问题
My question is based on a previous topic posted here: Mutating multiple columns in a data frame
Suppose I have a tibble as follows:
id char_var_1 char_var_2 num_var_1 num_var_2 ... x_var_n
1 ... ... ... ... ...
2 ... ... ... ... ...
3 ... ... ... ... ...
where id
is the key and char_var_x
is a character variable and num_var_x
is a numerical variable. I have 346 columns in total and I want to write a function that scales all the numerical variables except the id
column. I'm looking for an elegant way to mutate these columns using pipes and dplyr functions.
Obviously the following works for all numeric variables:
pre_process_data <- function(dt)
{
# scale numeric variables
dt %>% mutate_if(is.numeric, scale)
}
But I'm looking for a way to exclude id
column from scaling and retain the original values and at the same time scale all other numerical variables. Is there an elegant way to do this?
回答1:
Try below, answer is similar to select_if post:
library(dplyr)
# Using @Psidom's example data: https://stackoverflow.com/a/48408027
df %>%
mutate_if(function(col) is.numeric(col) &
!all(col == .$id), scale)
# id a b
# 1 1 a -1
# 2 2 b 0
# 3 3 c 1
回答2:
Not a canonical way to do this, but with a little bit hack, you can do this with mutate_at
by making the integer indices of columns to mutate using which
with manually constructed column selecting conditions:
df = data.frame(id = 1:3, a = letters[1:3], b = 2:4)
df %>%
mutate_at(vars(which(sapply(., is.numeric) & names(.) != 'id')), scale)
# id a b
#1 1 a -1
#2 2 b 0
#3 3 c 1
回答3:
How about the "make the column your interested a character, then change it back approach?"
dt %>%
mutate(id = as.character(id)) %>%
mutate_if(is.numeric, scale) %>%
mutate(id = as.numeric(id))
来源:https://stackoverflow.com/questions/48407741/conditionally-mutate-columns-based-on-column-class