3

有没有办法改变除一个（在这种情况下是年龄）或两个之外的所有数字变量？

数据

data = data.frame(
    Year = c(1,2,5,7,2,6,2,6),
    days = c(5,3,6,3,7,2,5,7),
    age = c(1,3,5,23,2,4,5,2),
    names = c("A063", "A013", "A063", "A083", "A019", "A012", "A013", "A113"))

像这样：我想缩放除年龄以外的所有数字项

data = mutate(across(where(is.numeric & !age), scale))
4

2 回答 2

5

一种选择可能是：

data %>%
 mutate(across(c(where(is.numeric), -age), scale))

        Year       days age names
1 -1.2199771  0.1309842   1  A063
2 -0.7956372 -0.9168895   3  A013
3  0.4773823  0.6549210   5  A063
4  1.3260620 -0.9168895  23  A083
5 -0.7956372  1.1788579   2  A019
6  0.9017222 -1.4408263   4  A012
7 -0.7956372  0.1309842   5  A013
8  0.9017222  1.1788579   2  A113
于 2020-09-08T22:01:02.963 回答
3

我们可以setdiff在selectednames where列上使用numeric并应用scale

library(dplyr)
out <- data %>%
   mutate(across(setdiff(names(select(., where(is.numeric))), 'age'), scale))
out
#        Year       days age names
#1 -1.2199771  0.1309842   1  A063
#2 -0.7956372 -0.9168895   3  A013
#3  0.4773823  0.6549210   5  A063
#4  1.3260620 -0.9168895  23  A083
#5 -0.7956372  1.1788579   2  A019
#6  0.9017222 -1.4408263   4  A012
#7 -0.7956372  0.1309842   5  A013
#8  0.9017222  1.1788579   2  A113

或另一种选择imap

library(purrr)
data %>%
   imap_dfc(~ if(is.numeric(.x) & .y != 'age') scale(.x) else .x)

或使用base R

i1 <- sapply(data, is.numeric) & names(data) != "age"
data[i1] <- lapply(data[i1], scale)
于 2020-09-08T21:27:23.193 回答