使用 mutate_at 创建新变量，同时保留原始变量 [英] Create new variables with mutate_at while keeping the original ones
问题描述
考虑这个简单的例子:
库(dplyr)数据帧 <- 数据帧(helloo = c(1,2,3,4,5,6),ooooHH = c(1,1,1,2,2,2),ahaaa = c(200,400,120,300,100,100))# 小费:6 x 3你好 ooooHH ahaaa<dbl><dbl><dbl>1 1 1 2002 2 1 4003 3 1 1204 4 2 3005 5 2 1006 6 2 100这里我想将函数 ntile 应用到包含 oo 的所有列，但我希望这些新列被称为 cat + 对应的列.
我知道我可以做到这一点
dataframe %>% mutate_at(vars(contains('oo')), .funs = funs(ntile(., 2)))# 小费:6 x 3你好 ooooHH ahaaa<int><int><dbl>1 1 1 2002 1 1 4003 1 1 1204 2 2 3005 2 2 1006 2 2 100但我需要的是这个
# tibble: 8 x 5你好 ooooHH ahaaa cat_helloo cat_ooooHH<dbl><dbl><dbl><int><int>1 1 1 200 1 12 2 1 400 1 13 3 1 120 1 14 4 2 300 2 25 5 2 100 2 26 5 2 100 2 27 6 2 100 2 28 6 2 100 2 2有没有不需要存储中间数据并合并回原始数据帧的解决方案?
更新 2020-06 for dplyr 1.0.0
从 dplyr 1.0.0 开始，across() 函数取代了诸如 mutate_at() 等函数的范围变体".代码在 across() 中看起来应该很熟悉，它嵌套在 mutate() 中.
为您在列表中给出的函数添加名称会将函数名称添加为后缀.
数据帧%>%变异(跨(包含('oo')，.fns = list(cat = ~ntile(., 2))) )# 小费:6 x 5你好 ooooHH 啊哈 hello_cat ooooHH_cat<dbl><dbl><dbl><int><int>1 1 1 200 1 12 2 1 400 1 13 3 1 120 1 14 4 2 300 2 25 5 2 100 2 26 6 2 100 2 2使用 across() 中的 .names 参数在 1.0.0 中更改新列名称更容易一些.这是将函数名称添加为前缀而不是后缀的示例.这使用胶水语法.
数据帧%>%变异(跨(包含('oo')，.fns = list(cat = ~ntile(., 2)),.names = "{fn}_{col}" ) )# 小费:6 x 5你好 ooooHH ahaaa cat_helloo cat_ooooHH<dbl><dbl><dbl><int><int>1 1 1 200 1 12 2 1 400 1 13 3 1 120 1 14 4 2 300 2 25 5 2 100 2 26 6 2 100 2 2mutate_at() 的原始答案
编辑以反映 dplyr 中的更改.从 dplyr 0.8.0 开始，funs() 已弃用，应使用 list() 和 ~ 代替.
您可以为传递给 .funs 的列表中的函数命名，以创建带有后缀名称的新变量.
dataframe %>% mutate_at(vars(contains('oo')), .funs = list(cat = ~ntile(., 2)))# 小费:6 x 5你好 ooooHH 啊哈 hello_cat ooooHH_cat<dbl><dbl><dbl><int><int>1 1 1 200 1 12 2 1 400 1 13 3 1 120 1 14 4 2 300 2 25 5 2 100 2 26 6 2 100 2 2如果您希望将其作为前缀，则可以使用 rename_at 来更改名称.
数据帧%>%mutate_at(vars(contains('oo')), .funs = list(cat = ~ntile(., 2))) %>%rename_at( vars( contains( "_cat") ), list( ~paste("cat", gsub("_cat", "", .), sep = "_") ) )# 小费:6 x 5你好 ooooHH ahaaa cat_helloo cat_ooooHH<dbl><dbl><dbl><int><int>1 1 1 200 1 12 2 1 400 1 13 3 1 120 1 14 4 2 300 2 25 5 2 100 2 26 6 2 100 2 2带有 funs() 的先前代码来自 dplyr 的早期版本:
数据帧%>%mutate_at(vars(contains('oo')), .funs = funs(cat = ntile(., 2))) %>%rename_at( vars( contains( "_cat") ), funs( paste("cat", gsub("_cat", "", .), sep = "_") ) )Consider this simple example:
library(dplyr)
dataframe <- data_frame(helloo = c(1,2,3,4,5,6),
ooooHH = c(1,1,1,2,2,2),
ahaaa = c(200,400,120,300,100,100))
# A tibble: 6 x 3
helloo ooooHH ahaaa
<dbl> <dbl> <dbl>
1 1 1 200
2 2 1 400
3 3 1 120
4 4 2 300
5 5 2 100
6 6 2 100
Here I want to apply the function ntile to all the columns that contains oo, but I would like these new columns to be called cat + the corresponding column.
I know I can do this
dataframe %>% mutate_at(vars(contains('oo')), .funs = funs(ntile(., 2)))
# A tibble: 6 x 3
helloo ooooHH ahaaa
<int> <int> <dbl>
1 1 1 200
2 1 1 400
3 1 1 120
4 2 2 300
5 2 2 100
6 2 2 100
But what I need is this
# A tibble: 8 x 5
helloo ooooHH ahaaa cat_helloo cat_ooooHH
<dbl> <dbl> <dbl> <int> <int>
1 1 1 200 1 1
2 2 1 400 1 1
3 3 1 120 1 1
4 4 2 300 2 2
5 5 2 100 2 2
6 5 2 100 2 2
7 6 2 100 2 2
8 6 2 100 2 2
Is there a solution that does NOT require to store the intermediate data, and merge back to the original dataframe?
Update 2020-06 for dplyr 1.0.0
Starting in dplyr 1.0.0, the across() function supersedes the "scoped variants" of functions such as mutate_at(). The code should look pretty familiar within across(), which is nested inside mutate().
Adding a name to the function(s) you give in the list adds the function name as a suffix.
dataframe %>%
mutate( across(contains('oo'),
.fns = list(cat = ~ntile(., 2))) )
# A tibble: 6 x 5
helloo ooooHH ahaaa helloo_cat ooooHH_cat
<dbl> <dbl> <dbl> <int> <int>
1 1 1 200 1 1
2 2 1 400 1 1
3 3 1 120 1 1
4 4 2 300 2 2
5 5 2 100 2 2
6 6 2 100 2 2
Changing the new columns names is a little easier in 1.0.0 with the .names argument in across(). Here is an example of adding the function name as a prefix instead of a suffix. This uses glue syntax.
dataframe %>%
mutate( across(contains('oo'),
.fns = list(cat = ~ntile(., 2)),
.names = "{fn}_{col}" ) )
# A tibble: 6 x 5
helloo ooooHH ahaaa cat_helloo cat_ooooHH
<dbl> <dbl> <dbl> <int> <int>
1 1 1 200 1 1
2 2 1 400 1 1
3 3 1 120 1 1
4 4 2 300 2 2
5 5 2 100 2 2
6 6 2 100 2 2
Original answer with mutate_at()
Edited to reflect changes in dplyr. As of dplyr 0.8.0, funs() is deprecated and list() with ~ should be used instead.
You can give names to the functions to the list you pass to .funs to make new variables with the names as suffixes attached.
dataframe %>% mutate_at(vars(contains('oo')), .funs = list(cat = ~ntile(., 2)))
# A tibble: 6 x 5
helloo ooooHH ahaaa helloo_cat ooooHH_cat
<dbl> <dbl> <dbl> <int> <int>
1 1 1 200 1 1
2 2 1 400 1 1
3 3 1 120 1 1
4 4 2 300 2 2
5 5 2 100 2 2
6 6 2 100 2 2
If you want it as a prefix instead, you could then use rename_at to change the names.
dataframe %>%
mutate_at(vars(contains('oo')), .funs = list(cat = ~ntile(., 2))) %>%
rename_at( vars( contains( "_cat") ), list( ~paste("cat", gsub("_cat", "", .), sep = "_") ) )
# A tibble: 6 x 5
helloo ooooHH ahaaa cat_helloo cat_ooooHH
<dbl> <dbl> <dbl> <int> <int>
1 1 1 200 1 1
2 2 1 400 1 1
3 3 1 120 1 1
4 4 2 300 2 2
5 5 2 100 2 2
6 6 2 100 2 2
Previous code with funs() from earlier versions of dplyr:
dataframe %>%
mutate_at(vars(contains('oo')), .funs = funs(cat = ntile(., 2))) %>%
rename_at( vars( contains( "_cat") ), funs( paste("cat", gsub("_cat", "", .), sep = "_") ) )
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