I am trying to replace all zeros in multiple columns with NA
using dplyr
.
However, since I have many variables, I do not want to call them all by one, but rather store them in an object that I can call afterwards.
This is a minimal example of what I did:
library(dplyr)
Data <- data.frame(var1 = c(1:10), var2 = rep(c(0, 4), 5), var3 = rep(c(2, 0, 3, 4, 5), 2), var4 = rep(c(7, 0), 5))
col <- Data[,c(2:4)]
Data <- Data %>%
mutate(across(col, na_if, 0))
However, if I do this, I get the following error message:
Error: Problem with 'mutate()' input '..1'.
x Must subset columns with a valid subscript vector.
x Subscript has the wrong type 'data.frame<
var2: double
var3: double
var4: double>'.
i It must be numeric or character.
i Input '..1' is '(function (.cols = everything(), .fns = NULL, ..., .names = NULL) ...'.
I have tried to change the format of col
to a tibble, but that did not help.
Could anyone tell me how to make this work?
Here, the col
should be names of the Data. As there is a function name with col
, we can name the object differently, wrap with all_of
and replace the 0 to NA
within across
library(dplyr)
col1 <- names(Data)[2:4]
Data <- Data %>%
mutate(across(all_of(col1) , na_if, 0))
-output
Data
# var1 var2 var3 var4
#1 1 NA 2 7
#2 2 4 NA NA
#3 3 NA 3 7
#4 4 4 4 NA
#5 5 NA 5 7
#6 6 4 2 NA
#7 7 NA NA 7
#8 8 4 3 NA
#9 9 NA 4 7
#10 10 4 5 NA
NOTE: Here the OP asked about looping based on either the index or the column names