R语言基础入门(4) mutate函数创建新列
2021-05-20 本文已影响0人
R语言数据分析指南
今天继续介绍dplyr包中的重要函数mutate，其基本功能为创建新列；mutate中的选项几乎是无穷无尽的，可以通过各种函数之间的组合来对数据集做任意的处理，下面通过具体的案例来进行演示
这次我们使用R内置的数据集msleep，其中包括哺乳动物的睡眠时间。让我们首先加载包并查看数据：
library(tidyverse)
msleep
name genus vore order conservation sleep_total sleep_rem sleep_cycle
<chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 Cheetah Acino~ carni Carn~ lc 12.1 NA NA
2 Owl mo~ Aotus omni Prim~ NA 17 1.8 NA
3 Mounta~ Aplod~ herbi Rode~ nt 14.4 2.4 NA
4 Greate~ Blari~ omni Sori~ lc 14.9 2.3 0.133
mutate 基础操作
最简单的的操作就是根据其他列中的值进行计算。在示例代码中，我们将睡眠数据从以小时为单位更改为分钟为单位
msleep %>%
select(name,sleep_total) %>%
mutate(sleep_total_min = sleep_total * 60)
name sleep_total sleep_total_min
<chr> <dbl> <dbl>
1 Cheetah 12.1 726
2 Owl monkey 17 1020
3 Mountain beaver 14.4 864
下列代码创建了两列新列：一列显示了睡眠时间与平均睡眠时间的差异，另一列显示了与睡眠时间最少的动物之间的差异；round( )对数据进行四舍五入操作
msleep %>%
select(name, sleep_total) %>%
mutate(AVG = sleep_total - round(mean(sleep_total), 1),
MIN = sleep_total - min(sleep_total))
# A tibble: 83 x 4
name sleep_total AVG MIN
<chr> <dbl> <dbl> <dbl>
1 Cheetah 12.1 1.7 10.2
2 Owl monkey 17 6.6 15.1
3 Mountain beaver 14.4 4 12.5
选择特定列按行求均值，rowwise( )说明按行进行操作
msleep %>%
select(name, contains("sleep")) %>%
rowwise() %>%
mutate(avg = mean(c(sleep_rem,sleep_cycle)))
name sleep_total sleep_rem sleep_cycle avg
<chr> <dbl> <dbl> <dbl> <dbl>
1 Cheetah 12.1 NA NA NA
2 Owl monkey 17 1.8 NA NA
3 Mountain beaver 14.4 2.4 NA NA
4 Greater short-tail~ 14.9 2.3 0.133 1.22
通过ifelse判断语句对数据进行操作，如果brainwt > 4返回NA,不满足此条件返回原值
msleep %>%
select(name, brainwt) %>%
mutate(brainwt2 = ifelse(brainwt > 4, NA, brainwt)) %>%
arrange(desc(brainwt))
name brainwt brainwt2
<chr> <dbl> <dbl>
1 African elephant 5.71 NA
2 Asian elephant 4.60 NA
3 Human 1.32 1.32
4 Horse 0.655 0.655
也可以结合使用stringr的功能或正则表达式来对字符串列进行操作；
示例代码将返回动物名称的最后一个单词，并使其小写
msleep %>%
select(name) %>%
mutate(name_last_word = tolower(str_extract(name, pattern = "\\w+$")))
name name_last_word
<chr> <chr>
1 Cheetah cheetah
2 Owl monkey monkey
3 Mountain beaver beaver
对多列同时进行操作
- mutate_all() 将对所有列进行操作
- mutate_if()首先需要一个返回布尔值，如果是T，则将在这些变量上执行mutate指令
- mutate_at()要求在vars() 参数内指定要进行改变的列
将所有数据转换为小写：
msleep %>% mutate_all(tolower)
name genus vore order conservation sleep_total sleep_rem
<chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 cheetah acin~ carni carn~ lc 12.1 NA
2 owl mo~ aotus omni prim~ NA 17 1.8
3 mounta~ aplo~ herbi rode~ nt 14.4 2.4
所有列添加" /n "
msleep %>% mutate_all(~paste(., " /n "))
将" /n "全部替换为空
msleep_ohno <- msleep %>% mutate_all(~paste(., " /n "))
msleep_ohno %>%
mutate_all(~str_replace_all(., "/n", "")) %>%
mutate_all(str_trim)
mutate_if()对数据进行判断
如果数据类型是数值，对其进行四舍五入操作
msleep %>%
select(name, sleep_total:bodywt) %>%
mutate_if(is.numeric, round)
name sleep_total sleep_rem sleep_cycle awake brainwt bodywt
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Cheetah 12 NA NA 12 NA 50
2 Owl monkey 17 2 NA 7 0 0
3 Mountain beaver 14 2 NA 10 NA 1
mutate_at( )对特定列进行操作
对列名含有sleep的进行操作
msleep %>%
select(name, sleep_total:awake) %>%
mutate_at(vars(contains("sleep")), ~(.*60))
name sleep_total sleep_rem sleep_cycle awake
<chr> <dbl> <dbl> <dbl> <dbl>
1 Cheetah 726 NA NA 11.9
2 Owl monkey 1020 108 NA 7
3 Mountain beaver 864 144 NA 9.6
更改列名
msleep %>%
select(name, sleep_total:awake) %>%
mutate_at(vars(contains("sleep")), ~(.*60)) %>%
rename_at(vars(contains("sleep")), ~paste0(.,"_min"))
name sleep_total_min sleep_rem_min sleep_cycle_min awake
<chr> <dbl> <dbl> <dbl> <dbl>
1 Cheetah 726 NA NA 11.9
2 Owl monkey 1020 108 NA 7
3 Mountain beaver 864 144 NA 9.6
保留原始数据
msleep %>%
select(name, sleep_total:awake) %>%
mutate_at(vars(contains("sleep")), funs(min = .*60))
name sleep_total sleep_rem sleep_cycle awake sleep_total_min sleep_rem_min sleep_cycle_min
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Cheetah 12.1 NA NA 11.9 726 NA NA
2 Owl monkey 17 1.8 NA 7 1020 108 NA
ifelse创建2个级别的离散列
msleep %>%
select(name, sleep_total) %>%
mutate(sleep_time = ifelse(sleep_total > 10, "long", "short"))
name sleep_total sleep_time
<chr> <dbl> <chr>
1 Cheetah 12.1 long
2 Owl monkey 17 long
3 Mountain beaver 14.4 long
case_when创建多级离散列
此函数在后续数据清洗中有大有，需要多多练习
msleep %>%
select(name, sleep_total) %>%
mutate(sleep_total_discr = case_when(
sleep_total > 13 ~ "very long",
sleep_total > 10 ~ "long",
sleep_total > 7 ~ "limited",
TRUE ~ "short"))
name sleep_total sleep_total_discr
<chr> <dbl> <chr>
1 Cheetah 12.1 long
2 Owl monkey 17 very long
3 Mountain beaver 14.4 very long
4 Greater short-tailed shrew 14.9 very long
将数据转化为NA
msleep %>%
select(name:order) %>%
na_if("omni")
name genus vore order
<chr> <chr> <chr> <chr>
1 Cheetah Acinonyx carni Carnivora
2 Owl monkey Aotus NA Primates
3 Mountain beaver Aplodontia herbi Rodentia
4 Greater short-tailed shrew Blarina NA Soricomorpha
