语法函数2-数据处理dplyr包

读取数据

• 在学习R语言过程中，除了使用内置的数据集外，我们更多的需要导入外部数据

数据科学中的文件管理

• 把项目所需的文件（代码、数据、图片等），放在一个文件夹里

• 读取文件

  • 事实上，R语言提供了很多读取数据的函数。下表列出了常见文件格式的读取方法

文件格式	R函数
txt	read.table()
.csv	read.csv() and readr::read_csv()
.xls and .xlsx	readxl::read_excel() and openxlsx::read.xlsx()
.sav(SPSS files)	haven::read_sav() and foreign::read.spss()
.Rdata or rda	load()
.rds	readRDS() and readr::read_rds()
.dta	haven::read_dta() and haven::read_stata()
.sas7bdat(SAS files)	haven::read_sas()
Internet	download.file()

here宏包

• 强大的here宏包，here()会告诉我们当前所在的目录

• 指向某个文件的路径信息

• here宏包的好处还在于，在不同的电脑和文件结构下，代码都能运行

here::here()

'/public/home/sll/mybook/content'

here::here("software", "smoove")
d <- read.table(file= "./data/txt_file.txt", header = TRUE)

'/public/home/sll/mybook/content/software/smoove'

Warning message in file(file, "rt"):
“无法打开文件'./data/txt_file.txt': 没有那个文件或目录”

Error in file(file, "rt"): 无法打开链结
Traceback:

1. read.table(file = "./data/txt_file.txt", header = TRUE)
2. file(file, "rt")

数据处理

• 数据处理的工具dplyr宏包–tidyverse里的“瑞士军刀”

• dplyr定义了数据处理的规范语法，其中主要包含以下10个主要的函数

  • mutate(), select(), rename() , filter()

  • summarise(), group_by(), arrange()

  • left_join(), right_join()， full_join()

• if_else() case_when() everything()函数

• count()

  • 对给定条件的某列进行计数，count(x, condition)相当于filter(x,condition) %>%  group_by(x) %>%  summarise(n=n())

• across()

  • 对给定的某些列赋予函数计算

  • across(.cols = everything(), .fns = NULL, ..., .names = NULL)

    • 用在 mutate() 和summarise() 函数里面

    • across() 对多列执行相同的函数操作，返回数据框

    • list()列出多个要使用的函数及对应列名

library(dplyr)

载入程辑包：‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union

df <- data.frame(
name=c("Alice", "Alice", "Bob", "Bob", "Carol", "Carol"),
type=c("english", "math", "english", "math", "english", "math"),
score=c(80, 60, 70, 69, 80, 90))
df

A data.frame: 6 × 3

name	type	score
<chr>	<chr>	<dbl>
Alice	english	80
Alice	math	60
Bob	english	70
Bob	math	69
Carol	english	80
Carol	math	90

1 新增一列 mutata()

• mutate()函数的功能是给数据框新增一列，使用语法为 mutate(.data = df, name = value)

  • 第一个参数.data，接受要处理的数据框，比如这里的df

  • 第二个参数是Name-value对, 比如extra = reward，

    • 等号左边，是我们为新增的一列取的名字，比如这里的extra，因为数据框每一列都是要有名字的；

    • 等号右边，是打算并入数据框的向量，比如这里的reward，它是装着学生成绩的向量。注意，向量的长度，

      • 要么与数据框的行数等长，比如这里向量长度为6；

      • 要么长度为1，即，新增的这一列所有的值都是一样的(循环补齐机制)。

• 因为mutate()函数处理的是数据框，并且固定放置在第一位置上（几乎所有dplyr的函数都是这样要求的），所以这个.data可以偷懒不写，直接写mutate(df, extra = reward)。另外，如果想同时新增多个列，只需要提供多个Name-value对即可

reward <- c(2, 5, 9, 8, 5, 6)
mutate(df, extra=reward)

A data.frame: 6 × 4

name	type	score	extra
<chr>	<chr>	<dbl>	<dbl>
Alice	english	80	2
Alice	math	60	5
Bob	english	70	9
Bob	math	69	8
Carol	english	80	5
Carol	math	90	6

# 新增多列
mutate(df,
      extra1 = c(2, 5, 9, 8, 5, 6),
      extra2 = c(1, 2, 3, 3, 2, 1),
      extra3 = c(8)
      )

A data.frame: 6 × 6

name	type	score	extra1	extra2	extra3
<chr>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>
Alice	english	80	2	1	8
Alice	math	60	5	2	8
Bob	english	70	9	3	8
Bob	math	69	8	3	8
Carol	english	80	5	2	8
Carol	math	90	6	1	8

2 管道 %>%

• Windows系统中可以通过Ctrl + Shift + M 快捷键产生 %>%

c(1:10) %>% sum()

55

• 这条语句的意思是f(x) 写成 x %>% f()，这里向量 c(1:10) 通过管道操作符 %>% ，传递到函数sum()的第一个参数位置，即sum(c(1:10))

• 

df %>% mutate(extra=reward)

A data.frame: 6 × 4

name	type	score	extra
<chr>	<chr>	<dbl>	<dbl>
Alice	english	80	2
Alice	math	60	5
Bob	english	70	9
Bob	math	69	8
Carol	english	80	5
Carol	math	90	6

3 向量函数与mutate()

• mutate()函数的本质还是向量函数和向量化操作，只不过是换作在数据框中完成，这样更能形成“数据框进、数据框出”的思维，方便快捷地构思并统计任务

• 在mutate()中引用数据框的某一列名，实际上是引用了列名对应的整个向量, 所以，这里我们传递score到calc_square()，就是把整个score向量传递给calc_square().

  • 

• 几何算符（这里是平方）是向量化的，因此calc_square()会对输入的score向量，返回一个等长的向量。

  • 

• mutate()拿到这个新的向量后，就在原有数据框中添加新的一列new_col

  • 

calc_square <- function(x){
    x^2
}
df %>% mutate(new_col = calc_square(score))

A data.frame: 6 × 4

name	type	score	new_col
<chr>	<chr>	<dbl>	<dbl>
Alice	english	80	6400
Alice	math	60	3600
Bob	english	70	4900
Bob	math	69	4761
Carol	english	80	6400
Carol	math	90	8100

4 保存为新的数据框

df_new <- df %>% 
  mutate(extra = reward) %>% 
  mutate(total = score + extra)
df_new

df_new2 <- df %>% 
  mutate(extra = reward,
        total = score + extra)
df_new2

A data.frame: 6 × 5

name	type	score	extra	total
<chr>	<chr>	<dbl>	<dbl>	<dbl>
Alice	english	80	2	82
Alice	math	60	5	65
Bob	english	70	9	79
Bob	math	69	8	77
Carol	english	80	5	85
Carol	math	90	6	96

A data.frame: 6 × 5

name	type	score	extra	total
<chr>	<chr>	<dbl>	<dbl>	<dbl>
Alice	english	80	2	82
Alice	math	60	5	65
Bob	english	70	9	79
Bob	math	69	8	77
Carol	english	80	5	85
Carol	math	90	6	96

5 选取列 select()

• select()顾名思义选择，就是选择数据框的某一列，或者某几列。

• 数据框进数据框出是dplyr函数的第二个特点

• 删除某列，可以在变量前面加 - 或者 !，两者的结果是一样的。

• 也可以通过位置索引进行选取

• 如果要选取数据框的列很多，我们也可以先观察列名的特征，用特定的函数进行选取

df_new %>% select(name, extra)

A data.frame: 6 × 2

name	extra
<chr>	<dbl>
Alice	2
Alice	5
Bob	9
Bob	8
Carol	5
Carol	6

df_new %>% select(-name)

A data.frame: 6 × 4

type	score	extra	total
<chr>	<dbl>	<dbl>	<dbl>
english	80	2	82
math	60	5	65
english	70	9	79
math	69	8	77
english	80	5	85
math	90	6	96

df_new %>% select(1,2,3)
df_new %>% select(1:3)

A data.frame: 6 × 3

name	type	score
<chr>	<chr>	<dbl>
Alice	english	80
Alice	math	60
Bob	english	70
Bob	math	69
Carol	english	80
Carol	math	90

A data.frame: 6 × 3

name	type	score
<chr>	<chr>	<dbl>
Alice	english	80
Alice	math	60
Bob	english	70
Bob	math	69
Carol	english	80
Carol	math	90

# 如果要选取数据框的列很多，我们也可以先观察列名的特征，用特定的函数进行选取
df_new %>% select(starts_with("s"))
df_new %>% select(ends_with("e"))
df_new %>% select(contains("score"))  # 列名包含
df_new %>% select(where(is.character)) # 通过变量的类型来选取
df_new %>% select(where(is.numeric))
df_new %>% select(where(is.numeric) & starts_with("t"))
df_new %>% select(!starts_with("s"))

A data.frame: 6 × 1

score
<dbl>
80
60
70
69
80
90

A data.frame: 6 × 3

name	type	score
<chr>	<chr>	<dbl>
Alice	english	80
Alice	math	60
Bob	english	70
Bob	math	69
Carol	english	80
Carol	math	90

A data.frame: 6 × 1

score
<dbl>
80
60
70
69
80
90

A data.frame: 6 × 2

name	type
<chr>	<chr>
Alice	english
Alice	math
Bob	english
Bob	math
Carol	english
Carol	math

A data.frame: 6 × 3

score	extra	total
<dbl>	<dbl>	<dbl>
80	2	82
60	5	65
70	9	79
69	8	77
80	5	85
90	6	96

A data.frame: 6 × 1

total
<dbl>
82
65
79
77
85
96

A data.frame: 6 × 4

name	type	extra	total
<chr>	<chr>	<dbl>	<dbl>
Alice	english	2	82
Alice	math	5	65
Bob	english	9	79
Bob	math	8	77
Carol	english	5	85
Carol	math	6	96

6 修改列名 rename()

• 用rename()修改列的名字, 具体方法是rename(.data, new_name = old_name)，和mutate()一样，等号左边是新的变量名，右边是已经存在的变量名（这是dplyr函数的第三个特征）

df_new %>% 
  select(name, type, total) %>% 
  rename(total_score = total)

A data.frame: 6 × 3

name	type	total_score
<chr>	<chr>	<dbl>
Alice	english	82
Alice	math	65
Bob	english	79
Bob	math	77
Carol	english	85
Carol	math	96

7 筛选filter()

• 前面select()是列方向的选择，而用filter()函数，我们可以对数据框行方向进行筛选，选出符合特定条件的某些行

• 

• 注意，这里filter()函数不是字面上“过滤掉”的意思，而是保留符合条件的行，也就说keep，不是drop的意思。

• R提供了其他比较关系的算符: <, >, <=, >=, == (equal), != (not equal), %in%, is.na() 和 !is.na() .

• 可以限定多个条件进行筛选，支持逻辑运算符

• 可以配合一些函数使用

filter()中的逻辑运算符

Operator	Meaning
==	Equal to
>	Greater than
<	Less than
>=	Greater than or equal to
<=	Less than or equal to
!=	Not equal to
%in%	in
is.na	is a missing value (NA)
!is.na	is not a missing value
&	and
|	or

df_new %>% filter(score > 70)

A data.frame: 3 × 5

name	type	score	extra	total
<chr>	<chr>	<dbl>	<dbl>	<dbl>
Alice	english	80	2	82
Carol	english	80	5	85
Carol	math	90	6	96

df_new %>% filter(type=="english" & score >= 75)
df_new %>% filter(type=="english", score >= 75)

A data.frame: 2 × 5

name	type	score	extra	total
<chr>	<chr>	<dbl>	<dbl>	<dbl>
Alice	english	80	2	82
Carol	english	80	5	85

A data.frame: 2 × 5

name	type	score	extra	total
<chr>	<chr>	<dbl>	<dbl>	<dbl>
Alice	english	80	2	82
Carol	english	80	5	85

df_new %>% filter(score == max(score))

A data.frame: 1 × 5

name	type	score	extra	total
<chr>	<chr>	<dbl>	<dbl>	<dbl>
Carol	math	90	6	96

8 统计汇总summarise()

• summarise()函数非常强大，主要用于统计汇总，往往与其他函数配合使用

df_new %>% summarise(mean_score = mean(score))
df_new %>% summarise(sd_score = sd(score))

A data.frame: 1 × 1

mean_score
<dbl>
74.83333

A data.frame: 1 × 1

sd_score
<dbl>
10.59088

df_new %>% summarise(
  mean_score = mean(score),
  median_score = median(score),
  n = n(),
  sum = sum(score))

A data.frame: 1 × 4

mean_score	median_score	n	sum
<dbl>	<dbl>	<int>	<dbl>
74.83333	75	6	449

9 分组统计 group_by()

• 实际运用中，summarise()函数往往配合group_by()一起使用，即，先分组再统计

df_new %>% 
  group_by(name) %>% 
  summarise(
    mean_score = mean(total),
    sd_score = sd(total))

A tibble: 3 × 3

name	mean_score	sd_score
<chr>	<dbl>	<dbl>
Alice	73.5	12.020815
Bob	78.0	1.414214
Carol	90.5	7.778175

10 排序 arrange()

• arrange()就是按照某个变量进行排序，默认为从小到大排序

• 在要排序的变量前加上-可改成从大到小排序

• 使用desc()函数也可实现从大到小排序

• 也可对多个变量依次排序

df_new %>% arrange(total)
df_new %>% arrange(-total)
df_new %>% arrange(desc(total))

A data.frame: 6 × 5

name	type	score	extra	total
<chr>	<chr>	<dbl>	<dbl>	<dbl>
Alice	math	60	5	65
Bob	math	69	8	77
Bob	english	70	9	79
Alice	english	80	2	82
Carol	english	80	5	85
Carol	math	90	6	96

A data.frame: 6 × 5

name	type	score	extra	total
<chr>	<chr>	<dbl>	<dbl>	<dbl>
Carol	math	90	6	96
Carol	english	80	5	85
Alice	english	80	2	82
Bob	english	70	9	79
Bob	math	69	8	77
Alice	math	60	5	65

A data.frame: 6 × 5

name	type	score	extra	total
<chr>	<chr>	<dbl>	<dbl>	<dbl>
Carol	math	90	6	96
Carol	english	80	5	85
Alice	english	80	2	82
Bob	english	70	9	79
Bob	math	69	8	77
Alice	math	60	5	65

df_new %>% 
  arrange(type, desc(total))

A data.frame: 6 × 5

name	type	score	extra	total
<chr>	<chr>	<dbl>	<dbl>	<dbl>
Carol	english	80	5	85
Alice	english	80	2	82
Bob	english	70	9	79
Carol	math	90	6	96
Bob	math	69	8	77
Alice	math	60	5	65

11 左联结 left_join()

• 实际操作中，会遇到数据框合并的情形

• left_join(df1, df2, by = "name"),by指定通过哪一列进行合并,合并按照df1进行

df1 <- df_new %>% 
  group_by(name) %>% 
  summarise(
    mean_score = mean(total)
  )
df1

df2 <- tibble(
       name = c("Alice", "Bob", "Dave"),
       age =  c(12, 13, 14)
)
df2
# 使用 left_join()函数 把两个数据框df1和df2合并连接在一起
left_join(df1, df2, by = "name")
df1 %>% left_join(df2, by = "name")

A tibble: 3 × 2

name	mean_score
<chr>	<dbl>
Alice	73.5
Bob	78.0
Carol	90.5

A tibble: 3 × 2

name	age
<chr>	<dbl>
Alice	12
Bob	13
Dave	14

A tibble: 3 × 3

name	mean_score	age
<chr>	<dbl>	<dbl>
Alice	73.5	12
Bob	78.0	13
Carol	90.5	NA

A tibble: 3 × 3

name	mean_score	age
<chr>	<dbl>	<dbl>
Alice	73.5	12
Bob	78.0	13
Carol	90.5	NA

12 右联结 right_join()

• right_join(df1, df2, by = "name"),by指定通过哪一列进行合并,合并按照df2进行，没有对应信息计为NA

df1 %>% right_join(df2, by = "name")

A tibble: 3 × 3

name	mean_score	age
<chr>	<dbl>	<dbl>
Alice	73.5	12
Bob	78.0	13
Dave	NA	14

13 满联结 full_join()

• 有时候，我们不想丢失项，可以使用full_join()，该函数确保条目是完整的，信息缺失的地方为NA

df1 %>% full_join(df2, by = "name")

A tibble: 4 × 3

name	mean_score	age
<chr>	<dbl>	<dbl>
Alice	73.5	12
Bob	78.0	13
Carol	90.5	NA
Dave	NA	14

14 内联结inner_join()

• 只保留name条目相同地记录

df1 %>% inner_join(df2, by = "name")

A tibble: 2 × 3

name	mean_score	age
<chr>	<dbl>	<dbl>
Alice	73.5	12
Bob	78.0	13

15 筛选联结 semi_join(x,y) anti_join(x,y)

• 筛选联结，有两个semi_join(x, y)和anti_join(x, y)，

• 函数不改变数据框x的变量的数量，主要影响的是x的观测，也就说会剔除一些行，其功能类似filter()

• 半联结semi_join(x, y)，保留name与y的name相一致的所有行，可以看作对x的筛选

• 反联结anti_join(x, y)，丢弃name与y的name相一致的所有行

df1 %>% semi_join(df2, by="name")
df1 %>% filter(
  name %in% df2$name)

A tibble: 2 × 2

name	mean_score
<chr>	<dbl>
Alice	73.5
Bob	78.0

A tibble: 2 × 2

name	mean_score
<chr>	<dbl>
Alice	73.5
Bob	78.0

df1 %>% anti_join(df2, by="name")
df1 %>% filter(
  ! name %in% df2$name)

A tibble: 1 × 2

name	mean_score
<chr>	<dbl>
Carol	90.5

A tibble: 1 × 2

name	mean_score
<chr>	<dbl>
Carol	90.5

df %>% 
  group_by(name) %>% 
  summarise(mean_score = mean(score))

df %>% 
  group_by(name) %>% 
  mutate(mean_score = mean(score))

A tibble: 3 × 2

name	mean_score
<chr>	<dbl>
Alice	70.0
Bob	69.5
Carol	85.0

A grouped_df: 6 × 4

name	type	score	mean_score
<chr>	<chr>	<dbl>	<dbl>
Alice	english	80	70.0
Alice	math	60	70.0
Bob	english	70	69.5
Bob	math	69	69.5
Carol	english	80	85.0
Carol	math	90	85.0

dplyr进阶

导入数据

library(tidyverse)
library(palmerpenguins)

── Attaching core tidyverse packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 2.0.0 ──
✔ forcats   1.0.0     ✔ readr     2.1.5
✔ ggplot2   3.5.0     ✔ stringr   1.5.1
✔ lubridate 1.9.3     ✔ tibble    3.2.1
✔ purrr     1.0.2     ✔ tidyr     1.3.1

── Conflicts ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

penguins <- penguins %>% drop_na() 

penguins %>% select(bill_length_mm, bill_depth_mm)
penguins %>% select(starts_with("bill_"))

A tibble: 333 × 2

bill_length_mm	bill_depth_mm
<dbl>	<dbl>
39.1	18.7
39.5	17.4
40.3	18.0
36.7	19.3
39.3	20.6
38.9	17.8
39.2	19.6
41.1	17.6
38.6	21.2
34.6	21.1
36.6	17.8
38.7	19.0
42.5	20.7
34.4	18.4
46.0	21.5
37.8	18.3
37.7	18.7
35.9	19.2
38.2	18.1
38.8	17.2
35.3	18.9
40.6	18.6
40.5	17.9
37.9	18.6
40.5	18.9
39.5	16.7
37.2	18.1
39.5	17.8
40.9	18.9
36.4	17.0
⋮	⋮
46.9	16.6
53.5	19.9
49.0	19.5
46.2	17.5
50.9	19.1
45.5	17.0
50.9	17.9
50.8	18.5
50.1	17.9
49.0	19.6
51.5	18.7
49.8	17.3
48.1	16.4
51.4	19.0
45.7	17.3
50.7	19.7
42.5	17.3
52.2	18.8
45.2	16.6
49.3	19.9
50.2	18.8
45.6	19.4
51.9	19.5
46.8	16.5
45.7	17.0
55.8	19.8
43.5	18.1
49.6	18.2
50.8	19.0
50.2	18.7

A tibble: 333 × 2

bill_length_mm	bill_depth_mm
<dbl>	<dbl>
39.1	18.7
39.5	17.4
40.3	18.0
36.7	19.3
39.3	20.6
38.9	17.8
39.2	19.6
41.1	17.6
38.6	21.2
34.6	21.1
36.6	17.8
38.7	19.0
42.5	20.7
34.4	18.4
46.0	21.5
37.8	18.3
37.7	18.7
35.9	19.2
38.2	18.1
38.8	17.2
35.3	18.9
40.6	18.6
40.5	17.9
37.9	18.6
40.5	18.9
39.5	16.7
37.2	18.1
39.5	17.8
40.9	18.9
36.4	17.0
⋮	⋮
46.9	16.6
53.5	19.9
49.0	19.5
46.2	17.5
50.9	19.1
45.5	17.0
50.9	17.9
50.8	18.5
50.1	17.9
49.0	19.6
51.5	18.7
49.8	17.3
48.1	16.4
51.4	19.0
45.7	17.3
50.7	19.7
42.5	17.3
52.2	18.8
45.2	16.6
49.3	19.9
50.2	18.8
45.6	19.4
51.9	19.5
46.8	16.5
45.7	17.0
55.8	19.8
43.5	18.1
49.6	18.2
50.8	19.0
50.2	18.7

penguins %>% select(where(is.numeric))
penguins %>% select(!where(is.character))

A tibble: 333 × 5

bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	year
<dbl>	<dbl>	<int>	<int>	<int>
39.1	18.7	181	3750	2007
39.5	17.4	186	3800	2007
40.3	18.0	195	3250	2007
36.7	19.3	193	3450	2007
39.3	20.6	190	3650	2007
38.9	17.8	181	3625	2007
39.2	19.6	195	4675	2007
41.1	17.6	182	3200	2007
38.6	21.2	191	3800	2007
34.6	21.1	198	4400	2007
36.6	17.8	185	3700	2007
38.7	19.0	195	3450	2007
42.5	20.7	197	4500	2007
34.4	18.4	184	3325	2007
46.0	21.5	194	4200	2007
37.8	18.3	174	3400	2007
37.7	18.7	180	3600	2007
35.9	19.2	189	3800	2007
38.2	18.1	185	3950	2007
38.8	17.2	180	3800	2007
35.3	18.9	187	3800	2007
40.6	18.6	183	3550	2007
40.5	17.9	187	3200	2007
37.9	18.6	172	3150	2007
40.5	18.9	180	3950	2007
39.5	16.7	178	3250	2007
37.2	18.1	178	3900	2007
39.5	17.8	188	3300	2007
40.9	18.9	184	3900	2007
36.4	17.0	195	3325	2007
⋮	⋮	⋮	⋮	⋮
46.9	16.6	192	2700	2008
53.5	19.9	205	4500	2008
49.0	19.5	210	3950	2008
46.2	17.5	187	3650	2008
50.9	19.1	196	3550	2008
45.5	17.0	196	3500	2008
50.9	17.9	196	3675	2009
50.8	18.5	201	4450	2009
50.1	17.9	190	3400	2009
49.0	19.6	212	4300	2009
51.5	18.7	187	3250	2009
49.8	17.3	198	3675	2009
48.1	16.4	199	3325	2009
51.4	19.0	201	3950	2009
45.7	17.3	193	3600	2009
50.7	19.7	203	4050	2009
42.5	17.3	187	3350	2009
52.2	18.8	197	3450	2009
45.2	16.6	191	3250	2009
49.3	19.9	203	4050	2009
50.2	18.8	202	3800	2009
45.6	19.4	194	3525	2009
51.9	19.5	206	3950	2009
46.8	16.5	189	3650	2009
45.7	17.0	195	3650	2009
55.8	19.8	207	4000	2009
43.5	18.1	202	3400	2009
49.6	18.2	193	3775	2009
50.8	19.0	210	4100	2009
50.2	18.7	198	3775	2009

A tibble: 333 × 8

species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
<fct>	<fct>	<dbl>	<dbl>	<int>	<int>	<fct>	<int>
Adelie	Torgersen	39.1	18.7	181	3750	male	2007
Adelie	Torgersen	39.5	17.4	186	3800	female	2007
Adelie	Torgersen	40.3	18.0	195	3250	female	2007
Adelie	Torgersen	36.7	19.3	193	3450	female	2007
Adelie	Torgersen	39.3	20.6	190	3650	male	2007
Adelie	Torgersen	38.9	17.8	181	3625	female	2007
Adelie	Torgersen	39.2	19.6	195	4675	male	2007
Adelie	Torgersen	41.1	17.6	182	3200	female	2007
Adelie	Torgersen	38.6	21.2	191	3800	male	2007
Adelie	Torgersen	34.6	21.1	198	4400	male	2007
Adelie	Torgersen	36.6	17.8	185	3700	female	2007
Adelie	Torgersen	38.7	19.0	195	3450	female	2007
Adelie	Torgersen	42.5	20.7	197	4500	male	2007
Adelie	Torgersen	34.4	18.4	184	3325	female	2007
Adelie	Torgersen	46.0	21.5	194	4200	male	2007
Adelie	Biscoe	37.8	18.3	174	3400	female	2007
Adelie	Biscoe	37.7	18.7	180	3600	male	2007
Adelie	Biscoe	35.9	19.2	189	3800	female	2007
Adelie	Biscoe	38.2	18.1	185	3950	male	2007
Adelie	Biscoe	38.8	17.2	180	3800	male	2007
Adelie	Biscoe	35.3	18.9	187	3800	female	2007
Adelie	Biscoe	40.6	18.6	183	3550	male	2007
Adelie	Biscoe	40.5	17.9	187	3200	female	2007
Adelie	Biscoe	37.9	18.6	172	3150	female	2007
Adelie	Biscoe	40.5	18.9	180	3950	male	2007
Adelie	Dream	39.5	16.7	178	3250	female	2007
Adelie	Dream	37.2	18.1	178	3900	male	2007
Adelie	Dream	39.5	17.8	188	3300	female	2007
Adelie	Dream	40.9	18.9	184	3900	male	2007
Adelie	Dream	36.4	17.0	195	3325	female	2007
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
Chinstrap	Dream	46.9	16.6	192	2700	female	2008
Chinstrap	Dream	53.5	19.9	205	4500	male	2008
Chinstrap	Dream	49.0	19.5	210	3950	male	2008
Chinstrap	Dream	46.2	17.5	187	3650	female	2008
Chinstrap	Dream	50.9	19.1	196	3550	male	2008
Chinstrap	Dream	45.5	17.0	196	3500	female	2008
Chinstrap	Dream	50.9	17.9	196	3675	female	2009
Chinstrap	Dream	50.8	18.5	201	4450	male	2009
Chinstrap	Dream	50.1	17.9	190	3400	female	2009
Chinstrap	Dream	49.0	19.6	212	4300	male	2009
Chinstrap	Dream	51.5	18.7	187	3250	male	2009
Chinstrap	Dream	49.8	17.3	198	3675	female	2009
Chinstrap	Dream	48.1	16.4	199	3325	female	2009
Chinstrap	Dream	51.4	19.0	201	3950	male	2009
Chinstrap	Dream	45.7	17.3	193	3600	female	2009
Chinstrap	Dream	50.7	19.7	203	4050	male	2009
Chinstrap	Dream	42.5	17.3	187	3350	female	2009
Chinstrap	Dream	52.2	18.8	197	3450	male	2009
Chinstrap	Dream	45.2	16.6	191	3250	female	2009
Chinstrap	Dream	49.3	19.9	203	4050	male	2009
Chinstrap	Dream	50.2	18.8	202	3800	male	2009
Chinstrap	Dream	45.6	19.4	194	3525	female	2009
Chinstrap	Dream	51.9	19.5	206	3950	male	2009
Chinstrap	Dream	46.8	16.5	189	3650	female	2009
Chinstrap	Dream	45.7	17.0	195	3650	female	2009
Chinstrap	Dream	55.8	19.8	207	4000	male	2009
Chinstrap	Dream	43.5	18.1	202	3400	female	2009
Chinstrap	Dream	49.6	18.2	193	3775	male	2009
Chinstrap	Dream	50.8	19.0	210	4100	male	2009
Chinstrap	Dream	50.2	18.7	198	3775	female	2009

# 多种组合选择
penguins %>% select(species, starts_with("bill_"))

A tibble: 333 × 3

species	bill_length_mm	bill_depth_mm
<fct>	<dbl>	<dbl>
Adelie	39.1	18.7
Adelie	39.5	17.4
Adelie	40.3	18.0
Adelie	36.7	19.3
Adelie	39.3	20.6
Adelie	38.9	17.8
Adelie	39.2	19.6
Adelie	41.1	17.6
Adelie	38.6	21.2
Adelie	34.6	21.1
Adelie	36.6	17.8
Adelie	38.7	19.0
Adelie	42.5	20.7
Adelie	34.4	18.4
Adelie	46.0	21.5
Adelie	37.8	18.3
Adelie	37.7	18.7
Adelie	35.9	19.2
Adelie	38.2	18.1
Adelie	38.8	17.2
Adelie	35.3	18.9
Adelie	40.6	18.6
Adelie	40.5	17.9
Adelie	37.9	18.6
Adelie	40.5	18.9
Adelie	39.5	16.7
Adelie	37.2	18.1
Adelie	39.5	17.8
Adelie	40.9	18.9
Adelie	36.4	17.0
⋮	⋮	⋮
Chinstrap	46.9	16.6
Chinstrap	53.5	19.9
Chinstrap	49.0	19.5
Chinstrap	46.2	17.5
Chinstrap	50.9	19.1
Chinstrap	45.5	17.0
Chinstrap	50.9	17.9
Chinstrap	50.8	18.5
Chinstrap	50.1	17.9
Chinstrap	49.0	19.6
Chinstrap	51.5	18.7
Chinstrap	49.8	17.3
Chinstrap	48.1	16.4
Chinstrap	51.4	19.0
Chinstrap	45.7	17.3
Chinstrap	50.7	19.7
Chinstrap	42.5	17.3
Chinstrap	52.2	18.8
Chinstrap	45.2	16.6
Chinstrap	49.3	19.9
Chinstrap	50.2	18.8
Chinstrap	45.6	19.4
Chinstrap	51.9	19.5
Chinstrap	46.8	16.5
Chinstrap	45.7	17.0
Chinstrap	55.8	19.8
Chinstrap	43.5	18.1
Chinstrap	49.6	18.2
Chinstrap	50.8	19.0
Chinstrap	50.2	18.7

## 返回向量
my_tibble[["x"]]
my_tibble$x
my_tibble %>% pull(x)

Error in eval(expr, envir, enclos): 找不到对象'my_tibble'
Traceback:

# 返回数据框
my_tibble["x"]
my_tibble %>% select(x)

Error in eval(expr, envir, enclos): 找不到对象'my_tibble'
Traceback:

tb <- tibble(
  x = 1:5,
  y = 0,
  z = 5:1,
  w = 0
)
tb

A tibble: 5 × 4

x	y	z	w
<int>	<dbl>	<int>	<dbl>
1	0	5	0
2	0	4	0
3	0	3	0
4	0	2	0
5	0	1	0

myfun <- function(x) sum(x) == 0

tb %>% select(where(myfun))

tb %>% select(where(~sum(.x) == 0))

A tibble: 5 × 2

y	w
<dbl>	<dbl>
0	0
0	0
0	0
0	0
0	0

A tibble: 5 × 2

y	w
<dbl>	<dbl>
0	0
0	0
0	0
0	0
0	0

df <- tibble(
  x = c(NA, NA, NA),
  y = c(2, 3, NA),
  z = c(NA, 5, NA)
)
df

df %>% select(where(~ !all(is.na(.x))))
df %>% filter(if_any(everything(), ~ !is.na(.x)))

A tibble: 3 × 3

x	y	z
<lgl>	<dbl>	<dbl>
NA	2	NA
NA	3	5
NA	NA	NA

A tibble: 3 × 2

y	z
<dbl>	<dbl>
2	NA
3	5
NA	NA

A tibble: 2 × 3

x	y	z
<lgl>	<dbl>	<dbl>
NA	2	NA
NA	3	5

penguins %>% filter(species %in% c("Adelie", "Gentoo"))
penguins %>% filter(species == "Adelie" & bill_length_mm > 40)

A tibble: 265 × 8

species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
<fct>	<fct>	<dbl>	<dbl>	<int>	<int>	<fct>	<int>
Adelie	Torgersen	39.1	18.7	181	3750	male	2007
Adelie	Torgersen	39.5	17.4	186	3800	female	2007
Adelie	Torgersen	40.3	18.0	195	3250	female	2007
Adelie	Torgersen	36.7	19.3	193	3450	female	2007
Adelie	Torgersen	39.3	20.6	190	3650	male	2007
Adelie	Torgersen	38.9	17.8	181	3625	female	2007
Adelie	Torgersen	39.2	19.6	195	4675	male	2007
Adelie	Torgersen	41.1	17.6	182	3200	female	2007
Adelie	Torgersen	38.6	21.2	191	3800	male	2007
Adelie	Torgersen	34.6	21.1	198	4400	male	2007
Adelie	Torgersen	36.6	17.8	185	3700	female	2007
Adelie	Torgersen	38.7	19.0	195	3450	female	2007
Adelie	Torgersen	42.5	20.7	197	4500	male	2007
Adelie	Torgersen	34.4	18.4	184	3325	female	2007
Adelie	Torgersen	46.0	21.5	194	4200	male	2007
Adelie	Biscoe	37.8	18.3	174	3400	female	2007
Adelie	Biscoe	37.7	18.7	180	3600	male	2007
Adelie	Biscoe	35.9	19.2	189	3800	female	2007
Adelie	Biscoe	38.2	18.1	185	3950	male	2007
Adelie	Biscoe	38.8	17.2	180	3800	male	2007
Adelie	Biscoe	35.3	18.9	187	3800	female	2007
Adelie	Biscoe	40.6	18.6	183	3550	male	2007
Adelie	Biscoe	40.5	17.9	187	3200	female	2007
Adelie	Biscoe	37.9	18.6	172	3150	female	2007
Adelie	Biscoe	40.5	18.9	180	3950	male	2007
Adelie	Dream	39.5	16.7	178	3250	female	2007
Adelie	Dream	37.2	18.1	178	3900	male	2007
Adelie	Dream	39.5	17.8	188	3300	female	2007
Adelie	Dream	40.9	18.9	184	3900	male	2007
Adelie	Dream	36.4	17.0	195	3325	female	2007
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
Gentoo	Biscoe	52.2	17.1	228	5400	male	2009
Gentoo	Biscoe	45.5	14.5	212	4750	female	2009
Gentoo	Biscoe	49.5	16.1	224	5650	male	2009
Gentoo	Biscoe	44.5	14.7	214	4850	female	2009
Gentoo	Biscoe	50.8	15.7	226	5200	male	2009
Gentoo	Biscoe	49.4	15.8	216	4925	male	2009
Gentoo	Biscoe	46.9	14.6	222	4875	female	2009
Gentoo	Biscoe	48.4	14.4	203	4625	female	2009
Gentoo	Biscoe	51.1	16.5	225	5250	male	2009
Gentoo	Biscoe	48.5	15.0	219	4850	female	2009
Gentoo	Biscoe	55.9	17.0	228	5600	male	2009
Gentoo	Biscoe	47.2	15.5	215	4975	female	2009
Gentoo	Biscoe	49.1	15.0	228	5500	male	2009
Gentoo	Biscoe	46.8	16.1	215	5500	male	2009
Gentoo	Biscoe	41.7	14.7	210	4700	female	2009
Gentoo	Biscoe	53.4	15.8	219	5500	male	2009
Gentoo	Biscoe	43.3	14.0	208	4575	female	2009
Gentoo	Biscoe	48.1	15.1	209	5500	male	2009
Gentoo	Biscoe	50.5	15.2	216	5000	female	2009
Gentoo	Biscoe	49.8	15.9	229	5950	male	2009
Gentoo	Biscoe	43.5	15.2	213	4650	female	2009
Gentoo	Biscoe	51.5	16.3	230	5500	male	2009
Gentoo	Biscoe	46.2	14.1	217	4375	female	2009
Gentoo	Biscoe	55.1	16.0	230	5850	male	2009
Gentoo	Biscoe	48.8	16.2	222	6000	male	2009
Gentoo	Biscoe	47.2	13.7	214	4925	female	2009
Gentoo	Biscoe	46.8	14.3	215	4850	female	2009
Gentoo	Biscoe	50.4	15.7	222	5750	male	2009
Gentoo	Biscoe	45.2	14.8	212	5200	female	2009
Gentoo	Biscoe	49.9	16.1	213	5400	male	2009

A tibble: 50 × 8

species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
<fct>	<fct>	<dbl>	<dbl>	<int>	<int>	<fct>	<int>
Adelie	Torgersen	40.3	18.0	195	3250	female	2007
Adelie	Torgersen	41.1	17.6	182	3200	female	2007
Adelie	Torgersen	42.5	20.7	197	4500	male	2007
Adelie	Torgersen	46.0	21.5	194	4200	male	2007
Adelie	Biscoe	40.6	18.6	183	3550	male	2007
Adelie	Biscoe	40.5	17.9	187	3200	female	2007
Adelie	Biscoe	40.5	18.9	180	3950	male	2007
Adelie	Dream	40.9	18.9	184	3900	male	2007
Adelie	Dream	42.2	18.5	180	3550	female	2007
Adelie	Dream	40.8	18.4	195	3900	male	2007
Adelie	Dream	44.1	19.7	196	4400	male	2007
Adelie	Dream	41.1	19.0	182	3425	male	2007
Adelie	Dream	42.3	21.2	191	4150	male	2007
Adelie	Biscoe	40.1	18.9	188	4300	male	2008
Adelie	Biscoe	42.0	19.5	200	4050	male	2008
Adelie	Biscoe	41.4	18.6	191	3700	male	2008
Adelie	Biscoe	40.6	18.8	193	3800	male	2008
Adelie	Biscoe	41.3	21.1	195	4400	male	2008
Adelie	Biscoe	41.1	18.2	192	4050	male	2008
Adelie	Biscoe	41.6	18.0	192	3950	male	2008
Adelie	Biscoe	41.1	19.1	188	4100	male	2008
Adelie	Torgersen	41.8	19.4	198	4450	male	2008
Adelie	Torgersen	45.8	18.9	197	4150	male	2008
Adelie	Torgersen	42.8	18.5	195	4250	male	2008
Adelie	Torgersen	40.9	16.8	191	3700	female	2008
Adelie	Torgersen	42.1	19.1	195	4000	male	2008
Adelie	Torgersen	42.9	17.6	196	4700	male	2008
Adelie	Dream	41.3	20.3	194	3550	male	2008
Adelie	Dream	41.1	18.1	205	4300	male	2008
Adelie	Dream	40.8	18.9	208	4300	male	2008
Adelie	Dream	40.3	18.5	196	4350	male	2008
Adelie	Dream	43.2	18.5	192	4100	male	2008
Adelie	Biscoe	41.0	20.0	203	4725	male	2009
Adelie	Biscoe	43.2	19.0	197	4775	male	2009
Adelie	Biscoe	45.6	20.3	191	4600	male	2009
Adelie	Biscoe	42.2	19.5	197	4275	male	2009
Adelie	Biscoe	42.7	18.3	196	4075	male	2009
Adelie	Torgersen	41.1	18.6	189	3325	male	2009
Adelie	Torgersen	40.2	17.0	176	3450	female	2009
Adelie	Torgersen	41.4	18.5	202	3875	male	2009
Adelie	Torgersen	40.6	19.0	199	4000	male	2009
Adelie	Torgersen	41.5	18.3	195	4300	male	2009
Adelie	Torgersen	44.1	18.0	210	4000	male	2009
Adelie	Torgersen	43.1	19.2	197	3500	male	2009
Adelie	Dream	41.1	17.5	190	3900	male	2009
Adelie	Dream	40.2	20.1	200	3975	male	2009
Adelie	Dream	40.2	17.1	193	3400	female	2009
Adelie	Dream	40.6	17.2	187	3475	male	2009
Adelie	Dream	40.7	17.0	190	3725	male	2009
Adelie	Dream	41.5	18.5	201	4000	male	2009

penguins %>%
  filter(species == "Adelie", bill_length_mm == max(bill_length_mm))

A tibble: 0 × 8

species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
<fct>	<fct>	<dbl>	<dbl>	<int>	<int>	<fct>	<int>

更多应用

• head()

• tail()

• slice() 取表格前几行，可与group_by()等其他函数连用

penguins %>% head()
penguins %>% tail()

A tibble: 6 × 8

species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
<fct>	<fct>	<dbl>	<dbl>	<int>	<int>	<fct>	<int>
Adelie	Torgersen	39.1	18.7	181	3750	male	2007
Adelie	Torgersen	39.5	17.4	186	3800	female	2007
Adelie	Torgersen	40.3	18.0	195	3250	female	2007
Adelie	Torgersen	36.7	19.3	193	3450	female	2007
Adelie	Torgersen	39.3	20.6	190	3650	male	2007
Adelie	Torgersen	38.9	17.8	181	3625	female	2007

A tibble: 6 × 8

species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
<fct>	<fct>	<dbl>	<dbl>	<int>	<int>	<fct>	<int>
Chinstrap	Dream	45.7	17.0	195	3650	female	2009
Chinstrap	Dream	55.8	19.8	207	4000	male	2009
Chinstrap	Dream	43.5	18.1	202	3400	female	2009
Chinstrap	Dream	49.6	18.2	193	3775	male	2009
Chinstrap	Dream	50.8	19.0	210	4100	male	2009
Chinstrap	Dream	50.2	18.7	198	3775	female	2009

penguins %>% slice(1)

A tibble: 1 × 8

species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
<fct>	<fct>	<dbl>	<dbl>	<int>	<int>	<fct>	<int>
Adelie	Torgersen	39.1	18.7	181	3750	male	2007

penguins %>% 
  group_by(species) %>% 
  slice(1)

A grouped_df: 3 × 8

species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
<fct>	<fct>	<dbl>	<dbl>	<int>	<int>	<fct>	<int>
Adelie	Torgersen	39.1	18.7	181	3750	male	2007
Chinstrap	Dream	46.5	17.9	192	3500	female	2007
Gentoo	Biscoe	46.1	13.2	211	4500	female	2007

penguins %>% filter(bill_length_mm == max(bill_length_mm))

penguins %>% 
  arrange(desc(bill_length_mm)) %>% 
  slice(1)

penguins %>% 
  slice_max(bill_length_mm)

A tibble: 1 × 8

species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
<fct>	<fct>	<dbl>	<dbl>	<int>	<int>	<fct>	<int>
Gentoo	Biscoe	59.6	17	230	6050	male	2007

A tibble: 1 × 8

species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
<fct>	<fct>	<dbl>	<dbl>	<int>	<int>	<fct>	<int>
Gentoo	Biscoe	59.6	17	230	6050	male	2007

A tibble: 1 × 8

species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
<fct>	<fct>	<dbl>	<dbl>	<int>	<int>	<fct>	<int>
Gentoo	Biscoe	59.6	17	230	6050	male	2007

separate

• 按照某个分隔符分开某一列

tb <- tibble::tribble(
  ~day, ~price,
  1,   "30-45",
  2,   "40-95",
  3,   "89-65",
  4,   "45-63",
  5,   "52-42"
)
tb
tb1 <- tb %>% 
  separate(price, into=c("low", "high"), sep="-")
tb1

A tibble: 5 × 2

day	price
<dbl>	<chr>
1	30-45
2	40-95
3	89-65
4	45-63
5	52-42

A tibble: 5 × 3

day	low	high
<dbl>	<chr>	<chr>
1	30	45
2	40	95
3	89	65
4	45	63
5	52	42

unite

• 将指定的列按照指定的连接符连接组成新的列

• remove=FALSE，不移除被连接的都列

tb1 %>% unite(col="prics", c(low, high), sep=":", remove=FALSE)
tb1 %>% unite("prics", c(low, high), sep=":")

A tibble: 5 × 4

day	prics	low	high
<dbl>	<chr>	<chr>	<chr>
1	30:45	30	45
2	40:95	40	95
3	89:65	89	65
4	45:63	45	63
5	52:42	52	42

A tibble: 5 × 2

day	prics
<dbl>	<chr>
1	30:45
2	40:95
3	89:65
4	45:63
5	52:42

distinct

• distinct()处理的对象是data.frame；

• 功能是筛选不重复的row；返回data.frame

• n_distinct()处理的对象是vector；功能是统计不同的元素有多少个；返回一个数值

df <- tibble::tribble(
  ~x, ~y, ~z,
  1, 1, 1,
  1, 1, 2,
  1, 1, 1,
  2, 1, 2,
  1, 1, 3,
  3, 3, 1)
df

A tibble: 6 × 3

x	y	z
<dbl>	<dbl>	<dbl>
1	1	1
1	1	2
1	1	1
2	1	2
1	1	3
3	3	1

df %>% distinct()
df %>% distinct(x)
df %>% distinct(x, y)
df %>% distinct(x, y, .keep_all = TRUE) # 只保留最先出现的row

A tibble: 5 × 3

x	y	z
<dbl>	<dbl>	<dbl>
1	1	1
1	1	2
2	1	2
1	1	3
3	3	1

A tibble: 3 × 1

x
<dbl>
1
2
3

A tibble: 3 × 2

x	y
<dbl>	<dbl>
1	1
2	1
3	3

A tibble: 3 × 3

x	y	z
<dbl>	<dbl>	<dbl>
1	1	1
2	1	2
3	3	1

df %>% distinct(
  across(c(x, y)),
  .keep_all = TRUE)

df %>%
  group_by(x) %>%
  distinct(y, .keep_all = TRUE)

A tibble: 3 × 3

x	y	z
<dbl>	<dbl>	<dbl>
1	1	1
2	1	2
3	3	1

A grouped_df: 3 × 3

x	y	z
<dbl>	<dbl>	<dbl>
1	1	1
2	1	2
3	3	1

df %>% group_by(x) %>% summarise(n = n_distinct(z))

A tibble: 3 × 2

x	n
<dbl>	<int>
1	3
2	1
3	1

有关NA的计算

• NA很讨厌，凡是它参与的四则运算，结果都是NA，

• 所以需要事先把它删除，增加参数说明 na.rm = TRUE

sum(c(1, 2, NA, 4))
sum(c(1, 2, NA, 4), na.rm=TRUE)

<NA>

7

penguins %>% 
  mutate(
    body = if_else(body_mass_g > 4200, "you are fat", "you are fine"))

A tibble: 333 × 9

species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year	body
<fct>	<fct>	<dbl>	<dbl>	<int>	<int>	<fct>	<int>	<chr>
Adelie	Torgersen	39.1	18.7	181	3750	male	2007	you are fine
Adelie	Torgersen	39.5	17.4	186	3800	female	2007	you are fine
Adelie	Torgersen	40.3	18.0	195	3250	female	2007	you are fine
Adelie	Torgersen	36.7	19.3	193	3450	female	2007	you are fine
Adelie	Torgersen	39.3	20.6	190	3650	male	2007	you are fine
Adelie	Torgersen	38.9	17.8	181	3625	female	2007	you are fine
Adelie	Torgersen	39.2	19.6	195	4675	male	2007	you are fat
Adelie	Torgersen	41.1	17.6	182	3200	female	2007	you are fine
Adelie	Torgersen	38.6	21.2	191	3800	male	2007	you are fine
Adelie	Torgersen	34.6	21.1	198	4400	male	2007	you are fat
Adelie	Torgersen	36.6	17.8	185	3700	female	2007	you are fine
Adelie	Torgersen	38.7	19.0	195	3450	female	2007	you are fine
Adelie	Torgersen	42.5	20.7	197	4500	male	2007	you are fat
Adelie	Torgersen	34.4	18.4	184	3325	female	2007	you are fine
Adelie	Torgersen	46.0	21.5	194	4200	male	2007	you are fine
Adelie	Biscoe	37.8	18.3	174	3400	female	2007	you are fine
Adelie	Biscoe	37.7	18.7	180	3600	male	2007	you are fine
Adelie	Biscoe	35.9	19.2	189	3800	female	2007	you are fine
Adelie	Biscoe	38.2	18.1	185	3950	male	2007	you are fine
Adelie	Biscoe	38.8	17.2	180	3800	male	2007	you are fine
Adelie	Biscoe	35.3	18.9	187	3800	female	2007	you are fine
Adelie	Biscoe	40.6	18.6	183	3550	male	2007	you are fine
Adelie	Biscoe	40.5	17.9	187	3200	female	2007	you are fine
Adelie	Biscoe	37.9	18.6	172	3150	female	2007	you are fine
Adelie	Biscoe	40.5	18.9	180	3950	male	2007	you are fine
Adelie	Dream	39.5	16.7	178	3250	female	2007	you are fine
Adelie	Dream	37.2	18.1	178	3900	male	2007	you are fine
Adelie	Dream	39.5	17.8	188	3300	female	2007	you are fine
Adelie	Dream	40.9	18.9	184	3900	male	2007	you are fine
Adelie	Dream	36.4	17.0	195	3325	female	2007	you are fine
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
Chinstrap	Dream	46.9	16.6	192	2700	female	2008	you are fine
Chinstrap	Dream	53.5	19.9	205	4500	male	2008	you are fat
Chinstrap	Dream	49.0	19.5	210	3950	male	2008	you are fine
Chinstrap	Dream	46.2	17.5	187	3650	female	2008	you are fine
Chinstrap	Dream	50.9	19.1	196	3550	male	2008	you are fine
Chinstrap	Dream	45.5	17.0	196	3500	female	2008	you are fine
Chinstrap	Dream	50.9	17.9	196	3675	female	2009	you are fine
Chinstrap	Dream	50.8	18.5	201	4450	male	2009	you are fat
Chinstrap	Dream	50.1	17.9	190	3400	female	2009	you are fine
Chinstrap	Dream	49.0	19.6	212	4300	male	2009	you are fat
Chinstrap	Dream	51.5	18.7	187	3250	male	2009	you are fine
Chinstrap	Dream	49.8	17.3	198	3675	female	2009	you are fine
Chinstrap	Dream	48.1	16.4	199	3325	female	2009	you are fine
Chinstrap	Dream	51.4	19.0	201	3950	male	2009	you are fine
Chinstrap	Dream	45.7	17.3	193	3600	female	2009	you are fine
Chinstrap	Dream	50.7	19.7	203	4050	male	2009	you are fine
Chinstrap	Dream	42.5	17.3	187	3350	female	2009	you are fine
Chinstrap	Dream	52.2	18.8	197	3450	male	2009	you are fine
Chinstrap	Dream	45.2	16.6	191	3250	female	2009	you are fine
Chinstrap	Dream	49.3	19.9	203	4050	male	2009	you are fine
Chinstrap	Dream	50.2	18.8	202	3800	male	2009	you are fine
Chinstrap	Dream	45.6	19.4	194	3525	female	2009	you are fine
Chinstrap	Dream	51.9	19.5	206	3950	male	2009	you are fine
Chinstrap	Dream	46.8	16.5	189	3650	female	2009	you are fine
Chinstrap	Dream	45.7	17.0	195	3650	female	2009	you are fine
Chinstrap	Dream	55.8	19.8	207	4000	male	2009	you are fine
Chinstrap	Dream	43.5	18.1	202	3400	female	2009	you are fine
Chinstrap	Dream	49.6	18.2	193	3775	male	2009	you are fine
Chinstrap	Dream	50.8	19.0	210	4100	male	2009	you are fine
Chinstrap	Dream	50.2	18.7	198	3775	female	2009	you are fine

df <- tibble::tribble(
    ~name,     ~type, ~score,
  "Alice", "english",    80,
  "Alice",    "math",    NA,
    "Bob", "english",    70,
    "Bob",    "math",    69,
  "Carol", "english",    NA,
  "Carol",    "math",    90
  )
df

A tibble: 6 × 3

name	type	score
<chr>	<chr>	<dbl>
Alice	english	80
Alice	math	NA
Bob	english	70
Bob	math	69
Carol	english	NA
Carol	math	90

df %>% 
  group_by(type) %>% 
  mutate(mean_score = mean(score, na.rm=TRUE)) %>% 
  mutate(new_score = if_else(is.na(score), mean_score, score))

A grouped_df: 6 × 5

name	type	score	mean_score	new_score
<chr>	<chr>	<dbl>	<dbl>	<dbl>
Alice	english	80	75.0	80.0
Alice	math	NA	79.5	79.5
Bob	english	70	75.0	70.0
Bob	math	69	79.5	69.0
Carol	english	NA	75.0	75.0
Carol	math	90	79.5	90.0

penguins %>% 
  mutate(
    body = case_when(
      body_mass_g < 3500   ~ "best",
      body_mass_g >= 3500 & body_mass_g < 4500  ~ "good",
      body_mass_g >= 4500 & body_mass_g < 5500 ~ "general",
      TRUE                                     ~ "other"))

A tibble: 333 × 9

species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year	body
<fct>	<fct>	<dbl>	<dbl>	<int>	<int>	<fct>	<int>	<chr>
Adelie	Torgersen	39.1	18.7	181	3750	male	2007	good
Adelie	Torgersen	39.5	17.4	186	3800	female	2007	good
Adelie	Torgersen	40.3	18.0	195	3250	female	2007	best
Adelie	Torgersen	36.7	19.3	193	3450	female	2007	best
Adelie	Torgersen	39.3	20.6	190	3650	male	2007	good
Adelie	Torgersen	38.9	17.8	181	3625	female	2007	good
Adelie	Torgersen	39.2	19.6	195	4675	male	2007	general
Adelie	Torgersen	41.1	17.6	182	3200	female	2007	best
Adelie	Torgersen	38.6	21.2	191	3800	male	2007	good
Adelie	Torgersen	34.6	21.1	198	4400	male	2007	good
Adelie	Torgersen	36.6	17.8	185	3700	female	2007	good
Adelie	Torgersen	38.7	19.0	195	3450	female	2007	best
Adelie	Torgersen	42.5	20.7	197	4500	male	2007	general
Adelie	Torgersen	34.4	18.4	184	3325	female	2007	best
Adelie	Torgersen	46.0	21.5	194	4200	male	2007	good
Adelie	Biscoe	37.8	18.3	174	3400	female	2007	best
Adelie	Biscoe	37.7	18.7	180	3600	male	2007	good
Adelie	Biscoe	35.9	19.2	189	3800	female	2007	good
Adelie	Biscoe	38.2	18.1	185	3950	male	2007	good
Adelie	Biscoe	38.8	17.2	180	3800	male	2007	good
Adelie	Biscoe	35.3	18.9	187	3800	female	2007	good
Adelie	Biscoe	40.6	18.6	183	3550	male	2007	good
Adelie	Biscoe	40.5	17.9	187	3200	female	2007	best
Adelie	Biscoe	37.9	18.6	172	3150	female	2007	best
Adelie	Biscoe	40.5	18.9	180	3950	male	2007	good
Adelie	Dream	39.5	16.7	178	3250	female	2007	best
Adelie	Dream	37.2	18.1	178	3900	male	2007	good
Adelie	Dream	39.5	17.8	188	3300	female	2007	best
Adelie	Dream	40.9	18.9	184	3900	male	2007	good
Adelie	Dream	36.4	17.0	195	3325	female	2007	best
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
Chinstrap	Dream	46.9	16.6	192	2700	female	2008	best
Chinstrap	Dream	53.5	19.9	205	4500	male	2008	general
Chinstrap	Dream	49.0	19.5	210	3950	male	2008	good
Chinstrap	Dream	46.2	17.5	187	3650	female	2008	good
Chinstrap	Dream	50.9	19.1	196	3550	male	2008	good
Chinstrap	Dream	45.5	17.0	196	3500	female	2008	good
Chinstrap	Dream	50.9	17.9	196	3675	female	2009	good
Chinstrap	Dream	50.8	18.5	201	4450	male	2009	good
Chinstrap	Dream	50.1	17.9	190	3400	female	2009	best
Chinstrap	Dream	49.0	19.6	212	4300	male	2009	good
Chinstrap	Dream	51.5	18.7	187	3250	male	2009	best
Chinstrap	Dream	49.8	17.3	198	3675	female	2009	good
Chinstrap	Dream	48.1	16.4	199	3325	female	2009	best
Chinstrap	Dream	51.4	19.0	201	3950	male	2009	good
Chinstrap	Dream	45.7	17.3	193	3600	female	2009	good
Chinstrap	Dream	50.7	19.7	203	4050	male	2009	good
Chinstrap	Dream	42.5	17.3	187	3350	female	2009	best
Chinstrap	Dream	52.2	18.8	197	3450	male	2009	best
Chinstrap	Dream	45.2	16.6	191	3250	female	2009	best
Chinstrap	Dream	49.3	19.9	203	4050	male	2009	good
Chinstrap	Dream	50.2	18.8	202	3800	male	2009	good
Chinstrap	Dream	45.6	19.4	194	3525	female	2009	good
Chinstrap	Dream	51.9	19.5	206	3950	male	2009	good
Chinstrap	Dream	46.8	16.5	189	3650	female	2009	good
Chinstrap	Dream	45.7	17.0	195	3650	female	2009	good
Chinstrap	Dream	55.8	19.8	207	4000	male	2009	good
Chinstrap	Dream	43.5	18.1	202	3400	female	2009	best
Chinstrap	Dream	49.6	18.2	193	3775	male	2009	good
Chinstrap	Dream	50.8	19.0	210	4100	male	2009	good
Chinstrap	Dream	50.2	18.7	198	3775	female	2009	good

penguins %>% 
  mutate(degree = case_when(
                  bill_length_mm < 35                        ~ "A",
      bill_length_mm >= 35 & bill_length_mm < 45 ~ "B",
      bill_length_mm >= 45 & bill_length_mm < 55 ~ "C",
      TRUE                                       ~ "D"))

A tibble: 333 × 9

species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year	degree
<fct>	<fct>	<dbl>	<dbl>	<int>	<int>	<fct>	<int>	<chr>
Adelie	Torgersen	39.1	18.7	181	3750	male	2007	B
Adelie	Torgersen	39.5	17.4	186	3800	female	2007	B
Adelie	Torgersen	40.3	18.0	195	3250	female	2007	B
Adelie	Torgersen	36.7	19.3	193	3450	female	2007	B
Adelie	Torgersen	39.3	20.6	190	3650	male	2007	B
Adelie	Torgersen	38.9	17.8	181	3625	female	2007	B
Adelie	Torgersen	39.2	19.6	195	4675	male	2007	B
Adelie	Torgersen	41.1	17.6	182	3200	female	2007	B
Adelie	Torgersen	38.6	21.2	191	3800	male	2007	B
Adelie	Torgersen	34.6	21.1	198	4400	male	2007	A
Adelie	Torgersen	36.6	17.8	185	3700	female	2007	B
Adelie	Torgersen	38.7	19.0	195	3450	female	2007	B
Adelie	Torgersen	42.5	20.7	197	4500	male	2007	B
Adelie	Torgersen	34.4	18.4	184	3325	female	2007	A
Adelie	Torgersen	46.0	21.5	194	4200	male	2007	C
Adelie	Biscoe	37.8	18.3	174	3400	female	2007	B
Adelie	Biscoe	37.7	18.7	180	3600	male	2007	B
Adelie	Biscoe	35.9	19.2	189	3800	female	2007	B
Adelie	Biscoe	38.2	18.1	185	3950	male	2007	B
Adelie	Biscoe	38.8	17.2	180	3800	male	2007	B
Adelie	Biscoe	35.3	18.9	187	3800	female	2007	B
Adelie	Biscoe	40.6	18.6	183	3550	male	2007	B
Adelie	Biscoe	40.5	17.9	187	3200	female	2007	B
Adelie	Biscoe	37.9	18.6	172	3150	female	2007	B
Adelie	Biscoe	40.5	18.9	180	3950	male	2007	B
Adelie	Dream	39.5	16.7	178	3250	female	2007	B
Adelie	Dream	37.2	18.1	178	3900	male	2007	B
Adelie	Dream	39.5	17.8	188	3300	female	2007	B
Adelie	Dream	40.9	18.9	184	3900	male	2007	B
Adelie	Dream	36.4	17.0	195	3325	female	2007	B
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
Chinstrap	Dream	46.9	16.6	192	2700	female	2008	C
Chinstrap	Dream	53.5	19.9	205	4500	male	2008	C
Chinstrap	Dream	49.0	19.5	210	3950	male	2008	C
Chinstrap	Dream	46.2	17.5	187	3650	female	2008	C
Chinstrap	Dream	50.9	19.1	196	3550	male	2008	C
Chinstrap	Dream	45.5	17.0	196	3500	female	2008	C
Chinstrap	Dream	50.9	17.9	196	3675	female	2009	C
Chinstrap	Dream	50.8	18.5	201	4450	male	2009	C
Chinstrap	Dream	50.1	17.9	190	3400	female	2009	C
Chinstrap	Dream	49.0	19.6	212	4300	male	2009	C
Chinstrap	Dream	51.5	18.7	187	3250	male	2009	C
Chinstrap	Dream	49.8	17.3	198	3675	female	2009	C
Chinstrap	Dream	48.1	16.4	199	3325	female	2009	C
Chinstrap	Dream	51.4	19.0	201	3950	male	2009	C
Chinstrap	Dream	45.7	17.3	193	3600	female	2009	C
Chinstrap	Dream	50.7	19.7	203	4050	male	2009	C
Chinstrap	Dream	42.5	17.3	187	3350	female	2009	B
Chinstrap	Dream	52.2	18.8	197	3450	male	2009	C
Chinstrap	Dream	45.2	16.6	191	3250	female	2009	C
Chinstrap	Dream	49.3	19.9	203	4050	male	2009	C
Chinstrap	Dream	50.2	18.8	202	3800	male	2009	C
Chinstrap	Dream	45.6	19.4	194	3525	female	2009	C
Chinstrap	Dream	51.9	19.5	206	3950	male	2009	C
Chinstrap	Dream	46.8	16.5	189	3650	female	2009	C
Chinstrap	Dream	45.7	17.0	195	3650	female	2009	C
Chinstrap	Dream	55.8	19.8	207	4000	male	2009	D
Chinstrap	Dream	43.5	18.1	202	3400	female	2009	B
Chinstrap	Dream	49.6	18.2	193	3775	male	2009	C
Chinstrap	Dream	50.8	19.0	210	4100	male	2009	C
Chinstrap	Dream	50.2	18.7	198	3775	female	2009	C

拓展函数

• n()函数，统计当前分组数据框的行数

• 统计某个变量中各组出现的次数，可以使用count()函数, 可以统计不同组合出现的次数

• 可以在count()里构建新变量，并利用这个新变量完成统计

penguins %>% summarise(n = n())

A tibble: 1 × 1

n
<int>
333

penguins %>% group_by(species) %>% summarise(n = n())

A tibble: 3 × 2

species	n
<fct>	<int>
Adelie	146
Chinstrap	68
Gentoo	119

penguins %>% count(species)
penguins %>% count(sex, sort = TRUE)
penguins %>% count(island, species)

A tibble: 3 × 2

species	n
<fct>	<int>
Adelie	146
Chinstrap	68
Gentoo	119

A tibble: 2 × 2

sex	n
<fct>	<int>
male	168
female	165

A tibble: 5 × 3

island	species	n
<fct>	<fct>	<int>
Biscoe	Adelie	44
Biscoe	Gentoo	119
Dream	Adelie	55
Dream	Chinstrap	68
Torgersen	Adelie	47

penguins %>% filter(bill_length_mm > 40) %>% summarise(n = n())
penguins %>% count(longer_bill = bill_length_mm > 40)

A tibble: 1 × 1

n
<int>
237

A tibble: 2 × 2

longer_bill	n
<lgl>	<int>
FALSE	96
TRUE	237

强制转换

• 矢量中的元素必须是相同的类型，但如果不一样呢，会发生什么？ 这个时候R会强制转换成相同的类型。这就涉及数据类型的转换层级

  • character > numeric > logical

  • double > integer

c("foo", 1, TRUE) # 强制转换成了字符串类型

1. 'foo'
2. '1'
3. 'TRUE'

penguins %>% 
  mutate(is_bigger40 = bill_length_mm > 40) %>% 
  summarise(n = n())

penguins %>% 
  filter(bill_length_mm > 40) %>% 
  summarise(n = n())

A tibble: 1 × 1

n
<int>
333

A tibble: 1 × 1

n
<int>
237

across()函数

• 更安全、更简练的写法

penguins %>% 
  summarise(
    length = mean(bill_length_mm, na.rm=TRUE))

A tibble: 1 × 1

length
<dbl>
43.99279

penguins %>% 
  summarise(
    length = mean(bill_length_mm, na.rm=TRUE),
    depth = mean(bill_depth_mm, na.rm=TRUE)
  )

A tibble: 1 × 2

length	depth
<dbl>	<dbl>
43.99279	17.16486

penguins %>% 
  summarise(
    across(c(bill_depth_mm, bill_length_mm), mean, na.rm=TRUE)
  )

Warning message:
“There was 1 warning in `summarise()`.
ℹ In argument: `across(c(bill_depth_mm, bill_length_mm), mean, na.rm = TRUE)`.
Caused by warning:
! The `...` argument of `across()` is deprecated as of dplyr 1.1.0.
Supply arguments directly to `.fns` through an anonymous function instead.

  # Previously
  across(a:b, mean, na.rm = TRUE)

  # Now
  across(a:b, \(x) mean(x, na.rm = TRUE))”

A tibble: 1 × 2

bill_depth_mm	bill_length_mm
<dbl>	<dbl>
17.16486	43.99279

penguins %>% 
  summarise(
    across(ends_with("_mm"), mean, na.rm=TRUE))

A tibble: 1 × 3

bill_length_mm	bill_depth_mm	flipper_length_mm
<dbl>	<dbl>	<dbl>
43.99279	17.16486	200.967

数据中心化

看数据的离散

penguins %>% 
  mutate(
    bill_length_mm = bill_length_mm - mean(bill_length_mm, na.rm=TRUE),
    bill_depth_mm = bill_depth_mm - mean(bill_depth_mm, na.rm=TRUE))

A tibble: 333 × 8

species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
<fct>	<fct>	<dbl>	<dbl>	<int>	<int>	<fct>	<int>
Adelie	Torgersen	-4.892793	1.53513514	181	3750	male	2007
Adelie	Torgersen	-4.492793	0.23513514	186	3800	female	2007
Adelie	Torgersen	-3.692793	0.83513514	195	3250	female	2007
Adelie	Torgersen	-7.292793	2.13513514	193	3450	female	2007
Adelie	Torgersen	-4.692793	3.43513514	190	3650	male	2007
Adelie	Torgersen	-5.092793	0.63513514	181	3625	female	2007
Adelie	Torgersen	-4.792793	2.43513514	195	4675	male	2007
Adelie	Torgersen	-2.892793	0.43513514	182	3200	female	2007
Adelie	Torgersen	-5.392793	4.03513514	191	3800	male	2007
Adelie	Torgersen	-9.392793	3.93513514	198	4400	male	2007
Adelie	Torgersen	-7.392793	0.63513514	185	3700	female	2007
Adelie	Torgersen	-5.292793	1.83513514	195	3450	female	2007
Adelie	Torgersen	-1.492793	3.53513514	197	4500	male	2007
Adelie	Torgersen	-9.592793	1.23513514	184	3325	female	2007
Adelie	Torgersen	2.007207	4.33513514	194	4200	male	2007
Adelie	Biscoe	-6.192793	1.13513514	174	3400	female	2007
Adelie	Biscoe	-6.292793	1.53513514	180	3600	male	2007
Adelie	Biscoe	-8.092793	2.03513514	189	3800	female	2007
Adelie	Biscoe	-5.792793	0.93513514	185	3950	male	2007
Adelie	Biscoe	-5.192793	0.03513514	180	3800	male	2007
Adelie	Biscoe	-8.692793	1.73513514	187	3800	female	2007
Adelie	Biscoe	-3.392793	1.43513514	183	3550	male	2007
Adelie	Biscoe	-3.492793	0.73513514	187	3200	female	2007
Adelie	Biscoe	-6.092793	1.43513514	172	3150	female	2007
Adelie	Biscoe	-3.492793	1.73513514	180	3950	male	2007
Adelie	Dream	-4.492793	-0.46486486	178	3250	female	2007
Adelie	Dream	-6.792793	0.93513514	178	3900	male	2007
Adelie	Dream	-4.492793	0.63513514	188	3300	female	2007
Adelie	Dream	-3.092793	1.73513514	184	3900	male	2007
Adelie	Dream	-7.592793	-0.16486486	195	3325	female	2007
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
Chinstrap	Dream	2.9072072	-0.5648649	192	2700	female	2008
Chinstrap	Dream	9.5072072	2.7351351	205	4500	male	2008
Chinstrap	Dream	5.0072072	2.3351351	210	3950	male	2008
Chinstrap	Dream	2.2072072	0.3351351	187	3650	female	2008
Chinstrap	Dream	6.9072072	1.9351351	196	3550	male	2008
Chinstrap	Dream	1.5072072	-0.1648649	196	3500	female	2008
Chinstrap	Dream	6.9072072	0.7351351	196	3675	female	2009
Chinstrap	Dream	6.8072072	1.3351351	201	4450	male	2009
Chinstrap	Dream	6.1072072	0.7351351	190	3400	female	2009
Chinstrap	Dream	5.0072072	2.4351351	212	4300	male	2009
Chinstrap	Dream	7.5072072	1.5351351	187	3250	male	2009
Chinstrap	Dream	5.8072072	0.1351351	198	3675	female	2009
Chinstrap	Dream	4.1072072	-0.7648649	199	3325	female	2009
Chinstrap	Dream	7.4072072	1.8351351	201	3950	male	2009
Chinstrap	Dream	1.7072072	0.1351351	193	3600	female	2009
Chinstrap	Dream	6.7072072	2.5351351	203	4050	male	2009
Chinstrap	Dream	-1.4927928	0.1351351	187	3350	female	2009
Chinstrap	Dream	8.2072072	1.6351351	197	3450	male	2009
Chinstrap	Dream	1.2072072	-0.5648649	191	3250	female	2009
Chinstrap	Dream	5.3072072	2.7351351	203	4050	male	2009
Chinstrap	Dream	6.2072072	1.6351351	202	3800	male	2009
Chinstrap	Dream	1.6072072	2.2351351	194	3525	female	2009
Chinstrap	Dream	7.9072072	2.3351351	206	3950	male	2009
Chinstrap	Dream	2.8072072	-0.6648649	189	3650	female	2009
Chinstrap	Dream	1.7072072	-0.1648649	195	3650	female	2009
Chinstrap	Dream	11.8072072	2.6351351	207	4000	male	2009
Chinstrap	Dream	-0.4927928	0.9351351	202	3400	female	2009
Chinstrap	Dream	5.6072072	1.0351351	193	3775	male	2009
Chinstrap	Dream	6.8072072	1.8351351	210	4100	male	2009
Chinstrap	Dream	6.2072072	1.5351351	198	3775	female	2009

centralized <- function(x){
    x - mean(x, na.rm=TRUE)
}

penguins %>% 
  mutate(
    across(c(bill_length_mm, bill_depth_mm), centralized))

A tibble: 333 × 8

species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
<fct>	<fct>	<dbl>	<dbl>	<int>	<int>	<fct>	<int>
Adelie	Torgersen	-4.892793	1.53513514	181	3750	male	2007
Adelie	Torgersen	-4.492793	0.23513514	186	3800	female	2007
Adelie	Torgersen	-3.692793	0.83513514	195	3250	female	2007
Adelie	Torgersen	-7.292793	2.13513514	193	3450	female	2007
Adelie	Torgersen	-4.692793	3.43513514	190	3650	male	2007
Adelie	Torgersen	-5.092793	0.63513514	181	3625	female	2007
Adelie	Torgersen	-4.792793	2.43513514	195	4675	male	2007
Adelie	Torgersen	-2.892793	0.43513514	182	3200	female	2007
Adelie	Torgersen	-5.392793	4.03513514	191	3800	male	2007
Adelie	Torgersen	-9.392793	3.93513514	198	4400	male	2007
Adelie	Torgersen	-7.392793	0.63513514	185	3700	female	2007
Adelie	Torgersen	-5.292793	1.83513514	195	3450	female	2007
Adelie	Torgersen	-1.492793	3.53513514	197	4500	male	2007
Adelie	Torgersen	-9.592793	1.23513514	184	3325	female	2007
Adelie	Torgersen	2.007207	4.33513514	194	4200	male	2007
Adelie	Biscoe	-6.192793	1.13513514	174	3400	female	2007
Adelie	Biscoe	-6.292793	1.53513514	180	3600	male	2007
Adelie	Biscoe	-8.092793	2.03513514	189	3800	female	2007
Adelie	Biscoe	-5.792793	0.93513514	185	3950	male	2007
Adelie	Biscoe	-5.192793	0.03513514	180	3800	male	2007
Adelie	Biscoe	-8.692793	1.73513514	187	3800	female	2007
Adelie	Biscoe	-3.392793	1.43513514	183	3550	male	2007
Adelie	Biscoe	-3.492793	0.73513514	187	3200	female	2007
Adelie	Biscoe	-6.092793	1.43513514	172	3150	female	2007
Adelie	Biscoe	-3.492793	1.73513514	180	3950	male	2007
Adelie	Dream	-4.492793	-0.46486486	178	3250	female	2007
Adelie	Dream	-6.792793	0.93513514	178	3900	male	2007
Adelie	Dream	-4.492793	0.63513514	188	3300	female	2007
Adelie	Dream	-3.092793	1.73513514	184	3900	male	2007
Adelie	Dream	-7.592793	-0.16486486	195	3325	female	2007
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
Chinstrap	Dream	2.9072072	-0.5648649	192	2700	female	2008
Chinstrap	Dream	9.5072072	2.7351351	205	4500	male	2008
Chinstrap	Dream	5.0072072	2.3351351	210	3950	male	2008
Chinstrap	Dream	2.2072072	0.3351351	187	3650	female	2008
Chinstrap	Dream	6.9072072	1.9351351	196	3550	male	2008
Chinstrap	Dream	1.5072072	-0.1648649	196	3500	female	2008
Chinstrap	Dream	6.9072072	0.7351351	196	3675	female	2009
Chinstrap	Dream	6.8072072	1.3351351	201	4450	male	2009
Chinstrap	Dream	6.1072072	0.7351351	190	3400	female	2009
Chinstrap	Dream	5.0072072	2.4351351	212	4300	male	2009
Chinstrap	Dream	7.5072072	1.5351351	187	3250	male	2009
Chinstrap	Dream	5.8072072	0.1351351	198	3675	female	2009
Chinstrap	Dream	4.1072072	-0.7648649	199	3325	female	2009
Chinstrap	Dream	7.4072072	1.8351351	201	3950	male	2009
Chinstrap	Dream	1.7072072	0.1351351	193	3600	female	2009
Chinstrap	Dream	6.7072072	2.5351351	203	4050	male	2009
Chinstrap	Dream	-1.4927928	0.1351351	187	3350	female	2009
Chinstrap	Dream	8.2072072	1.6351351	197	3450	male	2009
Chinstrap	Dream	1.2072072	-0.5648649	191	3250	female	2009
Chinstrap	Dream	5.3072072	2.7351351	203	4050	male	2009
Chinstrap	Dream	6.2072072	1.6351351	202	3800	male	2009
Chinstrap	Dream	1.6072072	2.2351351	194	3525	female	2009
Chinstrap	Dream	7.9072072	2.3351351	206	3950	male	2009
Chinstrap	Dream	2.8072072	-0.6648649	189	3650	female	2009
Chinstrap	Dream	1.7072072	-0.1648649	195	3650	female	2009
Chinstrap	Dream	11.8072072	2.6351351	207	4000	male	2009
Chinstrap	Dream	-0.4927928	0.9351351	202	3400	female	2009
Chinstrap	Dream	5.6072072	1.0351351	193	3775	male	2009
Chinstrap	Dream	6.8072072	1.8351351	210	4100	male	2009
Chinstrap	Dream	6.2072072	1.5351351	198	3775	female	2009

数据标准化

• (x - mean(x))/sd(x)

• 那一列数据的每个值都减去平均值再除以标准差

std <- function(x){
    (x-mean(x, na.rm=TRUE)) / sd(x, na.rm=TRUE)
}
penguins %>% 
  mutate(
    across(c(bill_length_mm, bill_depth_mm), std)
  )

A tibble: 333 × 8

species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
<fct>	<fct>	<dbl>	<dbl>	<int>	<int>	<fct>	<int>
Adelie	Torgersen	-0.8946955	0.77955895	181	3750	male	2007
Adelie	Torgersen	-0.8215515	0.11940428	186	3800	female	2007
Adelie	Torgersen	-0.6752636	0.42409105	195	3250	female	2007
Adelie	Torgersen	-1.3335592	1.08424573	193	3450	female	2007
Adelie	Torgersen	-0.8581235	1.74440040	190	3650	male	2007
Adelie	Torgersen	-0.9312674	0.32252879	181	3625	female	2007
Adelie	Torgersen	-0.8764095	1.23658911	195	4675	male	2007
Adelie	Torgersen	-0.5289757	0.22096653	182	3200	female	2007
Adelie	Torgersen	-0.9861254	2.04908718	191	3800	male	2007
Adelie	Torgersen	-1.7175649	1.99830605	198	4400	male	2007
Adelie	Torgersen	-1.3518452	0.32252879	185	3700	female	2007
Adelie	Torgersen	-0.9678394	0.93190234	195	3450	female	2007
Adelie	Torgersen	-0.2729719	1.79518153	197	4500	male	2007
Adelie	Torgersen	-1.7541369	0.62721557	184	3325	female	2007
Adelie	Torgersen	0.3670377	2.20143056	194	4200	male	2007
Adelie	Biscoe	-1.1324133	0.57643444	174	3400	female	2007
Adelie	Biscoe	-1.1506993	0.77955895	180	3600	male	2007
Adelie	Biscoe	-1.4798471	1.03346460	189	3800	female	2007
Adelie	Biscoe	-1.0592694	0.47487218	185	3950	male	2007
Adelie	Biscoe	-0.9495534	0.01784202	180	3800	male	2007
Adelie	Biscoe	-1.5895630	0.88112121	187	3800	female	2007
Adelie	Biscoe	-0.6204057	0.72877782	183	3550	male	2007
Adelie	Biscoe	-0.6386916	0.37330992	187	3200	female	2007
Adelie	Biscoe	-1.1141273	0.72877782	172	3150	female	2007
Adelie	Biscoe	-0.6386916	0.88112121	180	3950	male	2007
Adelie	Dream	-0.8215515	-0.23606363	178	3250	female	2007
Adelie	Dream	-1.2421292	0.47487218	178	3900	male	2007
Adelie	Dream	-0.8215515	0.32252879	188	3300	female	2007
Adelie	Dream	-0.5655477	0.88112121	184	3900	male	2007
Adelie	Dream	-1.3884171	-0.08372024	195	3325	female	2007
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
Chinstrap	Dream	0.53161154	-0.28684476	192	2700	female	2008
Chinstrap	Dream	1.73848670	1.38893250	205	4500	male	2008
Chinstrap	Dream	0.91561728	1.18580798	210	3950	male	2008
Chinstrap	Dream	0.40360963	0.17018541	187	3650	female	2008
Chinstrap	Dream	1.26305103	0.98268347	196	3550	male	2008
Chinstrap	Dream	0.27560772	-0.08372024	196	3500	female	2008
Chinstrap	Dream	1.26305103	0.37330992	196	3675	female	2009
Chinstrap	Dream	1.24476505	0.67799669	201	4450	male	2009
Chinstrap	Dream	1.11676314	0.37330992	190	3400	female	2009
Chinstrap	Dream	0.91561728	1.23658911	212	4300	male	2009
Chinstrap	Dream	1.37276696	0.77955895	187	3250	male	2009
Chinstrap	Dream	1.06190517	0.06862315	198	3675	female	2009
Chinstrap	Dream	0.75104339	-0.38840701	199	3325	female	2009
Chinstrap	Dream	1.35448097	0.93190234	201	3950	male	2009
Chinstrap	Dream	0.31217969	0.06862315	193	3600	female	2009
Chinstrap	Dream	1.22647906	1.28737024	203	4050	male	2009
Chinstrap	Dream	-0.27297190	0.06862315	187	3350	female	2009
Chinstrap	Dream	1.50076887	0.83034008	197	3450	male	2009
Chinstrap	Dream	0.22074976	-0.28684476	191	3250	female	2009
Chinstrap	Dream	0.97047524	1.38893250	203	4050	male	2009
Chinstrap	Dream	1.13504912	0.83034008	202	3800	male	2009
Chinstrap	Dream	0.29389371	1.13502686	194	3525	female	2009
Chinstrap	Dream	1.44591091	1.18580798	206	3950	male	2009
Chinstrap	Dream	0.51332555	-0.33762588	189	3650	female	2009
Chinstrap	Dream	0.31217969	-0.08372024	195	3650	female	2009
Chinstrap	Dream	2.15906441	1.33815137	207	4000	male	2009
Chinstrap	Dream	-0.09011203	0.47487218	202	3400	female	2009
Chinstrap	Dream	1.02533320	0.52565331	193	3775	male	2009
Chinstrap	Dream	1.24476505	0.93190234	210	4100	male	2009
Chinstrap	Dream	1.13504912	0.77955895	198	3775	female	2009

penguins %>% 
  mutate(
    across(c(bill_length_mm, bill_depth_mm), 
           ~(.x - mean(.x, na.rm=TRUE)) / sd(.x, na.rm=TRUE))
  )

A tibble: 333 × 8

species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
<fct>	<fct>	<dbl>	<dbl>	<int>	<int>	<fct>	<int>
Adelie	Torgersen	-0.8946955	0.77955895	181	3750	male	2007
Adelie	Torgersen	-0.8215515	0.11940428	186	3800	female	2007
Adelie	Torgersen	-0.6752636	0.42409105	195	3250	female	2007
Adelie	Torgersen	-1.3335592	1.08424573	193	3450	female	2007
Adelie	Torgersen	-0.8581235	1.74440040	190	3650	male	2007
Adelie	Torgersen	-0.9312674	0.32252879	181	3625	female	2007
Adelie	Torgersen	-0.8764095	1.23658911	195	4675	male	2007
Adelie	Torgersen	-0.5289757	0.22096653	182	3200	female	2007
Adelie	Torgersen	-0.9861254	2.04908718	191	3800	male	2007
Adelie	Torgersen	-1.7175649	1.99830605	198	4400	male	2007
Adelie	Torgersen	-1.3518452	0.32252879	185	3700	female	2007
Adelie	Torgersen	-0.9678394	0.93190234	195	3450	female	2007
Adelie	Torgersen	-0.2729719	1.79518153	197	4500	male	2007
Adelie	Torgersen	-1.7541369	0.62721557	184	3325	female	2007
Adelie	Torgersen	0.3670377	2.20143056	194	4200	male	2007
Adelie	Biscoe	-1.1324133	0.57643444	174	3400	female	2007
Adelie	Biscoe	-1.1506993	0.77955895	180	3600	male	2007
Adelie	Biscoe	-1.4798471	1.03346460	189	3800	female	2007
Adelie	Biscoe	-1.0592694	0.47487218	185	3950	male	2007
Adelie	Biscoe	-0.9495534	0.01784202	180	3800	male	2007
Adelie	Biscoe	-1.5895630	0.88112121	187	3800	female	2007
Adelie	Biscoe	-0.6204057	0.72877782	183	3550	male	2007
Adelie	Biscoe	-0.6386916	0.37330992	187	3200	female	2007
Adelie	Biscoe	-1.1141273	0.72877782	172	3150	female	2007
Adelie	Biscoe	-0.6386916	0.88112121	180	3950	male	2007
Adelie	Dream	-0.8215515	-0.23606363	178	3250	female	2007
Adelie	Dream	-1.2421292	0.47487218	178	3900	male	2007
Adelie	Dream	-0.8215515	0.32252879	188	3300	female	2007
Adelie	Dream	-0.5655477	0.88112121	184	3900	male	2007
Adelie	Dream	-1.3884171	-0.08372024	195	3325	female	2007
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
Chinstrap	Dream	0.53161154	-0.28684476	192	2700	female	2008
Chinstrap	Dream	1.73848670	1.38893250	205	4500	male	2008
Chinstrap	Dream	0.91561728	1.18580798	210	3950	male	2008
Chinstrap	Dream	0.40360963	0.17018541	187	3650	female	2008
Chinstrap	Dream	1.26305103	0.98268347	196	3550	male	2008
Chinstrap	Dream	0.27560772	-0.08372024	196	3500	female	2008
Chinstrap	Dream	1.26305103	0.37330992	196	3675	female	2009
Chinstrap	Dream	1.24476505	0.67799669	201	4450	male	2009
Chinstrap	Dream	1.11676314	0.37330992	190	3400	female	2009
Chinstrap	Dream	0.91561728	1.23658911	212	4300	male	2009
Chinstrap	Dream	1.37276696	0.77955895	187	3250	male	2009
Chinstrap	Dream	1.06190517	0.06862315	198	3675	female	2009
Chinstrap	Dream	0.75104339	-0.38840701	199	3325	female	2009
Chinstrap	Dream	1.35448097	0.93190234	201	3950	male	2009
Chinstrap	Dream	0.31217969	0.06862315	193	3600	female	2009
Chinstrap	Dream	1.22647906	1.28737024	203	4050	male	2009
Chinstrap	Dream	-0.27297190	0.06862315	187	3350	female	2009
Chinstrap	Dream	1.50076887	0.83034008	197	3450	male	2009
Chinstrap	Dream	0.22074976	-0.28684476	191	3250	female	2009
Chinstrap	Dream	0.97047524	1.38893250	203	4050	male	2009
Chinstrap	Dream	1.13504912	0.83034008	202	3800	male	2009
Chinstrap	Dream	0.29389371	1.13502686	194	3525	female	2009
Chinstrap	Dream	1.44591091	1.18580798	206	3950	male	2009
Chinstrap	Dream	0.51332555	-0.33762588	189	3650	female	2009
Chinstrap	Dream	0.31217969	-0.08372024	195	3650	female	2009
Chinstrap	Dream	2.15906441	1.33815137	207	4000	male	2009
Chinstrap	Dream	-0.09011203	0.47487218	202	3400	female	2009
Chinstrap	Dream	1.02533320	0.52565331	193	3775	male	2009
Chinstrap	Dream	1.24476505	0.93190234	210	4100	male	2009
Chinstrap	Dream	1.13504912	0.77955895	198	3775	female	2009

多列多个统计函数

penguins %>% 
  group_by(species) %>% 
  summarise(
    across(ends_with("_mm"), list(mean = mean, sd = sd), na.rm=TRUE))

A tibble: 3 × 7

species	bill_length_mm_mean	bill_length_mm_sd	bill_depth_mm_mean	bill_depth_mm_sd	flipper_length_mm_mean	flipper_length_mm_sd
<fct>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
Adelie	38.82397	2.662597	18.34726	1.219338	190.1027	6.521825
Chinstrap	48.83382	3.339256	18.42059	1.135395	195.8235	7.131894
Gentoo	47.56807	3.106116	14.99664	0.985998	217.2353	6.585431

penguins %>% 
  group_by(sex) %>% 
  summarise(
    across(starts_with("bill_"), list(max = max, min = min), na.rm=TRUE))

A tibble: 2 × 5

sex	bill_length_mm_max	bill_length_mm_min	bill_depth_mm_max	bill_depth_mm_min
<fct>	<dbl>	<dbl>	<dbl>	<dbl>
female	58.0	32.1	20.7	13.1
male	59.6	34.6	21.5	14.1

数据规整

• 1 宽表格变长表格gather() pivot_longer(), 推荐pivot_longer()

  • 

  • 参数cols，表示哪些列需要转换.

  • 参数names_to，表示cols选取的这些列的名字，构成了新的一列，这里需要取一个名字.

  • 参数values_to， 表示cols选取的这些列的值，构成了新的一列，这里也需要取一个名字.

  • 数据框总的信息量不会丢失

• 2 长表格变宽表格spread() pivot_wider()

library(tidyverse)

plant_height <- data.frame(
  Day = 1:5,
  A = c(0.7, 1.0, 1.5, 1.8, 2.2),
  B = c(0.5, 0.7, 0.9, 1.3, 1.8),
  C = c(0.3, 0.6, 1.0, 1.2, 2.2),
  D = c(0.4, 0.7, 1.2, 1.5, 3.2)
)


plant_height

A data.frame: 5 × 5

Day	A	B	C	D
<int>	<dbl>	<dbl>	<dbl>	<dbl>
1	0.7	0.5	0.3	0.4
2	1.0	0.7	0.6	0.7
3	1.5	0.9	1.0	1.2
4	1.8	1.3	1.2	1.5
5	2.2	1.8	2.2	3.2

melted <- plant_height %>% 
  pivot_longer(cols = A:D,   # -Day or c(A, B, C, D) or c("A", "B", "C", "D")
               names_to = "plant",
               values_to = "height")
melted

melted1 <- gather(plant_height, variable, value, A:D)
melted1

A tibble: 20 × 3

Day	plant	height
<int>	<chr>	<dbl>
1	A	0.7
1	B	0.5
1	C	0.3
1	D	0.4
2	A	1.0
2	B	0.7
2	C	0.6
2	D	0.7
3	A	1.5
3	B	0.9
3	C	1.0
3	D	1.2
4	A	1.8
4	B	1.3
4	C	1.2
4	D	1.5
5	A	2.2
5	B	1.8
5	C	2.2
5	D	3.2

A data.frame: 20 × 3

Day	variable	value
<int>	<chr>	<dbl>
1	A	0.7
2	A	1.0
3	A	1.5
4	A	1.8
5	A	2.2
1	B	0.5
2	B	0.7
3	B	0.9
4	B	1.3
5	B	1.8
1	C	0.3
2	C	0.6
3	C	1.0
4	C	1.2
5	C	2.2
1	D	0.4
2	D	0.7
3	D	1.2
4	D	1.5
5	D	3.2

melted %>% 
  ggplot(aes(x=Day, y=height, color=plant))+
  geom_line()

wide <- melted %>% 
  pivot_wider(
  names_from = "plant",
  values_from = "height"
)
wide

A tibble: 5 × 5

Day	A	B	C	D
<int>	<dbl>	<dbl>	<dbl>	<dbl>
1	0.7	0.5	0.3	0.4
2	1.0	0.7	0.6	0.7
3	1.5	0.9	1.0	1.2
4	1.8	1.3	1.2	1.5
5	2.2	1.8	2.2	3.2

• 列名转换成多个变量

  • pivot_longer()中的names_pattern = 指定表头属性

  • pivot_wider()中的names_glue = 指定转变后表头样式

plant_record <- data.frame(
         day = c(1L, 2L, 3L, 4L, 5L),
    A_height = c(1.1, 1.2, 1.3, 1.4, 1.5),
     A_width = c(2.1, 2.2, 2.3, 2.4, 2.5),
     A_depth = c(3.1, 3.2, 3.3, 3.4, 3.5),
    B_height = c(4.1, 4.2, 4.3, 4.4, 4.5),
     B_width = c(5.1, 5.2, 5.3, 5.4, 5.5),
     B_depth = c(6.1, 6.2, 6.3, 6.4, 6.5),
    C_height = c(7.1, 7.2, 7.3, 7.4, 7.5),
     C_width = c(8.1, 8.2, 8.3, 8.4, 8.5),
     C_depth = c(9.1, 9.2, 9.3, 9.4, 9.5)
)
plant_record

A data.frame: 5 × 10

day	A_height	A_width	A_depth	B_height	B_width	B_depth	C_height	C_width	C_depth
<int>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
1	1.1	2.1	3.1	4.1	5.1	6.1	7.1	8.1	9.1
2	1.2	2.2	3.2	4.2	5.2	6.2	7.2	8.2	9.2
3	1.3	2.3	3.3	4.3	5.3	6.3	7.3	8.3	9.3
4	1.4	2.4	3.4	4.4	5.4	6.4	7.4	8.4	9.4
5	1.5	2.5	3.5	4.5	5.5	6.5	7.5	8.5	9.5

我们想原始数据框的列名，转换成多个变量，比如A,B,C成为物种(species)变量，（height, width, depth）成为parameter变量

我们希望原始数据框的列名中，一部分进入变量，一部分保持原来的列名，比如，

## pivot_longer()函数的实现列名转换成多个变量
plant_record %>% 
  tidyr::pivot_longer(cols = !day,
                      names_to = c("species", "parameter"),
                      names_pattern = "(.*)_(.*)",
                      values_to = "value")

## pivot_longer()函数实现部分列名转变成变量，部分保持原来的列名
plant_record_longer <- plant_record %>% 
  tidyr::pivot_longer(cols = !day,
                      names_to = c("species",".value"),
                      names_pattern = "(.*)_(.*)")
plant_record_longer
# 注意 .value 而不是value，说明这里不是单个列名，
# 而是匹配得到的多个值做列名

A tibble: 45 × 4

day	species	parameter	value
<int>	<chr>	<chr>	<dbl>
1	A	height	1.1
1	A	width	2.1
1	A	depth	3.1
1	B	height	4.1
1	B	width	5.1
1	B	depth	6.1
1	C	height	7.1
1	C	width	8.1
1	C	depth	9.1
2	A	height	1.2
2	A	width	2.2
2	A	depth	3.2
2	B	height	4.2
2	B	width	5.2
2	B	depth	6.2
2	C	height	7.2
2	C	width	8.2
2	C	depth	9.2
3	A	height	1.3
3	A	width	2.3
3	A	depth	3.3
3	B	height	4.3
3	B	width	5.3
3	B	depth	6.3
3	C	height	7.3
3	C	width	8.3
3	C	depth	9.3
4	A	height	1.4
4	A	width	2.4
4	A	depth	3.4
4	B	height	4.4
4	B	width	5.4
4	B	depth	6.4
4	C	height	7.4
4	C	width	8.4
4	C	depth	9.4
5	A	height	1.5
5	A	width	2.5
5	A	depth	3.5
5	B	height	4.5
5	B	width	5.5
5	B	depth	6.5
5	C	height	7.5
5	C	width	8.5
5	C	depth	9.5

A tibble: 15 × 5

day	species	height	width	depth
<int>	<chr>	<dbl>	<dbl>	<dbl>
1	A	1.1	2.1	3.1
1	B	4.1	5.1	6.1
1	C	7.1	8.1	9.1
2	A	1.2	2.2	3.2
2	B	4.2	5.2	6.2
2	C	7.2	8.2	9.2
3	A	1.3	2.3	3.3
3	B	4.3	5.3	6.3
3	C	7.3	8.3	9.3
4	A	1.4	2.4	3.4
4	B	4.4	5.4	6.4
4	C	7.4	8.4	9.4
5	A	1.5	2.5	3.5
5	B	4.5	5.5	6.5
5	C	7.5	8.5	9.5

• 变回去

  • 

us_rent_income %>%
    pivot_wider(
      names_from = variable,
      names_glue = "{variable}_{.value}",
      values_from = c(estimate, moe))

A tibble: 52 × 6

GEOID	NAME	income_estimate	rent_estimate	income_moe	rent_moe
<chr>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>
01	Alabama	24476	747	136	3
02	Alaska	32940	1200	508	13
04	Arizona	27517	972	148	4
05	Arkansas	23789	709	165	5
06	California	29454	1358	109	3
08	Colorado	32401	1125	109	5
09	Connecticut	35326	1123	195	5
10	Delaware	31560	1076	247	10
11	District of Columbia	43198	1424	681	17
12	Florida	25952	1077	70	3
13	Georgia	27024	927	106	3
15	Hawaii	32453	1507	218	18
16	Idaho	25298	792	208	7
17	Illinois	30684	952	83	3
18	Indiana	27247	782	117	3
19	Iowa	30002	740	143	4
20	Kansas	29126	801	208	5
21	Kentucky	24702	713	159	4
22	Louisiana	25086	825	155	4
23	Maine	26841	808	187	7
24	Maryland	37147	1311	152	5
25	Massachusetts	34498	1173	199	5
26	Michigan	26987	824	82	3
27	Minnesota	32734	906	189	4
28	Mississippi	22766	740	194	5
29	Missouri	26999	784	113	4
30	Montana	26249	751	206	9
31	Nebraska	30020	773	146	4
32	Nevada	29019	1017	213	6
33	New Hampshire	33172	1052	387	9
34	New Jersey	35075	1249	148	4
35	New Mexico	24457	809	214	6
36	New York	31057	1194	69	3
37	North Carolina	26482	844	111	3
38	North Dakota	32336	775	245	9
39	Ohio	27435	764	94	2
40	Oklahoma	26207	766	101	3
41	Oregon	27389	988	146	4
42	Pennsylvania	28923	885	119	3
44	Rhode Island	30210	957	259	6
45	South Carolina	25454	836	123	4
46	South Dakota	28821	696	276	7
47	Tennessee	25453	808	102	4
48	Texas	28063	952	110	2
49	Utah	27928	948	239	6
50	Vermont	29351	945	361	11
51	Virginia	32545	1166	202	5
53	Washington	32318	1120	113	4
54	West Virginia	23707	681	203	6
55	Wisconsin	29868	813	135	3
56	Wyoming	30854	828	342	11
72	Puerto Rico	NA	464	NA	6

• 注意 .value 而不是value，说明这里不是单个列名，而是匹配得到的多个值做列名

plant_record_longer %>% 
  pivot_wider(names_from = species,
              values_from = c(height, width, depth),
              names_glue = "{species}_{.value}")

A tibble: 5 × 10

day	A_height	B_height	C_height	A_width	B_width	C_width	A_depth	B_depth	C_depth
<int>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
1	1.1	4.1	7.1	2.1	5.1	8.1	3.1	6.1	9.1
2	1.2	4.2	7.2	2.2	5.2	8.2	3.2	6.2	9.2
3	1.3	4.3	7.3	2.3	5.3	8.3	3.3	6.3	9.3
4	1.4	4.4	7.4	2.4	5.4	8.4	3.4	6.4	9.4
5	1.5	4.5	7.5	2.5	5.5	8.5	3.5	6.5	9.5

tidy data原则

• Hadley Wickhamt提出了数据科学tidy原则

  • 一切都是数据框，任何数据都可以规整

  • 数据框的一列代表一个变量，数据框的一行代表一次观察

  • 函数处理数据时，数据框进数据框出（函数的第一个参数始终为数据框）

根据Hadley Wickham的思想，这里的宽表格(plant_heigt和 wide)不是tidy的，只有长表格(long)才是tidy的，

melted

A tibble: 20 × 3

Day	plant	height
<int>	<chr>	<dbl>
1	A	0.7
1	B	0.5
1	C	0.3
1	D	0.4
2	A	1.0
2	B	0.7
2	C	0.6
2	D	0.7
3	A	1.5
3	B	0.9
3	C	1.0
3	D	1.2
4	A	1.8
4	B	1.3
4	C	1.2
4	D	1.5
5	A	2.2
5	B	1.8
5	C	2.2
5	D	3.2

fill()缺失值填充

• 根据最近进行补齐

• 可以控制填充的方向.direction = "up"

sales <- tibble::tribble(
  ~quarter, ~year, ~sales,
      "Q1",  2000,  66013,
      "Q2",    NA,  69182,
      "Q3",    NA,  53175,
      "Q4",    NA,  21001,
      "Q1",  2001,  46036,
      "Q2",    NA,  58842,
      "Q3",    NA,  44568,
      "Q4",    NA,  50197,
      "Q1",  2002,  39113,
      "Q2",    NA,  41668,
      "Q3",    NA,  30144,
      "Q4",    NA,  52897
  )
sales

A tibble: 12 × 3

quarter	year	sales
<chr>	<dbl>	<dbl>
Q1	2000	66013
Q2	NA	69182
Q3	NA	53175
Q4	NA	21001
Q1	2001	46036
Q2	NA	58842
Q3	NA	44568
Q4	NA	50197
Q1	2002	39113
Q2	NA	41668
Q3	NA	30144
Q4	NA	52897

sales %>% fill(year)
sales %>% fill(year, .direction="up") 

A tibble: 12 × 3

quarter	year	sales
<chr>	<dbl>	<dbl>
Q1	2000	66013
Q2	2000	69182
Q3	2000	53175
Q4	2000	21001
Q1	2001	46036
Q2	2001	58842
Q3	2001	44568
Q4	2001	50197
Q1	2002	39113
Q2	2002	41668
Q3	2002	30144
Q4	2002	52897

A tibble: 12 × 3

quarter	year	sales
<chr>	<dbl>	<dbl>
Q1	2000	66013
Q2	2001	69182
Q3	2001	53175
Q4	2001	21001
Q1	2001	46036
Q2	2002	58842
Q3	2002	44568
Q4	2002	50197
Q1	2002	39113
Q2	NA	41668
Q3	NA	30144
Q4	NA	52897

expand() 与 complete()

• expand()指定数据框的若干列，根据其向量元素值，产生所有可能的交叉组合

  • nesting()用于限定只产生数据框已出现的组合

• complete()补全，可以看作是expand(nesting()) + fill()

  • 数据在complete补全的时候，会面临有两种缺失值：

    • 1 补位的时候造成的空缺

    • 2 数据原先就存在缺失值

    • 3 补位的时候造成的空缺，可通过fill = list(x = 0)控制填充

    • 4 数据原先就存在缺失值，最好通过 explicit = FALSE显式地控制是否填充

df <- tibble::tribble(
  ~x, ~y, ~z,
  1L, 1L, 4L,
  1L, 2L, 5L,
  2L, 1L, NA,
  3L, 2L, 6L
)
df
df %>% expand(x,y)
df %>% expand(nesting(x,y))
df %>% expand(nesting(x,y), z)

A tibble: 4 × 3

x	y	z
<int>	<int>	<int>
1	1	4
1	2	5
2	1	NA
3	2	6

A tibble: 6 × 2

x	y
<int>	<int>
1	1
1	2
2	1
2	2
3	1
3	2

A tibble: 4 × 2

x	y
<int>	<int>
1	1
1	2
2	1
3	2

A tibble: 16 × 3

x	y	z
<int>	<int>	<int>
1	1	4
1	1	5
1	1	6
1	1	NA
1	2	4
1	2	5
1	2	6
1	2	NA
2	1	4
2	1	5
2	1	6
2	1	NA
3	2	4
3	2	5
3	2	6
3	2	NA

df %>% complete(x, y)
df %>% complete(x, y, fill=list(z=0))
df %>% complete(x, y, fill = list(z = 0), explicit = FALSE)

A tibble: 6 × 3

x	y	z
<int>	<int>	<int>
1	1	4
1	2	5
2	1	NA
2	2	NA
3	1	NA
3	2	6

A tibble: 6 × 3

x	y	z
<int>	<int>	<int>
1	1	4
1	2	5
2	1	0
2	2	0
3	1	0
3	2	6

A tibble: 6 × 3

x	y	z
<int>	<int>	<int>
1	1	4
1	2	5
2	1	NA
2	2	0
3	1	0
3	2	6

expand_grid() 与 crossing()

• expand_grid()产生一个新的数据框，每行对应着向量元素的所有交叉组合

• 向量换成数据框也可以，其结果就是数据框行与元素的交叉组合

• crossing()可以看作是expand_grid() + distinct()， 即crossing()在完成交叉组合之后会自动去重

expand_grid(x=c(1,1,2), y= 1:2)
crossing(x=c(1,1,2), y=1:2)

A tibble: 6 × 2

x	y
<dbl>	<int>
1	1
1	2
1	1
1	2
2	1
2	2

A tibble: 4 × 2

x	y
<dbl>	<int>
1	1
1	2
2	1
2	2

separate()与unite()

• 以提供的分隔符分隔或合并某些列

• 有时候分隔符搞不定的，可以用正则表达式，将捕获的每组弄成一列

tb <- tibble::tribble(
  ~day, ~price,
  1,   "30-45",
  2,   "40-95",
  3,   "89-65",
  4,   "45-63",
  5,   "52-42"
)
tb %>% 
  separate(price, into=c("low", "high"), sep="-") %>% 
  unite(col="price", c(low, high), sep=":", remove=FALSE)

A tibble: 5 × 4

day	price	low	high
<dbl>	<chr>	<chr>	<chr>
1	30:45	30	45
2	40:95	40	95
3	89:65	89	65
4	45:63	45	63
5	52:42	52	42

## 有时候分隔符搞不定的，可以用正则表达式，将捕获的每组弄成一列
dfc <- tibble(x = c("1-12week", "1-10wk", "5-12w", "01-05weeks"))
dfc

dfc %>% tidyr::extract(
  x, 
  c("start", "end", "letter"), "(\\d+)-(\\d+)([a-z]+)",
  remove = FALSE
)

A tibble: 4 × 1

x
<chr>
1-12week
1-10wk
5-12w
01-05weeks

A tibble: 4 × 4

x	start	end	letter
<chr>	<chr>	<chr>	<chr>
1-12week	1	12	week
1-10wk	1	10	wk
5-12w	5	12	w
01-05weeks	01	05	weeks

删除缺失值所在行drop_na()与replace_na()

• drop_na()删除有缺失值的所有row，等效于filter(across(everything(), ~!is.na(.x)))，可单独使用 df %>% drop_na()删除df中的缺失值

• 也可指定某列，只删除该列存在缺失值的行

• replace_na() coalesce()替换缺失值 x = replace_na(x, 0)将x列的缺失值替换为0, 可在mutate()等函数中使用

df <- tibble::tribble(
    ~name,     ~type, ~score, ~extra,
  "Alice", "english",     80,     10,
  "Alice",    "math",     NA,      5,
    "Bob", "english",     NA,      9,
    "Bob",    "math",     69,     NA,
  "Carol", "english",     80,     10,
  "Carol",    "math",     90,      5
  )

# 如果score列中有缺失值NA，就删除所在的row
df %>% 
  filter(!is.na(score))

df %>% 
  filter(
    across(score, ~ !is.na(.x))
  )

A tibble: 4 × 4

name	type	score	extra
<chr>	<chr>	<dbl>	<dbl>
Alice	english	80	10
Bob	math	69	NA
Carol	english	80	10
Carol	math	90	5

Warning message:
“Using `across()` in `filter()` was deprecated in dplyr 1.0.8.
ℹ Please use `if_any()` or `if_all()` instead.”

A tibble: 4 × 4

name	type	score	extra
<chr>	<chr>	<dbl>	<dbl>
Alice	english	80	10
Bob	math	69	NA
Carol	english	80	10
Carol	math	90	5

# 所有列，如果有缺失值NA，就删除所在的row
df %>% 
  filter(
    across(everything(), ~!is.na(.x))
  )

# 更简便的方法
df %>% 
  drop_na()
# 也可指定某咧
df %>% 
  drop_na(score)

Warning message:
“Using `across()` in `filter()` was deprecated in dplyr 1.0.8.
ℹ Please use `if_any()` or `if_all()` instead.”

A tibble: 3 × 4

name	type	score	extra
<chr>	<chr>	<dbl>	<dbl>
Alice	english	80	10
Carol	english	80	10
Carol	math	90	5

A tibble: 3 × 4

name	type	score	extra
<chr>	<chr>	<dbl>	<dbl>
Alice	english	80	10
Carol	english	80	10
Carol	math	90	5

A tibble: 4 × 4

name	type	score	extra
<chr>	<chr>	<dbl>	<dbl>
Alice	english	80	10
Bob	math	69	NA
Carol	english	80	10
Carol	math	90	5

# 将score替换为0分
df
df %>% 
  mutate(score = replace_na(score, 0))
df %>% 
  mutate(score = coalesce(score, 0))

A tibble: 6 × 4

name	type	score	extra
<chr>	<chr>	<dbl>	<dbl>
Alice	english	80	10
Alice	math	NA	5
Bob	english	NA	9
Bob	math	69	NA
Carol	english	80	10
Carol	math	90	5

A tibble: 6 × 4

name	type	score	extra
<chr>	<chr>	<dbl>	<dbl>
Alice	english	80	10
Alice	math	0	5
Bob	english	0	9
Bob	math	69	NA
Carol	english	80	10
Carol	math	90	5

A tibble: 6 × 4

name	type	score	extra
<chr>	<chr>	<dbl>	<dbl>
Alice	english	80	10
Alice	math	0	5
Bob	english	0	9
Bob	math	69	NA
Carol	english	80	10
Carol	math	90	5

df %>% 
  mutate(
    across(c(score, extra), ~ replace_na(.x,0))
  )
df %>% 
  mutate(
    across(c(score, extra), ~ coalesce(.x,0)))

A tibble: 6 × 4

name	type	score	extra
<chr>	<chr>	<dbl>	<dbl>
Alice	english	80	10
Alice	math	0	5
Bob	english	0	9
Bob	math	69	0
Carol	english	80	10
Carol	math	90	5

A tibble: 6 × 4

name	type	score	extra
<chr>	<chr>	<dbl>	<dbl>
Alice	english	80	10
Alice	math	0	5
Bob	english	0	9
Bob	math	69	0
Carol	english	80	10
Carol	math	90	5

df %>% 
  mutate(
    score = replace_na(score, mean(score, na.rm=TRUE))
  )

## 也可用if_else()来做
df %>% 
  mutate(
    score = if_else(is.na(score), mean(score, na.rm=TRUE), score)
  )

A tibble: 6 × 4

name	type	score	extra
<chr>	<chr>	<dbl>	<dbl>
Alice	english	80.00	10
Alice	math	79.75	5
Bob	english	79.75	9
Bob	math	69.00	NA
Carol	english	80.00	10
Carol	math	90.00	5

A tibble: 6 × 4

name	type	score	extra
<chr>	<chr>	<dbl>	<dbl>
Alice	english	80.00	10
Alice	math	79.75	5
Bob	english	79.75	9
Bob	math	69.00	NA
Carol	english	80.00	10
Carol	math	90.00	5