假设我有一个 tibble
我需要获取多个变量并将它们变异为新的多个新变量。
例如,这是一个简单的 tibble:
tb <- tribble(
~x, ~y1, ~y2, ~y3, ~z,
1,2,4,6,2,
2,1,2,3,3,
3,6,4,2,1
)
我想从名称以“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“y”变量。我希望解决方案能很好地适应
tidyverse
/dplyr
工作流程。本质上,我不明白如何将多个变量变异为多个新变量。我不确定您是否可以使用
mutate
在这种情况下?我试过 mutate_if
,但我认为我没有正确使用它(并且出现错误):tb %>% mutate_if(starts_with("y"), funs(.-z))
#Error: No tidyselect variables were registered
提前致谢!
最佳答案
因为是对列名进行操作,所以需要使用mutate_at
而不是 mutate_if
它使用列中的值
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,我们可以为 funs
命名。# 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,funs()
将被弃用( source1 & source2 ),需要使用 list()
反而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 :
dplyr
1.0.0+有 across()
进一步简化此任务的功能Basic usage
across()
has two primary arguments:
- The first argument,
.cols
, selects the columns you want to operate on. It uses tidy selection (likeselect()
) so you can pick variables by position, name, and type.
- The second argument,
.fns
, is a function or list of functions to apply to each column. This can also be a purrr style formula (or list of formulas) like~ .x / 2
. (This argument is optional, and you can omit it if you just want to get the underlying data; you'll see that technique used invignette("rowwise")
.)
# 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
创建于 2018-10-29 由 reprex package (v0.2.1)
关于r - 变异多个变量以创建多个新变量,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/48898121/