R 编程中的 tidyr 包
R 语言中的包是 R 函数、编译代码和示例数据的集合。它们存储在R 环境中名为“库”的目录下。默认情况下，R 在安装过程中会安装一组包。 R 中最重要的包之一是tidyr包。 tidyr包的唯一目的是简化创建整洁数据的过程。 Tidy data 描述了一种存储数据的标准方法，该方法在整个tidyverse中尽可能使用。如果您曾经确保您的数据是整洁的，那么您将花费更少的时间来处理这些工具，而将更多的时间用于您的分析。
安装
要在 R 编程中使用包，必须首先安装包。可以使用命令install.packages(“packagename”)完成此任务。要安装整个tidyverse包，请输入：
install.packages("tidyverse")
或者，要安装tidyr包，请输入：
install.packages("tidyr")
要从 GitHub 安装开发版本，请键入：
# install.packages("devtools")
devtools::install_github("tidyverse/tidyr")
tidyr 包中的重要动词函数
数据集：
在进入重要的动词函数之前，让我们先准备数据集。定义一个数据集tidy_dataframe ，其中包含有关特定组中人员频率的数据。
R
# load the tidyr package
library(tidyr)
n = 10
# creating a data frame
tidy_dataframe = data.frame(
S.No = c(1:n),
Group.1 = c(23, 345, 76, 212, 88,
199, 72, 35, 90, 265),
Group.2 = c(117, 89, 66, 334, 90,
101, 178, 233, 45, 200),
Group.3 = c(29, 101, 239, 289, 176,
320, 89, 109, 199, 56))
# print the elements of the data frame
tidy_dataframeR
# using gather() function on tidy_dataframe
long <- tidy_dataframe %>%
gather(Group, Frequency,
Group.1:Group.3)
# print the data frame in a long format
longR
# import tidyr package
library(tidyr)
long <- tidy_dataframe %>%
gather(Group, Frequency,
Group.1:Group.3)
# use separate() function to make data wider
separate_data <- long %>%
separate(Group, c("Allotment",
"Number"))
# print the wider format
separate_dataR
# import tidyr package
library(tidyr)
long <- tidy_dataframe %>%
gather(Group, Frequency,
Group.1:Group.3)
# use separate() function to make data wider
separate_data <- long %>%
separate(Group, c("Allotment",
"Number"))
# use unite() function to glue
# Allotment and Number columns
unite_data <- separate_data %>%
unite(Group, Allotment,
Number, sep = ".")
# print the new data frame
unite_dataR
# import tidyr package
library(tidyr)
long <- tidy_dataframe %>%
gather(Group, Frequency,
Group.1:Group.3)
# use separate() function to make data wider
separate_data <- long %>%
separate(Group, c("Allotment",
"Number"))
# use unite() function to glue
# Allotment and Number columns
unite_data <- separate_data %>%
unite(Group, Allotment,
Number, sep = ".")
# use unite() function to make data wider
back_to_wide <- unite_data %>%
spread(Group, Frequency)
# print the new data frame
back_to_wideR
# import tidyr package
library(tidyr)
df <- tidy_dataframe
# nest column Group.1 in
# tidy_dataframe using nest()
df %>% nest(data = c(Group.1))R
# import the tidyr package
library(tidyr)
df <- iris
names(iris)
# nesting the species column in
# the df data frame using nest()
head(df %>% nest(data = c(Species))) # Output (i)
# unnesting the species column
# in the df data frame using unnest()
head(df %>% unnest(Species,.drop = NA,
.preserve = NULL)) # Output (ii)R
# import the tidyr package
df <- data.frame(Month = 1:6,
Year = c(2000, rep(NA, 5)))
# print the df data frame
df # Output (i)
# use fill() to fill missing values in
# Year column in df data frame
df %>% fill(Year) # Output (ii)R
# import the tidyr package
library(tidyr)
# creating a numeric vector
num_vec <- c(1, 7, 9, 14, 19, 20)
# use full_seq() to fill missing
# values in num_vec
full_seq(num_vector, 1)R
# import tidyr package
library(tidyr)
# create a tibble df with missing values
df <- tibble(S.No = c(1:10),
Name = c('John', 'Smith', 'Peter',
'Luke', 'King', rep(NA, 5)))
# print df tibble
df # Output (i)
# use drop_na() to drop columns
# in df with missing values
df %>% drop_na(Name) # Output (ii)R
# import tidyr package
library(tidyr)
df <- data.frame(S.No = c(1:10),
Name = c('John', 'Smith',
'Peter', 'Luke',
'King', rep(NA, 5)))
df # Output (i)
# use replace_na() to replace missing values or na
df %>% replace_na(list(Name = 'Henry')) # Output (ii)输出：
S.No Group.1 Group.2 Group.3
1 1 23 117 29
2 2 345 89 101
3 3 76 66 239
4 4 212 334 289
5 5 88 90 176
6 6 199 101 320
7 7 72 178 89
8 8 35 233 109
9 9 90 45 199
10 10 265 200 56
tidyr 包提供了可用于数据清洗的各种重要功能。那些是：
- 收集（）函数：它需要多列并将它们收集成键值对。基本上它使“宽”数据更长。 gather()函数将获取多个列并将它们折叠成键值对，并根据需要复制所有其他列。
Syntax:
gather(data, key = “key”, value = “value”, …, na.rm = FALSE, convert = FALSE, factor_key = FALSE)
Parameter | Description |
| data | the data frame. |
| key, value | the names of new key and value columns, as strings or as symbols. |
| ……. | the selection of columns. If left empty, all variables are selected. You can supply bare variable names, select all variables between x and z with x:z, exclude y with -y. |
| na.rm | if set TRUE, it will remove rows from output where the value column is NA. |
| convert | is set TRUE, it will automatically run type.convert() on the key column. This is useful if the column types are actually numeric, integer, or logical. |
| factor_key | if FALSE, the default, the key values will be stored as a character vector. If TRUE, will be stored as a factor, which preserves the original ordering of the columns. |
例子：
现在为了更好地理解，我们将使用collect()函数使我们的数据变长。
R
# using gather() function on tidy_dataframe
long <- tidy_dataframe %>%
gather(Group, Frequency,
Group.1:Group.3)
# print the data frame in a long format
long
输出：
S.No Group Frequency
1 1 Group.1 23
2 2 Group.1 345
3 3 Group.1 76
4 4 Group.1 212
5 5 Group.1 88
6 6 Group.1 199
7 7 Group.1 72
8 8 Group.1 35
9 9 Group.1 90
10 10 Group.1 265
11 1 Group.2 117
12 2 Group.2 89
13 3 Group.2 66
14 4 Group.2 334
15 5 Group.2 90
16 6 Group.2 101
17 7 Group.2 178
18 8 Group.2 233
19 9 Group.2 45
20 10 Group.2 200
21 1 Group.3 29
22 2 Group.3 101
23 3 Group.3 239
24 4 Group.3 289
25 5 Group.3 176
26 6 Group.3 320
27 7 Group.3 89
28 8 Group.3 109
29 9 Group.3 199
30 10 Group.3 56
- 单独（）函数：它将较长的数据转换为更广泛的格式。 separate()函数将单个字符列转换为多列。
Syntax:
separate(data, col, into, sep = ” “, remove = TRUE, convert = FALSE)
Parameter | Description |
| data | A data frame. |
| col | Column name or position. |
| into | Names of new variables to create as character vector. Use NA to omit the variable in the output. |
| sep | The separator between the columns. |
| remove | If set TRUE, it will remove input column from the output data frame. |
| convert | If TRUE, will run type.convert() with as.is = TRUE on new columns. |
例子：
我们可以说使用gather()创建的长数据集适合使用，但我们可以使用separate()进一步分解Group 变量。
R
# import tidyr package
library(tidyr)
long <- tidy_dataframe %>%
gather(Group, Frequency,
Group.1:Group.3)
# use separate() function to make data wider
separate_data <- long %>%
separate(Group, c("Allotment",
"Number"))
# print the wider format
separate_data
输出：
S.No Allotment Number Frequency
1 1 Group 1 23
2 2 Group 1 345
3 3 Group 1 76
4 4 Group 1 212
5 5 Group 1 88
6 6 Group 1 199
7 7 Group 1 72
8 8 Group 1 35
9 9 Group 1 90
10 10 Group 1 265
11 1 Group 2 117
12 2 Group 2 89
13 3 Group 2 66
14 4 Group 2 334
15 5 Group 2 90
16 6 Group 2 101
17 7 Group 2 178
18 8 Group 2 233
19 9 Group 2 45
20 10 Group 2 200
21 1 Group 3 29
22 2 Group 3 101
23 3 Group 3 239
24 4 Group 3 289
25 5 Group 3 176
26 6 Group 3 320
27 7 Group 3 89
28 8 Group 3 109
29 9 Group 3 199
30 10 Group 3 56
- unity()函数：它将两列合并为一列。 unite()函数是一种方便的函数，可以将多个变量值粘贴到一个中。本质上，它将单个观察的两个变量组合成一个变量。
Syntax:
unite(data, col, …, sep = “_”, remove = TRUE)
| Parameter | Description |
| data | A data frame. |
| col | The name of the new column. |
| …. | A selection of desired columns. If empty, all variables are selected. |
| sep | A separator to use between values. |
| remove | If TRUE, remove input columns from output data frame. |
例子：
团结是分开的恭维。要撤消separate() ，我们可以使用unite() ，它将两个变量合并为一个。在这里，我们将使用分隔符“。”合并两列 Group 和 Number。 .
R
# import tidyr package
library(tidyr)
long <- tidy_dataframe %>%
gather(Group, Frequency,
Group.1:Group.3)
# use separate() function to make data wider
separate_data <- long %>%
separate(Group, c("Allotment",
"Number"))
# use unite() function to glue
# Allotment and Number columns
unite_data <- separate_data %>%
unite(Group, Allotment,
Number, sep = ".")
# print the new data frame
unite_data
输出：
S.No Group Frequency
1 1 Group.1 23
2 2 Group.1 345
3 3 Group.1 76
4 4 Group.1 212
5 5 Group.1 88
6 6 Group.1 199
7 7 Group.1 72
8 8 Group.1 35
9 9 Group.1 90
10 10 Group.1 265
11 1 Group.2 117
12 2 Group.2 89
13 3 Group.2 66
14 4 Group.2 334
15 5 Group.2 90
16 6 Group.2 101
17 7 Group.2 178
18 8 Group.2 233
19 9 Group.2 45
20 10 Group.2 200
21 1 Group.3 29
22 2 Group.3 101
23 3 Group.3 239
24 4 Group.3 289
25 5 Group.3 176
26 6 Group.3 320
27 7 Group.3 89
28 8 Group.3 109
29 9 Group.3 199
30 10 Group.3 56
- spread()函数：它有助于将较长的格式重塑为更宽的格式。 spread()函数将键值对分布在多个列中。
Syntax:
spread(data, key, value, fill = NA, convert = FALSE)
| Parameter | Description |
| data | A data frame. |
| key | Column names or positions. |
| value | Column names or positions. |
| fill | If set, missing values will be replaced with this value. |
| convert | If TRUE, type.convert() with asis = TRUE will be run on each of the new columns. |
例子：
我们可以使用spread()函数将数据从 long 转换回 Wide。
R
# import tidyr package
library(tidyr)
long <- tidy_dataframe %>%
gather(Group, Frequency,
Group.1:Group.3)
# use separate() function to make data wider
separate_data <- long %>%
separate(Group, c("Allotment",
"Number"))
# use unite() function to glue
# Allotment and Number columns
unite_data <- separate_data %>%
unite(Group, Allotment,
Number, sep = ".")
# use unite() function to make data wider
back_to_wide <- unite_data %>%
spread(Group, Frequency)
# print the new data frame
back_to_wide
输出：
S.No Group.1 Group.2 Group.3
1 1 23 117 29
2 2 345 89 101
3 3 76 66 239
4 4 212 334 289
5 5 88 90 176
6 6 199 101 320
7 7 72 178 89
8 8 35 233 109
9 9 90 45 199
10 10 265 200 56
- nest()函数：它创建一个包含所有嵌套变量的数据框列表。嵌套隐含地是一种汇总操作。这与其他适用于整个数据集的摘要（尤其是模型）结合使用非常有用。
Syntax: nest(data, …, .key = “data”)
| Parameter | Description |
| data | A data frame. |
| …. | A selection of columns. If empty, all variables are selected. |
| .key | The name of the new column, as a string or symbol. |
示例：让我们尝试从我们在数据集中创建的 tidy_dataframe 嵌套 Group.2 列。
R
# import tidyr package
library(tidyr)
df <- tidy_dataframe
# nest column Group.1 in
# tidy_dataframe using nest()
df %>% nest(data = c(Group.1))
输出：
# A tibble: 10 x 4
S.No Group.1 Group.3 data
1 1 23 29
2 2 345 101
3 3 76 239
4 4 212 289
5 5 88 176
6 6 199 320
7 7 72 89
8 8 35 109
9 9 90 199
10 10 265 56
- unnest()函数：它基本上反转了嵌套操作。它使列表的每个元素成为自己的行。它可以处理包含原子向量、列表或数据帧（但不能混合不同类型）的列表列。
Syntax:
unnest(data, …, .drop = NA, .id = NULL, .sep = NULL, .preserve = NULL)
| Parameter | Description |
| data | A data frame |
| …. | Specification of columns to unnest. If omitted, defaults to all list-columns. |
| .drop | Should additional list columns be dropped? By default, it will drop them if unnesting the specified columns requires the rows to be duplicated. |
| .id | Data frame identifier. |
| .sep | If non-NULL, the names of unnested data frame columns will combine the name of the original list-col with the names from nested data frame, separated by .sep. |
| .preserve | List-columns to preserve in the output. These will be duplicated in the same way as atomic vectors. |
例子：
我们将尝试在 tidyr 包的 iris 数据框中嵌套和取消嵌套 Species 列。
R
# import the tidyr package
library(tidyr)
df <- iris
names(iris)
# nesting the species column in
# the df data frame using nest()
head(df %>% nest(data = c(Species))) # Output (i)
# unnesting the species column
# in the df data frame using unnest()
head(df %>% unnest(Species,.drop = NA,
.preserve = NULL)) # Output (ii)
输出（一）：
# A tibble: 6 x 5
Sepal.Length Sepal.Width Petal.Length Petal.Width data
1 5.1 3.5 1.4 0.2
2 4.9 3 1.4 0.2
3 4.7 3.2 1.3 0.2
4 4.6 3.1 1.5 0.2
5 5 3.6 1.4 0.2
6 5.4 3.9 1.7 0.4
输出（ii）：
# A tibble: 6 x 5
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
- fill()函数：用于使用上一个条目填充选定列中的缺失值。这在值不重复的常见输出格式中很有用，每次更改时都会记录它们。缺失值在原子向量中被替换； NULL 在列表中被替换。
Syntax:
fill(data, …, .direction = c(“down”, “up”))
| Parameter | Description |
| data | A data frame. |
| …. | A selection of columns. If empty, nothing happens. |
| direction | Direction in which to fill missing values. Currently, either “down” (the default) or “up” |
例子：
R
# import the tidyr package
df <- data.frame(Month = 1:6,
Year = c(2000, rep(NA, 5)))
# print the df data frame
df # Output (i)
# use fill() to fill missing values in
# Year column in df data frame
df %>% fill(Year) # Output (ii)
输出（一）：
Month Year
1 1 2000
2 2 NA
3 3 NA
4 4 NA
5 5 NA
6 6 NA
输出（ii）：
Month Year
1 1 2000
2 2 2000
3 3 2000
4 4 2000
5 5 2000
6 6 2000
- full_seq()函数：它基本上填充了向量中应该观察到但没有观察到的缺失值。向量应该是数字。
Syntax: full_seq(x, period, tol = 1e-06)
| Parameter | Description |
| x | A numeric vector. |
| period | Gap between each observation. |
| tol | Numerical tolerance for checking periodicity. |
例子：
R
# import the tidyr package
library(tidyr)
# creating a numeric vector
num_vec <- c(1, 7, 9, 14, 19, 20)
# use full_seq() to fill missing
# values in num_vec
full_seq(num_vector, 1)
输出：
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
- drop_na()函数：此函数删除包含缺失值的行。
Syntax: drop_na(data, …)
| Parameter | Description |
| data | A data frame. |
| …. | A selection of columns. If empty, all variables are selected. |
例子：
R
# import tidyr package
library(tidyr)
# create a tibble df with missing values
df <- tibble(S.No = c(1:10),
Name = c('John', 'Smith', 'Peter',
'Luke', 'King', rep(NA, 5)))
# print df tibble
df # Output (i)
# use drop_na() to drop columns
# in df with missing values
df %>% drop_na(Name) # Output (ii)
输出（一）：
# A tibble: 10 x 2
S.No Name
1 1 John
2 2 Smith
3 3 Peter
4 4 Luke
5 5 King
6 6
7 7
8 8
9 9
10 10
输出（ii）：
# A tibble: 5 x 2
S.No Name
1 1 John
2 2 Smith
3 3 Peter
4 4 Luke
5 5 King
- replace_na()函数：它 替换缺失值。
Syntax: replace_na(data, replace, …)
| Parameter | Description |
| data | A data frame. |
| replace | If data is a data frame, returns a data frame. If data is a vector, returns a vector of class determined by the union of data and replace. |
例子：
R
# import tidyr package
library(tidyr)
df <- data.frame(S.No = c(1:10),
Name = c('John', 'Smith',
'Peter', 'Luke',
'King', rep(NA, 5)))
df # Output (i)
# use replace_na() to replace missing values or na
df %>% replace_na(list(Name = 'Henry')) # Output (ii)
输出（一）：
# A tibble: 10 x 2
S.No Name
1 1 John
2 2 Smith
3 3 Peter
4 4 Luke
5 5 King
6 6
7 7
8 8
9 9
10 10
输出（ii）：
S.No Name
1 1 John
2 2 Smith
3 3 Peter
4 4 Luke
5 5 King
6 6 Henry
7 7 Henry
8 8 Henry
9 9 Henry
10 10 Henry