《R包学习》专栏·第2篇

文| R学习者

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这是dplyr包系列第二篇文章。第一篇文章《dplyr包-列选择的方法 》总结了如何使用dplyr包有效地选择感兴趣的列。本文会总结列变换的方法，以实现按着需求对列做合理处理，例如：衍生新的列，列的离散化，列的拆分与合并等。

列的变换操作，会介绍如下内容：

1. 
   增加一列
   


2. 
   增加多列
   


3. 
   列的离散化操作
   


4. 
   列的拆分
   


5. 
   列的合并
   


6. 
   列的集成
   


7. 
   数据重塑
   

我们以R自带数据，采用管道的操作，演示列变换的具体操作和实现。

第一步：加载R包和数据集

1library(pacman)

2p_load(tidyverse)

3# 使用msleep数据集做示例

4msleep %>% glimpse

第二步：列变换操作实例

1 增加一列，使用mutate函数

1# 1 增加一列

2msleep%>%

3select(name, sleep_total) %>%

4mutate(sleep_total_min = sleep_total * 60) %>%

5head

增加的新列，也可使用聚合函数,计算平均值、中位数、最大值、最小值、标准差等。

1msleep%>%

2select(name, sleep_total) %>%

3mutate(sleep_total_vs_AVG = sleep_total - round(mean(sleep_total), 1)) %>%

4head

若要基于两列或者多列的值按着行来做聚合以生成新的列，可以参照如下方法实现。

1# 方法1 使用rowwise 指定按着每行做聚合计算

2msleep%>%

3select(name, contains( "sleep")) %>%

4rowwise %>%

5mutate(avg1 = mean(c(sleep_rem, sleep_cycle))) %>%

6head

7# 方法2

8msleep %>%

9select(name, contains( "sleep")) %>%

10mutate(avg1 = (sleep_rem + sleep_cycle)/ 2) %>%

11head

思考题：

1）若要增加一列，记录brainw大于4，就为NA，否则为原值，请问如何实现？

2）请问下面这个代码片段实现了什么功能？

1# ？这个代码片段的作用是什么

2msleep%>%

3select(name) %>%

4mutate(name_last_word = tolower(str_extract(name, pattern = "w+$"))) %>%

5head

2 增加多列，使用mutate_all,mutate_if和mutate_at函数

2.1 mutate_all,对所有列操作

1）把所有列小写化

1msleep%>%

2select(name, genus, vore) %>%

3mutate_all(tolower) %>%

4head

2）对所有列做数据清洗工作

1msleep_ohno<- msleep %>%

2select(name, genus, vore) %>%

3mutate_all(~paste(., " /n "))

4msleep_ohno %>% head

5msleep_corr <- msleep_ohno %>%

6mutate_all(~str_replace_all(., "/n", "")) %>%

7mutate_all(str_trim)

8msleep_corr %>% head

2.2 mutate_if,对布尔值为真的列进行操作

常用的布尔值判断函数

is.numeric,is.integer,is.double,is.logical,is.factor,lubridate::POSIXt或者lubridate::is.Date

例如，对所有数值型变量取一位小数

1# 自定义函数

2my_func <- function(x){

3return(round(x,1))

4}

5msleep%>%

6select( name, sleep_total:bodywt) %>%

7mutate_if( is.numeric, my_func) %>%

8head

9

2.3 mutate_at, 在vars参数中指定要操作的列

具体使用方法，第一步，在vars指定需要操作的列，第二步，使用波浪线引入所需操作的函数。

1msleep%>%

2select(name, sleep_total:awake) %>%

3mutate_at(vars(contains( "sleep")), ~(.* 60)) %>%

4rename_at(vars(contains( "sleep")), ~paste0(., "_min")) %>%

5head

3 列的离散化操作

1） 重编码操作，使用recode函数或者recode_factor函数

1msleep %>%

2mutate(conservation2 = recode(conservation,

3"en"= "Endangered",

4"lc"= "Least_Concern",

5"domesticated"= "Least_Concern",

6. default= "other")) %>%

7count(conservation2)

8msleep %>%

9mutate(conservation2 = recode_factor(conservation,

10"en"= "Endangered",

11"lc"= "Least_Concern",

12"domesticated"= "Least_Concern",

13. default= "other",

14.missing = "no data",

15.ordered = TRUE)) %>%

16count(conservation2)

2） 创建新的离散列（二元情形），使用ifelse函数

1msleep%>%

2select(name, sleep_total) %>%

3mutate(sleep_time = ifelse(sleep_total > 10, "long", "short")) %>%

4head

3） 创建新的离散列（多元情形），使用case_when函数

1msleep%>%

2select(name, sleep_total) %>%

3mutate(sleep_total_discr = case_when(

4sleep_total > 13~ "verylong ",

5sleep_total > 10 ~ " long ",

6sleep_total > 7 ~ " limited ",

7TRUE ~ " short ")) %>%

8mutate(sleep_total_discr = factor(sleep_total_discr,

9levels = c(" short ", "limited ",

10" long ", "very long "))) %>%

11head

12

4 列的拆分

使用separate函数

1(conservation_expl <- read_csv( "./datasets/conservation_explanation.csv"))

2(conservation_table <- conservation_expl %>%

3separate(`conservation abbreviation`,

4into= c( "abbreviation", "deion"), sep = " = "))

5 列的合并

使用unite函数

1conservation_table%>%

2unite(united_col, abbreviation, deion, sep= ": ")

6 列的集成

使用关联函数，左连接left_join,内连接inner_join等。

1msleep %>%

2select(name, conservation) %>%

3mutate(conservation = toupper(conservation)) %>%

4left_join(conservation_table, by= c( "conservation"= "abbreviation")) %>%

5mutate(deion = ifelse( is.na(deion), conservation, deion)) %>%

6head

7 数据重塑

使用gather函数把宽表转换成长表，使用spread函数把长表转换成宽表。

1）gather示例

1# 1）宽表变为长表

2msleep %>%

3select(name, contains( "sleep")) %>%

4gather(key = "sleep_measure", value= "time", -name) %>%

5head

思考题：下面这个代码片段，有什么作用？

1(msleep_g <- msleep %>%

2select(name, contains( "sleep")) %>%

3gather( key= "sleep_measure", value = "time", -name, factor_key = TRUE))

2）spread示例

1# 2) 长表变为宽表

2msleep_g%>%

3spread(sleep_measure, time) %>%

4head

完整代码：

1# dplyr包列变换的方法

2

3# 第一步：加载R包和数据集

4library(pacman)

5p_load(tidyverse)

6# 使用msleep数据集做示例

7msleep %>% glimpse

8

9# 第二步：列变换操作实例

10# 1 增加新的一列

11msleep %>%

12select(name, sleep_total) %>%

13mutate(sleep_total_min = sleep_total * 60) %>%

14head

15

16msleep %>%

17select(name, sleep_total) %>%

18mutate(sleep_total_vs_AVG = sleep_total - round(mean(sleep_total), 1)) %>%

19head

20

21# 方法1 使用rowwise 指定按着每行做聚合计算

22msleep %>%

23select(name, contains( "sleep")) %>%

24rowwise %>%

25mutate(avg1 = mean(c(sleep_rem, sleep_cycle))) %>%

26head

27# 方法2

28msleep %>%

29select(name, contains( "sleep")) %>%

30mutate(avg1 = (sleep_rem + sleep_cycle)/ 2) %>%

31head

32

33msleep %>%

34select(name, brainwt) %>%

35mutate(brainwt2 = ifelse(brainwt > 4, NA, brainwt)) %>%

36head

37

38

39msleep %>%

40select(name) %>%

41mutate(name_last_word = tolower(str_extract(name, pattern = "w+$"))) %>%

42head

43

44# 2 增加多列

45# mutate_all函数

46msleep %>%

47select(name, genus, vore) %>%

48mutate_all(tolower) %>%

49head

50

51msleep_ohno <- msleep %>%

52select(name, genus, vore) %>%

53mutate_all(~paste(., " /n "))

54msleep_ohno %>% head

55msleep_corr <- msleep_ohno %>%

56mutate_all(~str_replace_all(., "/n", "")) %>%

57mutate_all(str_trim)

58msleep_corr %>% head

59

60# mutate_if函数

61my_func <- function(x){

62return(round(x, 1))

63}

64msleep %>%

65select(name, sleep_total:bodywt) %>%

66mutate_if( is.numeric, my_func) %>%

67head

68

69# mutate_at函数

70msleep %>%

71select(name, sleep_total:awake) %>%

72mutate_at(vars(contains( "sleep")), ~(.* 60)) %>%

73head

74

75msleep %>%

76select(name, sleep_total:awake) %>%

77mutate_at(vars(contains( "sleep")), ~(.* 60)) %>%

78rename_at(vars(contains( "sleep")), ~paste0(., "_min")) %>%

79head

80

81# 3 列的离散化操作

82msleep %>%

83mutate(conservation2 = recode(conservation,

84"en"= "Endangered",

85"lc"= "Least_Concern",

86"domesticated"= "Least_Concern",

87. default= "other")) %>%

88count(conservation2)

89

90msleep %>%

91mutate(conservation2 = recode_factor(conservation,

92"en"= "Endangered",

93"lc"= "Least_Concern",

94"domesticated"= "Least_Concern",

95. default= "other",

96.missing = "no data",

97.ordered = TRUE)) %>%

98count(conservation2)

99

100msleep %>%

101select(name, sleep_total) %>%

102mutate(sleep_time = ifelse(sleep_total > 10, "long", "short")) %>%

103head

104

105msleep %>%

106select(name, sleep_total) %>%

107mutate(sleep_total_discr = case_when(

108sleep_total > 13~ "very long",

109sleep_total > 10~ "long",

110sleep_total > 7~ "limited",

111TRUE ~ "short")) %>%

112mutate(sleep_total_discr = factor(sleep_total_discr,

113levels = c( "short", "limited",

114"long", "very long"))) %>%

115head

116

117msleep %>%

118mutate(silly_groups = case_when(

119brainwt < 0.001~ "light_headed",

120sleep_total > 10~ "lazy_sleeper",

121is.na(sleep_rem) ~ "absent_rem",

122TRUE ~ "other")) %>%

123count(silly_groups)

124

125# 4 列的拆分

126(conservation_expl <- read_csv( "./datasets/conservation_explanation.csv"))

127(conservation_table <- conservation_expl %>%

128separate(`conservation abbreviation`,

129into= c( "abbreviation", "deion"), sep = " = "))

130

131# 5 列的合并

132conservation_table %>%

133unite(united_col, abbreviation, deion, sep= ": ")

134

135# 6 列的集成

136# 使用连接函数

137msleep %>%

138select(name, conservation) %>%

139mutate(conservation = toupper(conservation)) %>%

140left_join(conservation_table, by= c( "conservation"= "abbreviation")) %>%

141mutate(deion = ifelse( is.na(deion), conservation, deion)) %>%

142head

143

144# 7 列重塑

145# 1）宽表变为长表

146msleep %>%

147select(name, contains( "sleep")) %>%

148gather(key = "sleep_measure", value= "time", -name) %>%

149

150

151(msleep_g <- msleep %>%

152select(name, contains( "sleep")) %>%

153gather(key = "sleep_measure", value= "time", -name, factor_key = TRUE))

154

155# 2) 长表变为宽表

156msleep_g %>%

157spread(sleep_measure, time) %>%

158head

参考资料：

1https://suzan.rbind.io/2018/02/dplyr-tutorial-2/