1] \\begin{enumerate*}\n", "\\item 99\n", "\\item 97\n", "\\item 96\n", "\\item 83\n", "\\item 75\n", "\\item 65\n", "\\item 62\n", "\\item 54\n", "\\item 53\n", "\\item 52\n", "\\end{enumerate*}\n", "\n", "\\item[\\$student2] \\begin{enumerate*}\n", "\\item 94\n", "\\item 89\n", "\\item 86\n", "\\item 79\n", "\\item 79\n", "\\item 77\n", "\\item 74\n", "\\item 74\n", "\\item 65\n", "\\item 61\n", "\\end{enumerate*}\n", "\n", "\\item[\\$student3] \\begin{enumerate*}\n", "\\item 89\n", "\\item 87\n", "\\item 82\n", "\\item 80\n", "\\item 77\n", "\\item 76\n", "\\item 75\n", "\\item 67\n", "\\item 64\n", "\\item 54\n", "\\end{enumerate*}\n", "\n", "\\item[\\$student4] \\begin{enumerate*}\n", "\\item 94\n", "\\item 90\n", "\\item 90\n", "\\item 85\n", "\\item 80\n", "\\item 72\n", "\\item 71\n", "\\item 59\n", "\\item 51\n", "\\item 50\n", "\\end{enumerate*}\n", "\n", "\\item[\\$student5] \\begin{enumerate*}\n", "\\item 99\n", "\\item 94\n", "\\item 89\n", "\\item 80\n", "\\item 79\n", "\\item 79\n", "\\item 79\n", "\\item 73\n", "\\item 54\n", "\\item 52\n", "\\end{enumerate*}\n", "\n", "\\end{description}\n" ], "text/markdown": [ "$student1\n", ": 1. 99\n", "2. 97\n", "3. 96\n", "4. 83\n", "5. 75\n", "6. 65\n", "7. 62\n", "8. 54\n", "9. 53\n", "10. 52\n", "\n", "\n", "\n", "$student2\n", ": 1. 94\n", "2. 89\n", "3. 86\n", "4. 79\n", "5. 79\n", "6. 77\n", "7. 74\n", "8. 74\n", "9. 65\n", "10. 61\n", "\n", "\n", "\n", "$student3\n", ": 1. 89\n", "2. 87\n", "3. 82\n", "4. 80\n", "5. 77\n", "6. 76\n", "7. 75\n", "8. 67\n", "9. 64\n", "10. 54\n", "\n", "\n", "\n", "$student4\n", ": 1. 94\n", "2. 90\n", "3. 90\n", "4. 85\n", "5. 80\n", "6. 72\n", "7. 71\n", "8. 59\n", "9. 51\n", "10. 50\n", "\n", "\n", "\n", "$student5\n", ": 1. 99\n", "2. 94\n", "3. 89\n", "4. 80\n", "5. 79\n", "6. 79\n", "7. 79\n", "8. 73\n", "9. 54\n", "10. 52\n", "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "$student1\n", " [1] 99 97 96 83 75 65 62 54 53 52\n", "\n", "$student2\n", " [1] 94 89 86 79 79 77 74 74 65 61\n", "\n", "$student3\n", " [1] 89 87 82 80 77 76 75 67 64 54\n", "\n", "$student4\n", " [1] 94 90 90 85 80 72 71 59 51 50\n", "\n", "$student5\n", " [1] 99 94 89 80 79 79 79 73 54 52\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "map(exams, sort) # 默认升序\n", "map(exams, sort, decreasing=TRUE) # 改为降序" ] }, { "cell_type": "markdown", "id": "5f63ed9b", "metadata": {}, "source": [ "### 5 匿名函数\n", "我们也可以自定义函数。 比如我们这里定义了将向量**中心化**的函数（先求出10次考试的平均值，然后每次考试成绩去减这个平均值）" ] }, { "cell_type": "code", "execution_count": 230, "id": "cff626f5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 10 × 5
student1student2student3student4student5
<dbl><dbl><dbl><dbl><dbl>
23.4 1.2 4.9 10.8 16.2
22.4 11.2 11.9-23.2 21.2
-11.6 8.2 -0.1 15.8 1.2
-19.6 -3.8-21.1-24.2 -4.8
25.4-16.8 -8.1 -3.2 2.2
-20.6 -3.8 1.9 15.8 11.2
9.4-12.8 0.9 19.8 1.2
-21.6 1.2 13.9-15.2-25.8
1.4 -0.8 6.9 5.8-23.8
-8.6 16.2-11.1 -2.2 1.2
\n" ], "text/latex": [ "A tibble: 10 × 5\n", "\\begin{tabular}{lllll}\n", " student1 & student2 & student3 & student4 & student5\\\\\n", " & & & & \\\\\n", "\\hline\n", "\t 23.4 & 1.2 & 4.9 & 10.8 & 16.2\\\\\n", "\t 22.4 & 11.2 & 11.9 & -23.2 & 21.2\\\\\n", "\t -11.6 & 8.2 & -0.1 & 15.8 & 1.2\\\\\n", "\t -19.6 & -3.8 & -21.1 & -24.2 & -4.8\\\\\n", "\t 25.4 & -16.8 & -8.1 & -3.2 & 2.2\\\\\n", "\t -20.6 & -3.8 & 1.9 & 15.8 & 11.2\\\\\n", "\t 9.4 & -12.8 & 0.9 & 19.8 & 1.2\\\\\n", "\t -21.6 & 1.2 & 13.9 & -15.2 & -25.8\\\\\n", "\t 1.4 & -0.8 & 6.9 & 5.8 & -23.8\\\\\n", "\t -8.6 & 16.2 & -11.1 & -2.2 & 1.2\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 10 × 5\n", "\n", "| student1 <dbl> | student2 <dbl> | student3 <dbl> | student4 <dbl> | student5 <dbl> |\n", "|---|---|---|---|---|\n", "| 23.4 | 1.2 | 4.9 | 10.8 | 16.2 |\n", "| 22.4 | 11.2 | 11.9 | -23.2 | 21.2 |\n", "| -11.6 | 8.2 | -0.1 | 15.8 | 1.2 |\n", "| -19.6 | -3.8 | -21.1 | -24.2 | -4.8 |\n", "| 25.4 | -16.8 | -8.1 | -3.2 | 2.2 |\n", "| -20.6 | -3.8 | 1.9 | 15.8 | 11.2 |\n", "| 9.4 | -12.8 | 0.9 | 19.8 | 1.2 |\n", "| -21.6 | 1.2 | 13.9 | -15.2 | -25.8 |\n", "| 1.4 | -0.8 | 6.9 | 5.8 | -23.8 |\n", "| -8.6 | 16.2 | -11.1 | -2.2 | 1.2 |\n", "\n" ], "text/plain": [ " student1 student2 student3 student4 student5\n", "1 23.4 1.2 4.9 10.8 16.2 \n", "2 22.4 11.2 11.9 -23.2 21.2 \n", "3 -11.6 8.2 -0.1 15.8 1.2 \n", "4 -19.6 -3.8 -21.1 -24.2 -4.8 \n", "5 25.4 -16.8 -8.1 -3.2 2.2 \n", "6 -20.6 -3.8 1.9 15.8 11.2 \n", "7 9.4 -12.8 0.9 19.8 1.2 \n", "8 -21.6 1.2 13.9 -15.2 -25.8 \n", "9 1.4 -0.8 6.9 5.8 -23.8 \n", "10 -8.6 16.2 -11.1 -2.2 1.2 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "my_fun <- function(x){\n", " x - mean(x)\n", "}\n", "exams %>% map_df(my_fun)" ] }, { "cell_type": "markdown", "id": "5c4c0f5f", "metadata": {}, "source": [ "我们也可以不用命名函数，而使用**匿名函数**。匿名函数顾名思义，就是没有名字的函数，\n", "\n", "匿名函数直接放在`map()`函数中" ] }, { "cell_type": "code", "execution_count": 233, "id": "2bc252d2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 10 × 5
student1student2student3student4student5
<dbl><dbl><dbl><dbl><dbl>
23.4 1.2 4.9 10.8 16.2
22.4 11.2 11.9-23.2 21.2
-11.6 8.2 -0.1 15.8 1.2
-19.6 -3.8-21.1-24.2 -4.8
25.4-16.8 -8.1 -3.2 2.2
-20.6 -3.8 1.9 15.8 11.2
9.4-12.8 0.9 19.8 1.2
-21.6 1.2 13.9-15.2-25.8
1.4 -0.8 6.9 5.8-23.8
-8.6 16.2-11.1 -2.2 1.2
\n" ], "text/latex": [ "A tibble: 10 × 5\n", "\\begin{tabular}{lllll}\n", " student1 & student2 & student3 & student4 & student5\\\\\n", " & & & & \\\\\n", "\\hline\n", "\t 23.4 & 1.2 & 4.9 & 10.8 & 16.2\\\\\n", "\t 22.4 & 11.2 & 11.9 & -23.2 & 21.2\\\\\n", "\t -11.6 & 8.2 & -0.1 & 15.8 & 1.2\\\\\n", "\t -19.6 & -3.8 & -21.1 & -24.2 & -4.8\\\\\n", "\t 25.4 & -16.8 & -8.1 & -3.2 & 2.2\\\\\n", "\t -20.6 & -3.8 & 1.9 & 15.8 & 11.2\\\\\n", "\t 9.4 & -12.8 & 0.9 & 19.8 & 1.2\\\\\n", "\t -21.6 & 1.2 & 13.9 & -15.2 & -25.8\\\\\n", "\t 1.4 & -0.8 & 6.9 & 5.8 & -23.8\\\\\n", "\t -8.6 & 16.2 & -11.1 & -2.2 & 1.2\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 10 × 5\n", "\n", "| student1 <dbl> | student2 <dbl> | student3 <dbl> | student4 <dbl> | student5 <dbl> |\n", "|---|---|---|---|---|\n", "| 23.4 | 1.2 | 4.9 | 10.8 | 16.2 |\n", "| 22.4 | 11.2 | 11.9 | -23.2 | 21.2 |\n", "| -11.6 | 8.2 | -0.1 | 15.8 | 1.2 |\n", "| -19.6 | -3.8 | -21.1 | -24.2 | -4.8 |\n", "| 25.4 | -16.8 | -8.1 | -3.2 | 2.2 |\n", "| -20.6 | -3.8 | 1.9 | 15.8 | 11.2 |\n", "| 9.4 | -12.8 | 0.9 | 19.8 | 1.2 |\n", "| -21.6 | 1.2 | 13.9 | -15.2 | -25.8 |\n", "| 1.4 | -0.8 | 6.9 | 5.8 | -23.8 |\n", "| -8.6 | 16.2 | -11.1 | -2.2 | 1.2 |\n", "\n" ], "text/plain": [ " student1 student2 student3 student4 student5\n", "1 23.4 1.2 4.9 10.8 16.2 \n", "2 22.4 11.2 11.9 -23.2 21.2 \n", "3 -11.6 8.2 -0.1 15.8 1.2 \n", "4 -19.6 -3.8 -21.1 -24.2 -4.8 \n", "5 25.4 -16.8 -8.1 -3.2 2.2 \n", "6 -20.6 -3.8 1.9 15.8 11.2 \n", "7 9.4 -12.8 0.9 19.8 1.2 \n", "8 -21.6 1.2 13.9 -15.2 -25.8 \n", "9 1.4 -0.8 6.9 5.8 -23.8 \n", "10 -8.6 16.2 -11.1 -2.2 1.2 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "exams %>% \n", " map_df(function(x){x - mean(x)})" ] }, { "cell_type": "markdown", "id": "e51eb16a", "metadata": {}, "source": [ "还可以更加偷懒，用`~`代替`function()`，但代价是参数必须是规定的写法，比如`.x`" ] }, { "cell_type": "code", "execution_count": 234, "id": "9769a681", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 10 × 5
student1student2student3student4student5
<dbl><dbl><dbl><dbl><dbl>
23.4 1.2 4.9 10.8 16.2
22.4 11.2 11.9-23.2 21.2
-11.6 8.2 -0.1 15.8 1.2
-19.6 -3.8-21.1-24.2 -4.8
25.4-16.8 -8.1 -3.2 2.2
-20.6 -3.8 1.9 15.8 11.2
9.4-12.8 0.9 19.8 1.2
-21.6 1.2 13.9-15.2-25.8
1.4 -0.8 6.9 5.8-23.8
-8.6 16.2-11.1 -2.2 1.2
\n" ], "text/latex": [ "A tibble: 10 × 5\n", "\\begin{tabular}{lllll}\n", " student1 & student2 & student3 & student4 & student5\\\\\n", " & & & & \\\\\n", "\\hline\n", "\t 23.4 & 1.2 & 4.9 & 10.8 & 16.2\\\\\n", "\t 22.4 & 11.2 & 11.9 & -23.2 & 21.2\\\\\n", "\t -11.6 & 8.2 & -0.1 & 15.8 & 1.2\\\\\n", "\t -19.6 & -3.8 & -21.1 & -24.2 & -4.8\\\\\n", "\t 25.4 & -16.8 & -8.1 & -3.2 & 2.2\\\\\n", "\t -20.6 & -3.8 & 1.9 & 15.8 & 11.2\\\\\n", "\t 9.4 & -12.8 & 0.9 & 19.8 & 1.2\\\\\n", "\t -21.6 & 1.2 & 13.9 & -15.2 & -25.8\\\\\n", "\t 1.4 & -0.8 & 6.9 & 5.8 & -23.8\\\\\n", "\t -8.6 & 16.2 & -11.1 & -2.2 & 1.2\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 10 × 5\n", "\n", "| student1 <dbl> | student2 <dbl> | student3 <dbl> | student4 <dbl> | student5 <dbl> |\n", "|---|---|---|---|---|\n", "| 23.4 | 1.2 | 4.9 | 10.8 | 16.2 |\n", "| 22.4 | 11.2 | 11.9 | -23.2 | 21.2 |\n", "| -11.6 | 8.2 | -0.1 | 15.8 | 1.2 |\n", "| -19.6 | -3.8 | -21.1 | -24.2 | -4.8 |\n", "| 25.4 | -16.8 | -8.1 | -3.2 | 2.2 |\n", "| -20.6 | -3.8 | 1.9 | 15.8 | 11.2 |\n", "| 9.4 | -12.8 | 0.9 | 19.8 | 1.2 |\n", "| -21.6 | 1.2 | 13.9 | -15.2 | -25.8 |\n", "| 1.4 | -0.8 | 6.9 | 5.8 | -23.8 |\n", "| -8.6 | 16.2 | -11.1 | -2.2 | 1.2 |\n", "\n" ], "text/plain": [ " student1 student2 student3 student4 student5\n", "1 23.4 1.2 4.9 10.8 16.2 \n", "2 22.4 11.2 11.9 -23.2 21.2 \n", "3 -11.6 8.2 -0.1 15.8 1.2 \n", "4 -19.6 -3.8 -21.1 -24.2 -4.8 \n", "5 25.4 -16.8 -8.1 -3.2 2.2 \n", "6 -20.6 -3.8 1.9 15.8 11.2 \n", "7 9.4 -12.8 0.9 19.8 1.2 \n", "8 -21.6 1.2 13.9 -15.2 -25.8 \n", "9 1.4 -0.8 6.9 5.8 -23.8 \n", "10 -8.6 16.2 -11.1 -2.2 1.2 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "exams %>% map_df(~.x - mean(.x))" ] }, { "cell_type": "markdown", "id": "97ca64fa", "metadata": {}, "source": [ "有时候，程序员觉得`x`还是有点多余，于是更够懒一点，只用`.`， 也是可以的" ] }, { "cell_type": "code", "execution_count": 235, "id": "4a80dc1b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 10 × 5
student1student2student3student4student5
<dbl><dbl><dbl><dbl><dbl>
23.4 1.2 4.9 10.8 16.2
22.4 11.2 11.9-23.2 21.2
-11.6 8.2 -0.1 15.8 1.2
-19.6 -3.8-21.1-24.2 -4.8
25.4-16.8 -8.1 -3.2 2.2
-20.6 -3.8 1.9 15.8 11.2
9.4-12.8 0.9 19.8 1.2
-21.6 1.2 13.9-15.2-25.8
1.4 -0.8 6.9 5.8-23.8
-8.6 16.2-11.1 -2.2 1.2
\n" ], "text/latex": [ "A tibble: 10 × 5\n", "\\begin{tabular}{lllll}\n", " student1 & student2 & student3 & student4 & student5\\\\\n", " & & & & \\\\\n", "\\hline\n", "\t 23.4 & 1.2 & 4.9 & 10.8 & 16.2\\\\\n", "\t 22.4 & 11.2 & 11.9 & -23.2 & 21.2\\\\\n", "\t -11.6 & 8.2 & -0.1 & 15.8 & 1.2\\\\\n", "\t -19.6 & -3.8 & -21.1 & -24.2 & -4.8\\\\\n", "\t 25.4 & -16.8 & -8.1 & -3.2 & 2.2\\\\\n", "\t -20.6 & -3.8 & 1.9 & 15.8 & 11.2\\\\\n", "\t 9.4 & -12.8 & 0.9 & 19.8 & 1.2\\\\\n", "\t -21.6 & 1.2 & 13.9 & -15.2 & -25.8\\\\\n", "\t 1.4 & -0.8 & 6.9 & 5.8 & -23.8\\\\\n", "\t -8.6 & 16.2 & -11.1 & -2.2 & 1.2\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 10 × 5\n", "\n", "| student1 <dbl> | student2 <dbl> | student3 <dbl> | student4 <dbl> | student5 <dbl> |\n", "|---|---|---|---|---|\n", "| 23.4 | 1.2 | 4.9 | 10.8 | 16.2 |\n", "| 22.4 | 11.2 | 11.9 | -23.2 | 21.2 |\n", "| -11.6 | 8.2 | -0.1 | 15.8 | 1.2 |\n", "| -19.6 | -3.8 | -21.1 | -24.2 | -4.8 |\n", "| 25.4 | -16.8 | -8.1 | -3.2 | 2.2 |\n", "| -20.6 | -3.8 | 1.9 | 15.8 | 11.2 |\n", "| 9.4 | -12.8 | 0.9 | 19.8 | 1.2 |\n", "| -21.6 | 1.2 | 13.9 | -15.2 | -25.8 |\n", "| 1.4 | -0.8 | 6.9 | 5.8 | -23.8 |\n", "| -8.6 | 16.2 | -11.1 | -2.2 | 1.2 |\n", "\n" ], "text/plain": [ " student1 student2 student3 student4 student5\n", "1 23.4 1.2 4.9 10.8 16.2 \n", "2 22.4 11.2 11.9 -23.2 21.2 \n", "3 -11.6 8.2 -0.1 15.8 1.2 \n", "4 -19.6 -3.8 -21.1 -24.2 -4.8 \n", "5 25.4 -16.8 -8.1 -3.2 2.2 \n", "6 -20.6 -3.8 1.9 15.8 11.2 \n", "7 9.4 -12.8 0.9 19.8 1.2 \n", "8 -21.6 1.2 13.9 -15.2 -25.8 \n", "9 1.4 -0.8 6.9 5.8 -23.8 \n", "10 -8.6 16.2 -11.1 -2.2 1.2 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "exams %>% map_df(~. - mean(.))" ] }, { "cell_type": "markdown", "id": "9d2a5521", "metadata": {}, "source": [ "`~ `告诉 `map()` 后面跟随的是一个匿名函数，`.` 对应函数的参数，可以认为是一个占位符，等待传送带的student1、student2到student5 依次传递到函数机器。![image.png](image/map-anonymous.png)" ] }, { "cell_type": "markdown", "id": "416a99a4", "metadata": {}, "source": [ "如果熟悉匿名函数的写法，会增强代码的可读性" ] }, { "cell_type": "code", "execution_count": 236, "id": "fe769c23", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\n", "
A tibble: 1 × 5
student1student2student3student4student5
<int><int><int><int><int>
43343
\n" ], "text/latex": [ "A tibble: 1 × 5\n", "\\begin{tabular}{lllll}\n", " student1 & student2 & student3 & student4 & student5\\\\\n", " & & & & \\\\\n", "\\hline\n", "\t 4 & 3 & 3 & 4 & 3\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 1 × 5\n", "\n", "| student1 <int> | student2 <int> | student3 <int> | student4 <int> | student5 <int> |\n", "|---|---|---|---|---|\n", "| 4 | 3 | 3 | 4 | 3 |\n", "\n" ], "text/plain": [ " student1 student2 student3 student4 student5\n", "1 4 3 3 4 3 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "exams %>% \n", " map_df(~length(.[. > 80]))" ] }, { "cell_type": "markdown", "id": "f2779ee3", "metadata": {}, "source": [ "总之，有三种方法将函数传递给`map()`" ] }, { "cell_type": "markdown", "id": "a8e494fc", "metadata": {}, "source": [ "- 直接传递" ] }, { "cell_type": "code", "execution_count": null, "id": "b5d45576", "metadata": {}, "outputs": [], "source": [ "map(.x, mean, na.rm=TRUE)" ] }, { "cell_type": "markdown", "id": "1b3e1f1f", "metadata": {}, "source": [ "- 匿名函数" ] }, { "cell_type": "code", "execution_count": null, "id": "6de43bdc", "metadata": {}, "outputs": [], "source": [ "map(.x, funcition(.x){mean(.x, na.rm=TRUE)})" ] }, { "cell_type": "markdown", "id": "3df0ccdf", "metadata": {}, "source": [ "- 使用 `~`" ] }, { "cell_type": "code", "execution_count": null, "id": "1355553e", "metadata": {}, "outputs": [], "source": [ "function(.x){.x*2}\n", "\n", "~.x*2\n", "\n", "map(.x, ~mean(.x, na.rm=TRUE))" ] }, { "cell_type": "markdown", "id": "188b189a", "metadata": {}, "source": [ "## 在`dplyr`函数中的运用`map`\n", "### 1 在`Tibble`中\n", "`Tibble`本质上是向量构成的列表，因此`tibble`也适用`map`。假定有`tibble`如下" ] }, { "cell_type": "code", "execution_count": 239, "id": "6e7280f7", "metadata": {}, "outputs": [], "source": [ "tb <- \n", " tibble(\n", " col_1 = c(1, 2, 3),\n", " col_2 = c(100, 200, 300),\n", " col_3 = c(0.1, 0.2, 0.3)\n", " )" ] }, { "cell_type": "markdown", "id": "fcd6c812", "metadata": {}, "source": [ "`map()`中的函数`f`，可以作用到每一列" ] }, { "cell_type": "code", "execution_count": 240, "id": "9d3bdbb1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\n", "
A tibble: 1 × 3
col_1col_2col_3
<dbl><dbl><dbl>
22000.2
\n" ], "text/latex": [ "A tibble: 1 × 3\n", "\\begin{tabular}{lll}\n", " col\\_1 & col\\_2 & col\\_3\\\\\n", " & & \\\\\n", "\\hline\n", "\t 2 & 200 & 0.2\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 1 × 3\n", "\n", "| col_1 <dbl> | col_2 <dbl> | col_3 <dbl> |\n", "|---|---|---|\n", "| 2 | 200 | 0.2 |\n", "\n" ], "text/plain": [ " col_1 col_2 col_3\n", "1 2 200 0.2 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "map_df(tb, median)" ] }, { "cell_type": "markdown", "id": "94a2e0d2", "metadata": {}, "source": [ "在比如，找出企鹅数据中每列缺失值`NA`的数量" ] }, { "cell_type": "code", "execution_count": 242, "id": "5bcf7f41", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\n", "
A tibble: 1 × 8
speciesislandbill_length_mmbill_depth_mmflipper_length_mmbody_mass_gsexyear
<int><int><int><int><int><int><int><int>
002222110
\n" ], "text/latex": [ "A tibble: 1 × 8\n", "\\begin{tabular}{llllllll}\n", " species & island & bill\\_length\\_mm & bill\\_depth\\_mm & flipper\\_length\\_mm & body\\_mass\\_g & sex & year\\\\\n", " & & & & & & & \\\\\n", "\\hline\n", "\t 0 & 0 & 2 & 2 & 2 & 2 & 11 & 0\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 1 × 8\n", "\n", "| species <int> | island <int> | bill_length_mm <int> | bill_depth_mm <int> | flipper_length_mm <int> | body_mass_g <int> | sex <int> | year <int> |\n", "|---|---|---|---|---|---|---|---|\n", "| 0 | 0 | 2 | 2 | 2 | 2 | 11 | 0 |\n", "\n" ], "text/plain": [ " species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex\n", "1 0 0 2 2 2 2 11 \n", " year\n", "1 0 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "palmerpenguins::penguins %>% \n", " map_df(~sum(is.na(.)))" ] }, { "cell_type": "markdown", "id": "22655bc2", "metadata": {}, "source": [ "### 2 在`col-column`中\n", "如果想显示列表中每个元素的长度，可以这样写" ] }, { "cell_type": "code", "execution_count": 245, "id": "81d75576", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 3 × 2
xlength
<list><int>
11
2, 32
4, 5, 63
\n" ], "text/latex": [ "A tibble: 3 × 2\n", "\\begin{tabular}{ll}\n", " x & length\\\\\n", " & \\\\\n", "\\hline\n", "\t 1 & 1\\\\\n", "\t 2, 3 & 2\\\\\n", "\t 4, 5, 6 & 3\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 3 × 2\n", "\n", "| x <list> | length <int> |\n", "|---|---|\n", "| 1 | 1 |\n", "| 2, 3 | 2 |\n", "| 4, 5, 6 | 3 |\n", "\n" ], "text/plain": [ " x length\n", "1 1 1 \n", "2 2, 3 2 \n", "3 4, 5, 6 3 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tibble(\n", " x = list(1, 2:3, 4:6)\n", ") %>% \n", " mutate(length = purrr::map_int(x, length))" ] }, { "cell_type": "markdown", "id": "e73ae25b", "metadata": {}, "source": [ "用于各种函数，比如产生随机数" ] }, { "cell_type": "code", "execution_count": 249, "id": "49faccbc", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 3 × 2
xr
<dbl><list>
3-0.9904658, -1.0449938, -0.2570393
50.5757297, 0.0917176, -0.9051678, 0.6882445, 1.3148412
6-0.89064232, -1.16340784, 0.17958432, 1.39125076, -0.36293285, -0.09630495
\n" ], "text/latex": [ "A tibble: 3 × 2\n", "\\begin{tabular}{ll}\n", " x & r\\\\\n", " & \\\\\n", "\\hline\n", "\t 3 & -0.9904658, -1.0449938, -0.2570393\\\\\n", "\t 5 & 0.5757297, 0.0917176, -0.9051678, 0.6882445, 1.3148412\\\\\n", "\t 6 & -0.89064232, -1.16340784, 0.17958432, 1.39125076, -0.36293285, -0.09630495\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 3 × 2\n", "\n", "| x <dbl> | r <list> |\n", "|---|---|\n", "| 3 | -0.9904658, -1.0449938, -0.2570393 |\n", "| 5 | 0.5757297, 0.0917176, -0.9051678, 0.6882445, 1.3148412 |\n", "| 6 | -0.89064232, -1.16340784, 0.17958432, 1.39125076, -0.36293285, -0.09630495 |\n", "\n" ], "text/plain": [ " x r \n", "1 3 -0.9904658, -1.0449938, -0.2570393 \n", "2 5 0.5757297, 0.0917176, -0.9051678, 0.6882445, 1.3148412 \n", "3 6 -0.89064232, -1.16340784, 0.17958432, 1.39125076, -0.36293285, -0.09630495" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tibble(\n", " x = c(3, 5, 6)\n", ") %>% \n", " mutate(r = purrr::map(x, ~rnorm(., mean= 0, sd = 1)))" ] }, { "cell_type": "markdown", "id": "c4807cf2", "metadata": {}, "source": [ "用于建模" ] }, { "cell_type": "code", "execution_count": 250, "id": "8a8158f3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A grouped_df: 6 × 8
cyldatamodeltermestimatestd.errorstatisticp.value
<dbl><list><list><chr><dbl><dbl><dbl><dbl>
621.000, 21.000, 21.400, 18.100, 19.200, 17.800, 19.700, 160.000, 160.000, 258.000, 225.000, 167.600, 167.600, 145.000, 110.000, 110.000, 110.000, 105.000, 123.000, 123.000, 175.000, 3.900, 3.900, 3.080, 2.760, 3.920, 3.920, 3.620, 2.620, 2.875, 3.215, 3.460, 3.440, 3.440, 2.770, 16.460, 17.020, 19.440, 20.220, 18.300, 18.900, 15.500, 0.000, 0.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 0.000, 0.000, 0.000, 0.000, 1.000, 4.000, 4.000, 3.000, 3.000, 4.000, 4.000, 5.000, 4.000, 4.000, 1.000, 1.000, 4.000, 4.000, 6.00028.40884, -2.780106, -0.124967, 0.5839601, 1.929196, -0.689678, 0.3547199, -1.04528, -1.007951, -52.23469, -2.426656, 2.111436, -0.3526643, 0.679099, -0.720901, -1.10683, 2, 21.12497, 20.41604, 19.4708, 18.78968, 18.84528, 18.84528, 20.70795, 0, 1, -2.645751, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, -8.247185, 0.8728647, -0.2683341, -0.5490191, -0.526106, -0.526106, 0.2414814, 1.377964, 1.121188, 1, 2, 1e-07, 2, 5, lm(formula = mpg ~ wt, data = .), mpg ~ wt, 21, 21, 21.4, 18.1, 19.2, 17.8, 19.7, 2.62, 2.875, 3.215, 3.46, 3.44, 3.44, 2.77(Intercept)28.4088454.1843688 6.7892781.054844e-03
621.000, 21.000, 21.400, 18.100, 19.200, 17.800, 19.700, 160.000, 160.000, 258.000, 225.000, 167.600, 167.600, 145.000, 110.000, 110.000, 110.000, 105.000, 123.000, 123.000, 175.000, 3.900, 3.900, 3.080, 2.760, 3.920, 3.920, 3.620, 2.620, 2.875, 3.215, 3.460, 3.440, 3.440, 2.770, 16.460, 17.020, 19.440, 20.220, 18.300, 18.900, 15.500, 0.000, 0.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 0.000, 0.000, 0.000, 0.000, 1.000, 4.000, 4.000, 3.000, 3.000, 4.000, 4.000, 5.000, 4.000, 4.000, 1.000, 1.000, 4.000, 4.000, 6.00028.40884, -2.780106, -0.124967, 0.5839601, 1.929196, -0.689678, 0.3547199, -1.04528, -1.007951, -52.23469, -2.426656, 2.111436, -0.3526643, 0.679099, -0.720901, -1.10683, 2, 21.12497, 20.41604, 19.4708, 18.78968, 18.84528, 18.84528, 20.70795, 0, 1, -2.645751, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, -8.247185, 0.8728647, -0.2683341, -0.5490191, -0.526106, -0.526106, 0.2414814, 1.377964, 1.121188, 1, 2, 1e-07, 2, 5, lm(formula = mpg ~ wt, data = .), mpg ~ wt, 21, 21, 21.4, 18.1, 19.2, 17.8, 19.7, 2.62, 2.875, 3.215, 3.46, 3.44, 3.44, 2.77wt -2.7801061.3349173-2.0826059.175766e-02
422.800, 24.400, 22.800, 32.400, 30.400, 33.900, 21.500, 27.300, 26.000, 30.400, 21.400, 108.000, 146.700, 140.800, 78.700, 75.700, 71.100, 120.100, 79.000, 120.300, 95.100, 121.000, 93.000, 62.000, 95.000, 66.000, 52.000, 65.000, 97.000, 66.000, 91.000, 113.000, 109.000, 3.850, 3.690, 3.920, 4.080, 4.930, 4.220, 3.700, 4.080, 4.430, 3.770, 4.110, 2.320, 3.190, 3.150, 2.200, 1.615, 1.835, 2.465, 1.935, 2.140, 1.513, 2.780, 18.610, 20.000, 22.900, 19.470, 18.520, 19.900, 20.010, 18.900, 16.700, 16.900, 18.600, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 0.000, 0.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 1.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 3.000, 4.000, 5.000, 5.000, 4.000, 1.000, 2.000, 2.000, 1.000, 2.000, 1.000, 1.000, 1.000, 2.000, 2.000, 2.00039.5712, -5.647025, -3.670097, 2.842815, 1.016934, 5.25226, -0.05125022, 4.691095, -4.151279, -1.344202, -1.486562, -0.6272468, -2.472466, -88.43328, 10.17096, 0.6947654, 6.230721, 1.728126, 6.169273, -3.535624, -0.00293297, -0.4259551, 1.291776, -2.288073, 2, 26.4701, 21.55719, 21.78307, 27.14774, 30.45125, 29.2089, 25.65128, 28.6442, 27.48656, 31.02725, 23.87247, 0, 1, -3.316625, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, -7.5809, -1.801119, 0.4754451, -0.05200489, -0.376803, -0.2546567, 0.0951259, -0.1991357, -0.08531752, -0.4334345, 0.2700172, 1.301511, 1.497654, 1, 2, 1e-07, 2, 9, lm(formula = mpg ~ wt, data = .), mpg ~ wt, 22.8, 24.4, 22.8, 32.4, 30.4, 33.9, 21.5, 27.3, 26, 30.4, 21.4, 2.32, 3.19, 3.15, 2.2, 1.615, 1.835, 2.465, 1.935, 2.14, 1.513, 2.78(Intercept)39.5711964.3465820 9.1039807.771511e-06
422.800, 24.400, 22.800, 32.400, 30.400, 33.900, 21.500, 27.300, 26.000, 30.400, 21.400, 108.000, 146.700, 140.800, 78.700, 75.700, 71.100, 120.100, 79.000, 120.300, 95.100, 121.000, 93.000, 62.000, 95.000, 66.000, 52.000, 65.000, 97.000, 66.000, 91.000, 113.000, 109.000, 3.850, 3.690, 3.920, 4.080, 4.930, 4.220, 3.700, 4.080, 4.430, 3.770, 4.110, 2.320, 3.190, 3.150, 2.200, 1.615, 1.835, 2.465, 1.935, 2.140, 1.513, 2.780, 18.610, 20.000, 22.900, 19.470, 18.520, 19.900, 20.010, 18.900, 16.700, 16.900, 18.600, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 0.000, 0.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 1.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 3.000, 4.000, 5.000, 5.000, 4.000, 1.000, 2.000, 2.000, 1.000, 2.000, 1.000, 1.000, 1.000, 2.000, 2.000, 2.00039.5712, -5.647025, -3.670097, 2.842815, 1.016934, 5.25226, -0.05125022, 4.691095, -4.151279, -1.344202, -1.486562, -0.6272468, -2.472466, -88.43328, 10.17096, 0.6947654, 6.230721, 1.728126, 6.169273, -3.535624, -0.00293297, -0.4259551, 1.291776, -2.288073, 2, 26.4701, 21.55719, 21.78307, 27.14774, 30.45125, 29.2089, 25.65128, 28.6442, 27.48656, 31.02725, 23.87247, 0, 1, -3.316625, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, -7.5809, -1.801119, 0.4754451, -0.05200489, -0.376803, -0.2546567, 0.0951259, -0.1991357, -0.08531752, -0.4334345, 0.2700172, 1.301511, 1.497654, 1, 2, 1e-07, 2, 9, lm(formula = mpg ~ wt, data = .), mpg ~ wt, 22.8, 24.4, 22.8, 32.4, 30.4, 33.9, 21.5, 27.3, 26, 30.4, 21.4, 2.32, 3.19, 3.15, 2.2, 1.615, 1.835, 2.465, 1.935, 2.14, 1.513, 2.78wt -5.6470251.8501185-3.0522511.374278e-02
818.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.00023.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = .), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57(Intercept)23.8680293.0054619 7.9415514.052705e-06
818.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.00023.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = .), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57wt -2.1924380.7392393-2.9658031.179281e-02
\n" ], "text/latex": [ "A grouped\\_df: 6 × 8\n", "\\begin{tabular}{llllllll}\n", " cyl & data & model & term & estimate & std.error & statistic & p.value\\\\\n", " & & & & & & & \\\\\n", "\\hline\n", "\t 6 & 21.000, 21.000, 21.400, 18.100, 19.200, 17.800, 19.700, 160.000, 160.000, 258.000, 225.000, 167.600, 167.600, 145.000, 110.000, 110.000, 110.000, 105.000, 123.000, 123.000, 175.000, 3.900, 3.900, 3.080, 2.760, 3.920, 3.920, 3.620, 2.620, 2.875, 3.215, 3.460, 3.440, 3.440, 2.770, 16.460, 17.020, 19.440, 20.220, 18.300, 18.900, 15.500, 0.000, 0.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 0.000, 0.000, 0.000, 0.000, 1.000, 4.000, 4.000, 3.000, 3.000, 4.000, 4.000, 5.000, 4.000, 4.000, 1.000, 1.000, 4.000, 4.000, 6.000 & 28.40884, -2.780106, -0.124967, 0.5839601, 1.929196, -0.689678, 0.3547199, -1.04528, -1.007951, -52.23469, -2.426656, 2.111436, -0.3526643, 0.679099, -0.720901, -1.10683, 2, 21.12497, 20.41604, 19.4708, 18.78968, 18.84528, 18.84528, 20.70795, 0, 1, -2.645751, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, -8.247185, 0.8728647, -0.2683341, -0.5490191, -0.526106, -0.526106, 0.2414814, 1.377964, 1.121188, 1, 2, 1e-07, 2, 5, lm(formula = mpg \\textasciitilde{} wt, data = .), mpg \\textasciitilde{} wt, 21, 21, 21.4, 18.1, 19.2, 17.8, 19.7, 2.62, 2.875, 3.215, 3.46, 3.44, 3.44, 2.77 & (Intercept) & 28.408845 & 4.1843688 & 6.789278 & 1.054844e-03\\\\\n", "\t 6 & 21.000, 21.000, 21.400, 18.100, 19.200, 17.800, 19.700, 160.000, 160.000, 258.000, 225.000, 167.600, 167.600, 145.000, 110.000, 110.000, 110.000, 105.000, 123.000, 123.000, 175.000, 3.900, 3.900, 3.080, 2.760, 3.920, 3.920, 3.620, 2.620, 2.875, 3.215, 3.460, 3.440, 3.440, 2.770, 16.460, 17.020, 19.440, 20.220, 18.300, 18.900, 15.500, 0.000, 0.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 0.000, 0.000, 0.000, 0.000, 1.000, 4.000, 4.000, 3.000, 3.000, 4.000, 4.000, 5.000, 4.000, 4.000, 1.000, 1.000, 4.000, 4.000, 6.000 & 28.40884, -2.780106, -0.124967, 0.5839601, 1.929196, -0.689678, 0.3547199, -1.04528, -1.007951, -52.23469, -2.426656, 2.111436, -0.3526643, 0.679099, -0.720901, -1.10683, 2, 21.12497, 20.41604, 19.4708, 18.78968, 18.84528, 18.84528, 20.70795, 0, 1, -2.645751, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, -8.247185, 0.8728647, -0.2683341, -0.5490191, -0.526106, -0.526106, 0.2414814, 1.377964, 1.121188, 1, 2, 1e-07, 2, 5, lm(formula = mpg \\textasciitilde{} wt, data = .), mpg \\textasciitilde{} wt, 21, 21, 21.4, 18.1, 19.2, 17.8, 19.7, 2.62, 2.875, 3.215, 3.46, 3.44, 3.44, 2.77 & wt & -2.780106 & 1.3349173 & -2.082605 & 9.175766e-02\\\\\n", "\t 4 & 22.800, 24.400, 22.800, 32.400, 30.400, 33.900, 21.500, 27.300, 26.000, 30.400, 21.400, 108.000, 146.700, 140.800, 78.700, 75.700, 71.100, 120.100, 79.000, 120.300, 95.100, 121.000, 93.000, 62.000, 95.000, 66.000, 52.000, 65.000, 97.000, 66.000, 91.000, 113.000, 109.000, 3.850, 3.690, 3.920, 4.080, 4.930, 4.220, 3.700, 4.080, 4.430, 3.770, 4.110, 2.320, 3.190, 3.150, 2.200, 1.615, 1.835, 2.465, 1.935, 2.140, 1.513, 2.780, 18.610, 20.000, 22.900, 19.470, 18.520, 19.900, 20.010, 18.900, 16.700, 16.900, 18.600, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 0.000, 0.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 1.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 3.000, 4.000, 5.000, 5.000, 4.000, 1.000, 2.000, 2.000, 1.000, 2.000, 1.000, 1.000, 1.000, 2.000, 2.000, 2.000 & 39.5712, -5.647025, -3.670097, 2.842815, 1.016934, 5.25226, -0.05125022, 4.691095, -4.151279, -1.344202, -1.486562, -0.6272468, -2.472466, -88.43328, 10.17096, 0.6947654, 6.230721, 1.728126, 6.169273, -3.535624, -0.00293297, -0.4259551, 1.291776, -2.288073, 2, 26.4701, 21.55719, 21.78307, 27.14774, 30.45125, 29.2089, 25.65128, 28.6442, 27.48656, 31.02725, 23.87247, 0, 1, -3.316625, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, -7.5809, -1.801119, 0.4754451, -0.05200489, -0.376803, -0.2546567, 0.0951259, -0.1991357, -0.08531752, -0.4334345, 0.2700172, 1.301511, 1.497654, 1, 2, 1e-07, 2, 9, lm(formula = mpg \\textasciitilde{} wt, data = .), mpg \\textasciitilde{} wt, 22.8, 24.4, 22.8, 32.4, 30.4, 33.9, 21.5, 27.3, 26, 30.4, 21.4, 2.32, 3.19, 3.15, 2.2, 1.615, 1.835, 2.465, 1.935, 2.14, 1.513, 2.78 & (Intercept) & 39.571196 & 4.3465820 & 9.103980 & 7.771511e-06\\\\\n", "\t 4 & 22.800, 24.400, 22.800, 32.400, 30.400, 33.900, 21.500, 27.300, 26.000, 30.400, 21.400, 108.000, 146.700, 140.800, 78.700, 75.700, 71.100, 120.100, 79.000, 120.300, 95.100, 121.000, 93.000, 62.000, 95.000, 66.000, 52.000, 65.000, 97.000, 66.000, 91.000, 113.000, 109.000, 3.850, 3.690, 3.920, 4.080, 4.930, 4.220, 3.700, 4.080, 4.430, 3.770, 4.110, 2.320, 3.190, 3.150, 2.200, 1.615, 1.835, 2.465, 1.935, 2.140, 1.513, 2.780, 18.610, 20.000, 22.900, 19.470, 18.520, 19.900, 20.010, 18.900, 16.700, 16.900, 18.600, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 0.000, 0.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 1.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 3.000, 4.000, 5.000, 5.000, 4.000, 1.000, 2.000, 2.000, 1.000, 2.000, 1.000, 1.000, 1.000, 2.000, 2.000, 2.000 & 39.5712, -5.647025, -3.670097, 2.842815, 1.016934, 5.25226, -0.05125022, 4.691095, -4.151279, -1.344202, -1.486562, -0.6272468, -2.472466, -88.43328, 10.17096, 0.6947654, 6.230721, 1.728126, 6.169273, -3.535624, -0.00293297, -0.4259551, 1.291776, -2.288073, 2, 26.4701, 21.55719, 21.78307, 27.14774, 30.45125, 29.2089, 25.65128, 28.6442, 27.48656, 31.02725, 23.87247, 0, 1, -3.316625, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, -7.5809, -1.801119, 0.4754451, -0.05200489, -0.376803, -0.2546567, 0.0951259, -0.1991357, -0.08531752, -0.4334345, 0.2700172, 1.301511, 1.497654, 1, 2, 1e-07, 2, 9, lm(formula = mpg \\textasciitilde{} wt, data = .), mpg \\textasciitilde{} wt, 22.8, 24.4, 22.8, 32.4, 30.4, 33.9, 21.5, 27.3, 26, 30.4, 21.4, 2.32, 3.19, 3.15, 2.2, 1.615, 1.835, 2.465, 1.935, 2.14, 1.513, 2.78 & wt & -5.647025 & 1.8501185 & -3.052251 & 1.374278e-02\\\\\n", "\t 8 & 18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000 & 23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg \\textasciitilde{} wt, data = .), mpg \\textasciitilde{} wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57 & (Intercept) & 23.868029 & 3.0054619 & 7.941551 & 4.052705e-06\\\\\n", "\t 8 & 18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000 & 23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg \\textasciitilde{} wt, data = .), mpg \\textasciitilde{} wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57 & wt & -2.192438 & 0.7392393 & -2.965803 & 1.179281e-02\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A grouped_df: 6 × 8\n", "\n", "| cyl <dbl> | data <list> | model <list> | term <chr> | estimate <dbl> | std.error <dbl> | statistic <dbl> | p.value <dbl> |\n", "|---|---|---|---|---|---|---|---|\n", "| 6 | 21.000, 21.000, 21.400, 18.100, 19.200, 17.800, 19.700, 160.000, 160.000, 258.000, 225.000, 167.600, 167.600, 145.000, 110.000, 110.000, 110.000, 105.000, 123.000, 123.000, 175.000, 3.900, 3.900, 3.080, 2.760, 3.920, 3.920, 3.620, 2.620, 2.875, 3.215, 3.460, 3.440, 3.440, 2.770, 16.460, 17.020, 19.440, 20.220, 18.300, 18.900, 15.500, 0.000, 0.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 0.000, 0.000, 0.000, 0.000, 1.000, 4.000, 4.000, 3.000, 3.000, 4.000, 4.000, 5.000, 4.000, 4.000, 1.000, 1.000, 4.000, 4.000, 6.000 | 28.40884, -2.780106, -0.124967, 0.5839601, 1.929196, -0.689678, 0.3547199, -1.04528, -1.007951, -52.23469, -2.426656, 2.111436, -0.3526643, 0.679099, -0.720901, -1.10683, 2, 21.12497, 20.41604, 19.4708, 18.78968, 18.84528, 18.84528, 20.70795, 0, 1, -2.645751, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, -8.247185, 0.8728647, -0.2683341, -0.5490191, -0.526106, -0.526106, 0.2414814, 1.377964, 1.121188, 1, 2, 1e-07, 2, 5, lm(formula = mpg ~ wt, data = .), mpg ~ wt, 21, 21, 21.4, 18.1, 19.2, 17.8, 19.7, 2.62, 2.875, 3.215, 3.46, 3.44, 3.44, 2.77 | (Intercept) | 28.408845 | 4.1843688 | 6.789278 | 1.054844e-03 |\n", "| 6 | 21.000, 21.000, 21.400, 18.100, 19.200, 17.800, 19.700, 160.000, 160.000, 258.000, 225.000, 167.600, 167.600, 145.000, 110.000, 110.000, 110.000, 105.000, 123.000, 123.000, 175.000, 3.900, 3.900, 3.080, 2.760, 3.920, 3.920, 3.620, 2.620, 2.875, 3.215, 3.460, 3.440, 3.440, 2.770, 16.460, 17.020, 19.440, 20.220, 18.300, 18.900, 15.500, 0.000, 0.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 0.000, 0.000, 0.000, 0.000, 1.000, 4.000, 4.000, 3.000, 3.000, 4.000, 4.000, 5.000, 4.000, 4.000, 1.000, 1.000, 4.000, 4.000, 6.000 | 28.40884, -2.780106, -0.124967, 0.5839601, 1.929196, -0.689678, 0.3547199, -1.04528, -1.007951, -52.23469, -2.426656, 2.111436, -0.3526643, 0.679099, -0.720901, -1.10683, 2, 21.12497, 20.41604, 19.4708, 18.78968, 18.84528, 18.84528, 20.70795, 0, 1, -2.645751, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, -8.247185, 0.8728647, -0.2683341, -0.5490191, -0.526106, -0.526106, 0.2414814, 1.377964, 1.121188, 1, 2, 1e-07, 2, 5, lm(formula = mpg ~ wt, data = .), mpg ~ wt, 21, 21, 21.4, 18.1, 19.2, 17.8, 19.7, 2.62, 2.875, 3.215, 3.46, 3.44, 3.44, 2.77 | wt | -2.780106 | 1.3349173 | -2.082605 | 9.175766e-02 |\n", "| 4 | 22.800, 24.400, 22.800, 32.400, 30.400, 33.900, 21.500, 27.300, 26.000, 30.400, 21.400, 108.000, 146.700, 140.800, 78.700, 75.700, 71.100, 120.100, 79.000, 120.300, 95.100, 121.000, 93.000, 62.000, 95.000, 66.000, 52.000, 65.000, 97.000, 66.000, 91.000, 113.000, 109.000, 3.850, 3.690, 3.920, 4.080, 4.930, 4.220, 3.700, 4.080, 4.430, 3.770, 4.110, 2.320, 3.190, 3.150, 2.200, 1.615, 1.835, 2.465, 1.935, 2.140, 1.513, 2.780, 18.610, 20.000, 22.900, 19.470, 18.520, 19.900, 20.010, 18.900, 16.700, 16.900, 18.600, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 0.000, 0.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 1.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 3.000, 4.000, 5.000, 5.000, 4.000, 1.000, 2.000, 2.000, 1.000, 2.000, 1.000, 1.000, 1.000, 2.000, 2.000, 2.000 | 39.5712, -5.647025, -3.670097, 2.842815, 1.016934, 5.25226, -0.05125022, 4.691095, -4.151279, -1.344202, -1.486562, -0.6272468, -2.472466, -88.43328, 10.17096, 0.6947654, 6.230721, 1.728126, 6.169273, -3.535624, -0.00293297, -0.4259551, 1.291776, -2.288073, 2, 26.4701, 21.55719, 21.78307, 27.14774, 30.45125, 29.2089, 25.65128, 28.6442, 27.48656, 31.02725, 23.87247, 0, 1, -3.316625, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, -7.5809, -1.801119, 0.4754451, -0.05200489, -0.376803, -0.2546567, 0.0951259, -0.1991357, -0.08531752, -0.4334345, 0.2700172, 1.301511, 1.497654, 1, 2, 1e-07, 2, 9, lm(formula = mpg ~ wt, data = .), mpg ~ wt, 22.8, 24.4, 22.8, 32.4, 30.4, 33.9, 21.5, 27.3, 26, 30.4, 21.4, 2.32, 3.19, 3.15, 2.2, 1.615, 1.835, 2.465, 1.935, 2.14, 1.513, 2.78 | (Intercept) | 39.571196 | 4.3465820 | 9.103980 | 7.771511e-06 |\n", "| 4 | 22.800, 24.400, 22.800, 32.400, 30.400, 33.900, 21.500, 27.300, 26.000, 30.400, 21.400, 108.000, 146.700, 140.800, 78.700, 75.700, 71.100, 120.100, 79.000, 120.300, 95.100, 121.000, 93.000, 62.000, 95.000, 66.000, 52.000, 65.000, 97.000, 66.000, 91.000, 113.000, 109.000, 3.850, 3.690, 3.920, 4.080, 4.930, 4.220, 3.700, 4.080, 4.430, 3.770, 4.110, 2.320, 3.190, 3.150, 2.200, 1.615, 1.835, 2.465, 1.935, 2.140, 1.513, 2.780, 18.610, 20.000, 22.900, 19.470, 18.520, 19.900, 20.010, 18.900, 16.700, 16.900, 18.600, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 0.000, 0.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 1.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 3.000, 4.000, 5.000, 5.000, 4.000, 1.000, 2.000, 2.000, 1.000, 2.000, 1.000, 1.000, 1.000, 2.000, 2.000, 2.000 | 39.5712, -5.647025, -3.670097, 2.842815, 1.016934, 5.25226, -0.05125022, 4.691095, -4.151279, -1.344202, -1.486562, -0.6272468, -2.472466, -88.43328, 10.17096, 0.6947654, 6.230721, 1.728126, 6.169273, -3.535624, -0.00293297, -0.4259551, 1.291776, -2.288073, 2, 26.4701, 21.55719, 21.78307, 27.14774, 30.45125, 29.2089, 25.65128, 28.6442, 27.48656, 31.02725, 23.87247, 0, 1, -3.316625, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, -7.5809, -1.801119, 0.4754451, -0.05200489, -0.376803, -0.2546567, 0.0951259, -0.1991357, -0.08531752, -0.4334345, 0.2700172, 1.301511, 1.497654, 1, 2, 1e-07, 2, 9, lm(formula = mpg ~ wt, data = .), mpg ~ wt, 22.8, 24.4, 22.8, 32.4, 30.4, 33.9, 21.5, 27.3, 26, 30.4, 21.4, 2.32, 3.19, 3.15, 2.2, 1.615, 1.835, 2.465, 1.935, 2.14, 1.513, 2.78 | wt | -5.647025 | 1.8501185 | -3.052251 | 1.374278e-02 |\n", "| 8 | 18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000 | 23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = .), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57 | (Intercept) | 23.868029 | 3.0054619 | 7.941551 | 4.052705e-06 |\n", "| 8 | 18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000 | 23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = .), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57 | wt | -2.192438 | 0.7392393 | -2.965803 | 1.179281e-02 |\n", "\n" ], "text/plain": [ " cyl\n", "1 6 \n", "2 6 \n", "3 4 \n", "4 4 \n", "5 8 \n", "6 8 \n", " data \n", "1 21.000, 21.000, 21.400, 18.100, 19.200, 17.800, 19.700, 160.000, 160.000, 258.000, 225.000, 167.600, 167.600, 145.000, 110.000, 110.000, 110.000, 105.000, 123.000, 123.000, 175.000, 3.900, 3.900, 3.080, 2.760, 3.920, 3.920, 3.620, 2.620, 2.875, 3.215, 3.460, 3.440, 3.440, 2.770, 16.460, 17.020, 19.440, 20.220, 18.300, 18.900, 15.500, 0.000, 0.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 0.000, 0.000, 0.000, 0.000, 1.000, 4.000, 4.000, 3.000, 3.000, 4.000, 4.000, 5.000, 4.000, 4.000, 1.000, 1.000, 4.000, 4.000, 6.000 \n", "2 21.000, 21.000, 21.400, 18.100, 19.200, 17.800, 19.700, 160.000, 160.000, 258.000, 225.000, 167.600, 167.600, 145.000, 110.000, 110.000, 110.000, 105.000, 123.000, 123.000, 175.000, 3.900, 3.900, 3.080, 2.760, 3.920, 3.920, 3.620, 2.620, 2.875, 3.215, 3.460, 3.440, 3.440, 2.770, 16.460, 17.020, 19.440, 20.220, 18.300, 18.900, 15.500, 0.000, 0.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 0.000, 0.000, 0.000, 0.000, 1.000, 4.000, 4.000, 3.000, 3.000, 4.000, 4.000, 5.000, 4.000, 4.000, 1.000, 1.000, 4.000, 4.000, 6.000 \n", "3 22.800, 24.400, 22.800, 32.400, 30.400, 33.900, 21.500, 27.300, 26.000, 30.400, 21.400, 108.000, 146.700, 140.800, 78.700, 75.700, 71.100, 120.100, 79.000, 120.300, 95.100, 121.000, 93.000, 62.000, 95.000, 66.000, 52.000, 65.000, 97.000, 66.000, 91.000, 113.000, 109.000, 3.850, 3.690, 3.920, 4.080, 4.930, 4.220, 3.700, 4.080, 4.430, 3.770, 4.110, 2.320, 3.190, 3.150, 2.200, 1.615, 1.835, 2.465, 1.935, 2.140, 1.513, 2.780, 18.610, 20.000, 22.900, 19.470, 18.520, 19.900, 20.010, 18.900, 16.700, 16.900, 18.600, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 0.000, 0.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 1.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 3.000, 4.000, 5.000, 5.000, 4.000, 1.000, 2.000, 2.000, 1.000, 2.000, 1.000, 1.000, 1.000, 2.000, 2.000, 2.000 \n", "4 22.800, 24.400, 22.800, 32.400, 30.400, 33.900, 21.500, 27.300, 26.000, 30.400, 21.400, 108.000, 146.700, 140.800, 78.700, 75.700, 71.100, 120.100, 79.000, 120.300, 95.100, 121.000, 93.000, 62.000, 95.000, 66.000, 52.000, 65.000, 97.000, 66.000, 91.000, 113.000, 109.000, 3.850, 3.690, 3.920, 4.080, 4.930, 4.220, 3.700, 4.080, 4.430, 3.770, 4.110, 2.320, 3.190, 3.150, 2.200, 1.615, 1.835, 2.465, 1.935, 2.140, 1.513, 2.780, 18.610, 20.000, 22.900, 19.470, 18.520, 19.900, 20.010, 18.900, 16.700, 16.900, 18.600, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 0.000, 0.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 1.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 3.000, 4.000, 5.000, 5.000, 4.000, 1.000, 2.000, 2.000, 1.000, 2.000, 1.000, 1.000, 1.000, 2.000, 2.000, 2.000 \n", "5 18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000\n", "6 18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000\n", " model \n", "1 28.40884, -2.780106, -0.124967, 0.5839601, 1.929196, -0.689678, 0.3547199, -1.04528, -1.007951, -52.23469, -2.426656, 2.111436, -0.3526643, 0.679099, -0.720901, -1.10683, 2, 21.12497, 20.41604, 19.4708, 18.78968, 18.84528, 18.84528, 20.70795, 0, 1, -2.645751, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, -8.247185, 0.8728647, -0.2683341, -0.5490191, -0.526106, -0.526106, 0.2414814, 1.377964, 1.121188, 1, 2, 1e-07, 2, 5, lm(formula = mpg ~ wt, data = .), mpg ~ wt, 21, 21, 21.4, 18.1, 19.2, 17.8, 19.7, 2.62, 2.875, 3.215, 3.46, 3.44, 3.44, 2.77 \n", "2 28.40884, -2.780106, -0.124967, 0.5839601, 1.929196, -0.689678, 0.3547199, -1.04528, -1.007951, -52.23469, -2.426656, 2.111436, -0.3526643, 0.679099, -0.720901, -1.10683, 2, 21.12497, 20.41604, 19.4708, 18.78968, 18.84528, 18.84528, 20.70795, 0, 1, -2.645751, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, -8.247185, 0.8728647, -0.2683341, -0.5490191, -0.526106, -0.526106, 0.2414814, 1.377964, 1.121188, 1, 2, 1e-07, 2, 5, lm(formula = mpg ~ wt, data = .), mpg ~ wt, 21, 21, 21.4, 18.1, 19.2, 17.8, 19.7, 2.62, 2.875, 3.215, 3.46, 3.44, 3.44, 2.77 \n", "3 39.5712, -5.647025, -3.670097, 2.842815, 1.016934, 5.25226, -0.05125022, 4.691095, -4.151279, -1.344202, -1.486562, -0.6272468, -2.472466, -88.43328, 10.17096, 0.6947654, 6.230721, 1.728126, 6.169273, -3.535624, -0.00293297, -0.4259551, 1.291776, -2.288073, 2, 26.4701, 21.55719, 21.78307, 27.14774, 30.45125, 29.2089, 25.65128, 28.6442, 27.48656, 31.02725, 23.87247, 0, 1, -3.316625, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, -7.5809, -1.801119, 0.4754451, -0.05200489, -0.376803, -0.2546567, 0.0951259, -0.1991357, -0.08531752, -0.4334345, 0.2700172, 1.301511, 1.497654, 1, 2, 1e-07, 2, 9, lm(formula = mpg ~ wt, data = .), mpg ~ wt, 22.8, 24.4, 22.8, 32.4, 30.4, 33.9, 21.5, 27.3, 26, 30.4, 21.4, 2.32, 3.19, 3.15, 2.2, 1.615, 1.835, 2.465, 1.935, 2.14, 1.513, 2.78 \n", "4 39.5712, -5.647025, -3.670097, 2.842815, 1.016934, 5.25226, -0.05125022, 4.691095, -4.151279, -1.344202, -1.486562, -0.6272468, -2.472466, -88.43328, 10.17096, 0.6947654, 6.230721, 1.728126, 6.169273, -3.535624, -0.00293297, -0.4259551, 1.291776, -2.288073, 2, 26.4701, 21.55719, 21.78307, 27.14774, 30.45125, 29.2089, 25.65128, 28.6442, 27.48656, 31.02725, 23.87247, 0, 1, -3.316625, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, -7.5809, -1.801119, 0.4754451, -0.05200489, -0.376803, -0.2546567, 0.0951259, -0.1991357, -0.08531752, -0.4334345, 0.2700172, 1.301511, 1.497654, 1, 2, 1e-07, 2, 9, lm(formula = mpg ~ wt, data = .), mpg ~ wt, 22.8, 24.4, 22.8, 32.4, 30.4, 33.9, 21.5, 27.3, 26, 30.4, 21.4, 2.32, 3.19, 3.15, 2.2, 1.615, 1.835, 2.465, 1.935, 2.14, 1.513, 2.78 \n", "5 23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = .), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57\n", "6 23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = .), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57\n", " term estimate std.error statistic p.value \n", "1 (Intercept) 28.408845 4.1843688 6.789278 1.054844e-03\n", "2 wt -2.780106 1.3349173 -2.082605 9.175766e-02\n", "3 (Intercept) 39.571196 4.3465820 9.103980 7.771511e-06\n", "4 wt -5.647025 1.8501185 -3.052251 1.374278e-02\n", "5 (Intercept) 23.868029 3.0054619 7.941551 4.052705e-06\n", "6 wt -2.192438 0.7392393 -2.965803 1.179281e-02" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mtcars %>% \n", " group_by(cyl) %>% \n", " nest() %>% \n", " mutate(model = purrr::map(data, ~ lm(mpg ~ wt, data = .))) %>% \n", " mutate(result = purrr::map(model, ~ broom::tidy(.))) %>% \n", " unnest(result)" ] }, { "cell_type": "code", "execution_count": 256, "id": "7be4e102", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 10 × 5
student1student2student3student4student5
<dbl><dbl><dbl><dbl><dbl>
23.4 1.2 4.9 10.8 16.2
22.4 11.2 11.9-23.2 21.2
-11.6 8.2 -0.1 15.8 1.2
-19.6 -3.8-21.1-24.2 -4.8
25.4-16.8 -8.1 -3.2 2.2
-20.6 -3.8 1.9 15.8 11.2
9.4-12.8 0.9 19.8 1.2
-21.6 1.2 13.9-15.2-25.8
1.4 -0.8 6.9 5.8-23.8
-8.6 16.2-11.1 -2.2 1.2
\n" ], "text/latex": [ "A tibble: 10 × 5\n", "\\begin{tabular}{lllll}\n", " student1 & student2 & student3 & student4 & student5\\\\\n", " & & & & \\\\\n", "\\hline\n", "\t 23.4 & 1.2 & 4.9 & 10.8 & 16.2\\\\\n", "\t 22.4 & 11.2 & 11.9 & -23.2 & 21.2\\\\\n", "\t -11.6 & 8.2 & -0.1 & 15.8 & 1.2\\\\\n", "\t -19.6 & -3.8 & -21.1 & -24.2 & -4.8\\\\\n", "\t 25.4 & -16.8 & -8.1 & -3.2 & 2.2\\\\\n", "\t -20.6 & -3.8 & 1.9 & 15.8 & 11.2\\\\\n", "\t 9.4 & -12.8 & 0.9 & 19.8 & 1.2\\\\\n", "\t -21.6 & 1.2 & 13.9 & -15.2 & -25.8\\\\\n", "\t 1.4 & -0.8 & 6.9 & 5.8 & -23.8\\\\\n", "\t -8.6 & 16.2 & -11.1 & -2.2 & 1.2\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 10 × 5\n", "\n", "| student1 <dbl> | student2 <dbl> | student3 <dbl> | student4 <dbl> | student5 <dbl> |\n", "|---|---|---|---|---|\n", "| 23.4 | 1.2 | 4.9 | 10.8 | 16.2 |\n", "| 22.4 | 11.2 | 11.9 | -23.2 | 21.2 |\n", "| -11.6 | 8.2 | -0.1 | 15.8 | 1.2 |\n", "| -19.6 | -3.8 | -21.1 | -24.2 | -4.8 |\n", "| 25.4 | -16.8 | -8.1 | -3.2 | 2.2 |\n", "| -20.6 | -3.8 | 1.9 | 15.8 | 11.2 |\n", "| 9.4 | -12.8 | 0.9 | 19.8 | 1.2 |\n", "| -21.6 | 1.2 | 13.9 | -15.2 | -25.8 |\n", "| 1.4 | -0.8 | 6.9 | 5.8 | -23.8 |\n", "| -8.6 | 16.2 | -11.1 | -2.2 | 1.2 |\n", "\n" ], "text/plain": [ " student1 student2 student3 student4 student5\n", "1 23.4 1.2 4.9 10.8 16.2 \n", "2 22.4 11.2 11.9 -23.2 21.2 \n", "3 -11.6 8.2 -0.1 15.8 1.2 \n", "4 -19.6 -3.8 -21.1 -24.2 -4.8 \n", "5 25.4 -16.8 -8.1 -3.2 2.2 \n", "6 -20.6 -3.8 1.9 15.8 11.2 \n", "7 9.4 -12.8 0.9 19.8 1.2 \n", "8 -21.6 1.2 13.9 -15.2 -25.8 \n", "9 1.4 -0.8 6.9 5.8 -23.8 \n", "10 -8.6 16.2 -11.1 -2.2 1.2 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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$student1
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  1. 23.4
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$student2
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  10. 16.2
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$student3
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  1. 4.90000000000001
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$student4
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    \n", "\t
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\n" ], "text/latex": [ "A tibble: 3 × 3\n", "\\begin{tabular}{lll}\n", " a & b & min\\\\\n", " & & \\\\\n", "\\hline\n", "\t 1 & 4 & 1\\\\\n", "\t 2 & 5 & 2\\\\\n", "\t 3 & 6 & 3\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 3 × 3\n", "\n", "| a <dbl> | b <dbl> | min <list> |\n", "|---|---|---|\n", "| 1 | 4 | 1 |\n", "| 2 | 5 | 2 |\n", "| 3 | 6 | 3 |\n", "\n" ], "text/plain": [ " a b min\n", "1 1 4 1 \n", "2 2 5 2 \n", "3 3 6 3 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df <- tibble(\n", " a = c(1, 2, 3),\n", " b = c(4, 5, 6)\n", ")\n", "\n", "df %>% \n", " mutate(min = map2(a, b, ~min(.x, .y)))" ] }, { "cell_type": "markdown", "id": "945faf3d", "metadata": {}, "source": [ "也可以简写" ] }, { "cell_type": "code", "execution_count": 267, "id": "034d0b5b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
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\n" ], "text/latex": [ "A tibble: 3 × 3\n", "\\begin{tabular}{lll}\n", " a & b & min\\\\\n", " & & \\\\\n", "\\hline\n", "\t 1 & 4 & 1\\\\\n", "\t 2 & 5 & 2\\\\\n", "\t 3 & 6 & 3\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 3 × 3\n", "\n", "| a <dbl> | b <dbl> | min <dbl> |\n", "|---|---|---|\n", "| 1 | 4 | 1 |\n", "| 2 | 5 | 2 |\n", "| 3 | 6 | 3 |\n", "\n" ], "text/plain": [ " a b min\n", "1 1 4 1 \n", "2 2 5 2 \n", "3 3 6 3 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df %>% \n", " mutate(min = map2_dbl(a, b, min))" ] }, { "cell_type": "markdown", "id": "1b8e242e", "metadata": {}, "source": [ "`mutate()` 是`column-operation`，即提取数据框一列作为向量，传递到`mutate`中，`map2_dbl()`返回的也是一个等长的向量。\n", "\n", "因此，也可以用`rowwise()`逐行应用函数" ] }, { "cell_type": "code", "execution_count": 268, "id": "9f12a231", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 3 × 3
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\n" ], "text/latex": [ "A tibble: 3 × 3\n", "\\begin{tabular}{lll}\n", " a & b & min\\\\\n", " & & \\\\\n", "\\hline\n", "\t 1 & 4 & 1\\\\\n", "\t 2 & 5 & 2\\\\\n", "\t 3 & 6 & 3\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 3 × 3\n", "\n", "| a <dbl> | b <dbl> | min <dbl> |\n", "|---|---|---|\n", "| 1 | 4 | 1 |\n", "| 2 | 5 | 2 |\n", "| 3 | 6 | 3 |\n", "\n" ], "text/plain": [ " a b min\n", "1 1 4 1 \n", "2 2 5 2 \n", "3 3 6 3 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df %>% \n", " rowwise() %>% \n", " mutate(min = min(a, b)) %>% \n", " ungroup()" ] }, { "cell_type": "markdown", "id": "82dafad4", "metadata": {}, "source": [ "## `pmap()`\n", "没有`map3()`或者`map4()`函数，只有 `pmap()` 函数可用(`p` 的意思是 `parallel`)\n", "\n", "`purrr::pmap()`函数稍微有点不一样的地方:\n", "\n", "- `map()`和`map2()`函数，指定传递给函数`f`的向量，向量各自放在各自的位置上\n", "- `pmap()`需要将传递给函数的向量名，先装入一个`list()`中, 再传递给函数`f`![image.png](image/pmap-list.png)\n", "翻转列表的图示，参数的传递关系看地更清楚。![image-2.png](image/pmap-flipped.png)\n", "\n", "事实上，`map2()`是`pmap()`的一种特殊情况" ] }, { "cell_type": "code", "execution_count": 269, "id": "1a481589", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
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\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 1\n", "\\item 60\n", "\\item 40\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 1\n", "2. 60\n", "3. 40\n", "\n", "\n" ], "text/plain": [ "[1] 1 60 40" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tibble(\n", " a = c(50, 60, 70),\n", " b = c(10, 90, 40),\n", " c = c(1, 105, 200)\n", ") %>% \n", " pmap_dbl(min)" ] }, { "cell_type": "markdown", "id": "717f7c05", "metadata": {}, "source": [ "### 2 匿名函数\n", "`pmap()`可以接受多个向量，因此在`pmap()`中使用匿名函数，就需要一种新的方法来标识每个向量。\n", "\n", "由于向量是多个，因此不再用`.x`， `.y`，而是用`..1`, `..2`, `..3` 分别代表第一个向量、第二个向量和第三个向量。" ] }, { "cell_type": "code", "execution_count": 275, "id": "86594b3c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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"cell_type": "markdown", "id": "d600355e", "metadata": {}, "source": [ "## 其他`purrr`函数\n", "### 1 Map functions that output tibbles\n", "接着介绍`purrr`宏包的其他函数。 `map()`家族除了返回`list`和`atomic vector` 外，`map_df()`, `map_dfr()` 和`map_dfc()`还可以返回`tibble`。\n", "\n", "这个过程，好比生产线上的工人把输入的列表元素依次转换成一个个`tibble`，![image.png](image/map_df.png)\n", "最后归集一个大的`tibble`。在归集成一个大的`tibble`的时候，有两种方式，\n", "- 竖着堆积，`map_dfr()`(r for rows)![image-2.png](image/map_dfr.png)\n", "- 并排堆放`map_dfc()`(c for columns)![image-3.png](image/map_dfc.png)\n", "### 2 Walk and friends\n", "`walk()`函数与`map()`系列函数类似，但应用场景不同，`map()`在于执行函数操作，而`walk()` 保存记录数据（比如`print()`,`write.csv()`, `ggsave()`），常用于保存数据和生成图片。比如我们用`map()`生成系列图片，" ] }, { "cell_type": "code", "execution_count": 281, "id": "4592672f", "metadata": {}, "outputs": [ { "data": { "image/png": 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3eXTwIkZ+wAAMIQdgAAQQg7AIAghB0AQBDCDgAgCGEHABCEsAMACELYAQAE4QOK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\niLxnyV9LS0veI+Tvxhtv/OxnP/uRj3xkwYIFec/SFDo7O/MeIWe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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "plot_rnorm <- function(sd){\n", " tibble(x = rnorm(n = 5000, mean = 0, sd = sd)) %>% \n", " ggplot(aes(x)) +\n", " geom_histogram(bins = 40) +\n", " geom_vline(xintercept = 0, color = \"blue\")\n", "}\n", "\n", "plots <- \n", " c(5, 1, 9) %>% \n", " map(plot_rnorm)\n", "\n", "plots %>% \n", " walk(print)" ] }, { "cell_type": "markdown", "id": "974304f1", "metadata": {}, "source": [ "`map()`函数是一定要返回列表的，但`walk()`看上去函数没有返回值，实际上它返回的就是它的输入，只是用户不可见而已。![image.png](image/walk.png)\n", "这样的设计很有用，尤其在管道操作中，我们可以统计中，用`walk()`保存中间计算的结果或者生成图片，然后若无其事地继续管道（因为`walk()`返回值，就是输入`walk`的值），保持计算的连贯。" ] }, { "cell_type": "code", "execution_count": null, "id": "3151e54a", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "4.2.0" } }, "nbformat": 4, "nbformat_minor": 5 }