问题描述
假设我有一个 tibble
,我需要在其中采用多个变量并将其变异为新的多个新变量。
Let's say I have a tibble
where I need to take multiple variables and mutate them into new multiple new variables.
例如,下面是一个简单的小标题:
As an example, here is a simple tibble:
tb <- tribble(
~x, ~y1, ~y2, ~y3, ~z,
1,2,4,6,2,
2,1,2,3,3,
3,6,4,2,1
)
I想要从名称以 y开头的每个变量中减去变量z,并将结果变异为tb的新变量。另外,假设我不知道我有多少个 y变量。我希望该解决方案很好地适合 tidyverse
/ dplyr
工作流程。
I want to subtract variable z from every variable with a name starting with "y", and mutate the results as new variables of tb. Also, suppose I don't know how many "y" variables I have. I want the solution to fit nicely within tidyverse
/ dplyr
workflow.
本质上,我不了解如何将多个变量突变为多个新变量。我不确定在这种情况下是否可以使用 mutate
?我已经尝试过 mutate_if
,但是我认为我使用的方式不正确(并且出现错误):
In essence, I don't understand how to mutate multiple variables into multiple new variables. I'm not sure if you can use mutate
in this instance? I've tried mutate_if
, but I don't think I'm using it right (and I get an error):
tb %>% mutate_if(starts_with("y"), funs(.-z))
#Error: No tidyselect variables were registered
提前谢谢!
推荐答案
由于要对列名进行操作,因此需要使用 mutate_at
而不是 mutate_if
它使用列中的值
Because you are operating on column names, you need to use mutate_at
rather than mutate_if
which uses the values within columns
tb %>% mutate_at(vars(starts_with("y")), funs(. - z))
#> # A tibble: 3 x 5
#> x y1 y2 y3 z
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 0 2 4 2
#> 2 2 -2 -1 0 3
#> 3 3 5 3 1 1
要创建新列,而不是覆盖现有列,我们可以将名称命名为 funs
To create new columns, instead of overwriting existing ones, we can give name to funs
# add suffix
tb %>% mutate_at(vars(starts_with("y")), funs(mod = . - z))
#> # A tibble: 3 x 8
#> x y1 y2 y3 z y1_mod y2_mod y3_mod
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 2 4 6 2 0 2 4
#> 2 2 1 2 3 3 -2 -1 0
#> 3 3 6 4 2 1 5 3 1
# remove suffix, add prefix
tb %>%
mutate_at(vars(starts_with("y")), funs(mod = . - z)) %>%
rename_at(vars(ends_with("_mod")), funs(paste("mod", gsub("_mod", "", .), sep = "_")))
#> # A tibble: 3 x 8
#> x y1 y2 y3 z mod_y1 mod_y2 mod_y3
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 2 4 6 2 0 2 4
#> 2 2 1 2 3 3 -2 -1 0
#> 3 3 6 4 2 1 5 3 1
编辑 :在 dplyr 0.8.0
或更高版本中,不建议使用 funs()
(& ),需要改用 list()
Edit: In dplyr 0.8.0
or higher versions, funs()
will be deprecated (source1 & source2), need to use list()
instead
tb %>% mutate_at(vars(starts_with("y")), list(~ . - z))
#> # A tibble: 3 x 5
#> x y1 y2 y3 z
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 0 2 4 2
#> 2 2 -2 -1 0 3
#> 3 3 5 3 1 1
tb %>% mutate_at(vars(starts_with("y")), list(mod = ~ . - z))
#> # A tibble: 3 x 8
#> x y1 y2 y3 z y1_mod y2_mod y3_mod
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 2 4 6 2 0 2 4
#> 2 2 1 2 3 3 -2 -1 0
#> 3 3 6 4 2 1 5 3 1
tb %>%
mutate_at(vars(starts_with("y")), list(mod = ~ . - z)) %>%
rename_at(vars(ends_with("_mod")), list(~ paste("mod", gsub("_mod", "", .), sep = "_")))
#> # A tibble: 3 x 8
#> x y1 y2 y3 z mod_y1 mod_y2 mod_y3
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 2 4 6 2 0 2 4
#> 2 2 1 2 3 3 -2 -1 0
#> 3 3 6 4 2 1 5 3 1
编辑2 :具有函数可进一步简化此任务
Edit 2: dplyr
1.0.0+ has across()
function which simplifies this task even further
# Control how the names are created with the `.names` argument which
# takes a [glue](http://glue.tidyverse.org/) spec:
tb %>%
mutate(
across(starts_with("y"), ~ .x - z, .names = "mod_{col}")
)
#> # A tibble: 3 x 8
#> x y1 y2 y3 z mod_y1 mod_y2 mod_y3
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 2 4 6 2 0 2 4
#> 2 2 1 2 3 3 -2 -1 0
#> 3 3 6 4 2 1 5 3 1
tb %>%
mutate(
across(num_range(prefix = "y", range = 1:3), ~ .x - z, .names = "mod_{col}")
)
#> # A tibble: 3 x 8
#> x y1 y2 y3 z mod_y1 mod_y2 mod_y3
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 2 4 6 2 0 2 4
#> 2 2 1 2 3 3 -2 -1 0
#> 3 3 6 4 2 1 5 3 1
### Multiple functions
tb %>%
mutate(
across(c(matches("x"), contains("z")), ~ max(.x, na.rm = TRUE), .names = "max_{col}"),
across(c(y1:y3), ~ .x - z, .names = "mod_{col}")
)
#> # A tibble: 3 x 10
#> x y1 y2 y3 z max_x max_z mod_y1 mod_y2 mod_y3
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 2 4 6 2 3 3 0 2 4
#> 2 2 1 2 3 3 3 3 -2 -1 0
#> 3 3 6 4 2 1 3 3 5 3 1
这篇关于突变多个变量以创建多个新变量的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持!