如何转换df'；从for循环到purrr和dplyr的s变量赋值？

代码来自

但我想充分利用FP

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这对我来说很难，因为组合变量赋值和purrr是一个可能的建议，但它只是同一油漆的不同颜色

result <- mtcars
walk(1:length(trans),
  function(i) result <<- result %>% mutate_at(names(trans)[[i]],trans[[i]]))
result

---

result这里有一个
purrr
/
dplyr
选项，使用
imap\u-dfc

library(tidyverse)
imap_dfc(trans, ~mtcars %>% transmute_at(vars(.y), funs(.x))) %>%
    bind_cols(mtcars %>% select(-one_of(names(trans)))) %>%
    select(names(mtcars))
#    mpg cyl     disp  hp drat    wt  qsec vs     am gear carb
#1  21.0   6 2.621936 110 3.90 2.620 16.46  0 manual    4    4
#2  21.0   6 2.621936 110 3.90 2.875 17.02  0 manual    4    4
#3  22.8   4 1.769807  93 3.85 2.320 18.61  1 manual    4    1
#4  21.4   6 4.227872 110 3.08 3.215 19.44  1   auto    3    1
#5  18.7   8 5.899356 175 3.15 3.440 17.02  0   auto    3    2
#6  18.1   6 3.687098 105 2.76 3.460 20.22  1   auto    3    1
#7  14.3   8 5.899356 245 3.21 3.570 15.84  0   auto    3    4
#8  24.4   4 2.403988  62 3.69 3.190 20.00  1   auto    4    2
#9  22.8   4 2.307304  95 3.92 3.150 22.90  1   auto    4    2
#10 19.2   6 2.746478 123 3.92 3.440 18.30  1   auto    4    4
#11 17.8   6 2.746478 123 3.92 3.440 18.90  1   auto    4    4
#12 16.4   8 4.519562 180 3.07 4.070 17.40  0   auto    3    3
#13 17.3   8 4.519562 180 3.07 3.730 17.60  0   auto    3    3
#14 15.2   8 4.519562 180 3.07 3.780 18.00  0   auto    3    3
#15 10.4   8 7.734711 205 2.93 5.250 17.98  0   auto    3    4
#16 10.4   8 7.538066 215 3.00 5.424 17.82  0   auto    3    4
#17 14.7   8 7.210324 230 3.23 5.345 17.42  0   auto    3    4
#18 32.4   4 1.289665  66 4.08 2.200 19.47  1 manual    4    1
#19 30.4   4 1.240503  52 4.93 1.615 18.52  1 manual    4    2
#20 33.9   4 1.165123  65 4.22 1.835 19.90  1 manual    4    1
#21 21.5   4 1.968091  97 3.70 2.465 20.01  1   auto    3    1
#22 15.5   8 5.211098 150 2.76 3.520 16.87  0   auto    3    2
#23 15.2   8 4.981678 150 3.15 3.435 17.30  0   auto    3    2
#24 13.3   8 5.735485 245 3.73 3.840 15.41  0   auto    3    4
#25 19.2   8 6.554840 175 3.08 3.845 17.05  0   auto    3    2
#26 27.3   4 1.294581  66 4.08 1.935 18.90  1 manual    4    1
#27 26.0   4 1.971368  91 4.43 2.140 16.70  0 manual    5    2
#28 30.4   4 1.558413 113 3.77 1.513 16.90  1 manual    5    2
#29 15.8   8 5.751872 264 4.22 3.170 14.50  0 manual    5    4
#30 19.7   6 2.376130 175 3.62 2.770 15.50  0 manual    5    6
#31 15.0   8 4.932517 335 3.54 3.570 14.60  0 manual    5    8
#32 21.4   4 1.982839 109 4.11 2.780 18.60  1 manual    4    2

说明：
imap\u dfc（…）
column绑定两个修改过的列，然后将这两个列绑定到
mtcars
，而不包含修改过的两个列；最后一行重新排列列，使其对应于原始的
mtcars
列顺序

result <- mtcars
walk(1:length(trans),
  function(i) result <<- result %>% mutate_at(names(trans)[[i]],trans[[i]]))
result

result <- mtcars
pmap(list(names(trans),trans),
  function(n,f) result <<- result %>% mutate_at(n,f))
result

result <- mtcars
iwalk(trans,
  function(f,n) result <<- result %>% mutate_at(n,f))
result

library(tidyverse)
imap_dfc(trans, ~mtcars %>% transmute_at(vars(.y), funs(.x))) %>%
    bind_cols(mtcars %>% select(-one_of(names(trans)))) %>%
    select(names(mtcars))
#    mpg cyl     disp  hp drat    wt  qsec vs     am gear carb
#1  21.0   6 2.621936 110 3.90 2.620 16.46  0 manual    4    4
#2  21.0   6 2.621936 110 3.90 2.875 17.02  0 manual    4    4
#3  22.8   4 1.769807  93 3.85 2.320 18.61  1 manual    4    1
#4  21.4   6 4.227872 110 3.08 3.215 19.44  1   auto    3    1
#5  18.7   8 5.899356 175 3.15 3.440 17.02  0   auto    3    2
#6  18.1   6 3.687098 105 2.76 3.460 20.22  1   auto    3    1
#7  14.3   8 5.899356 245 3.21 3.570 15.84  0   auto    3    4
#8  24.4   4 2.403988  62 3.69 3.190 20.00  1   auto    4    2
#9  22.8   4 2.307304  95 3.92 3.150 22.90  1   auto    4    2
#10 19.2   6 2.746478 123 3.92 3.440 18.30  1   auto    4    4
#11 17.8   6 2.746478 123 3.92 3.440 18.90  1   auto    4    4
#12 16.4   8 4.519562 180 3.07 4.070 17.40  0   auto    3    3
#13 17.3   8 4.519562 180 3.07 3.730 17.60  0   auto    3    3
#14 15.2   8 4.519562 180 3.07 3.780 18.00  0   auto    3    3
#15 10.4   8 7.734711 205 2.93 5.250 17.98  0   auto    3    4
#16 10.4   8 7.538066 215 3.00 5.424 17.82  0   auto    3    4
#17 14.7   8 7.210324 230 3.23 5.345 17.42  0   auto    3    4
#18 32.4   4 1.289665  66 4.08 2.200 19.47  1 manual    4    1
#19 30.4   4 1.240503  52 4.93 1.615 18.52  1 manual    4    2
#20 33.9   4 1.165123  65 4.22 1.835 19.90  1 manual    4    1
#21 21.5   4 1.968091  97 3.70 2.465 20.01  1   auto    3    1
#22 15.5   8 5.211098 150 2.76 3.520 16.87  0   auto    3    2
#23 15.2   8 4.981678 150 3.15 3.435 17.30  0   auto    3    2
#24 13.3   8 5.735485 245 3.73 3.840 15.41  0   auto    3    4
#25 19.2   8 6.554840 175 3.08 3.845 17.05  0   auto    3    2
#26 27.3   4 1.294581  66 4.08 1.935 18.90  1 manual    4    1
#27 26.0   4 1.971368  91 4.43 2.140 16.70  0 manual    5    2
#28 30.4   4 1.558413 113 3.77 1.513 16.90  1 manual    5    2
#29 15.8   8 5.751872 264 4.22 3.170 14.50  0 manual    5    4
#30 19.7   6 2.376130 175 3.62 2.770 15.50  0 manual    5    6
#31 15.0   8 4.932517 335 3.54 3.570 14.60  0 manual    5    8
#32 21.4   4 1.982839 109 4.11 2.780 18.60  1 manual    4    2