I have simplified my df to:
A <- c("a", "b", "c", "d", "e", "f", "g", "NA", "h", "I")
B <- c(NA, 2, 3, 4, NA, NA, 5, 6, 8, NA)
C <- c(NA, 9, 8, 4, 5, 7, 5, 6, NA, NA)
D <- c(NA, 1, NA, 3, NA, 5, NA, NA, 8, NA)
E <- c(1,2,3,4,5,6,7,8,9,10)
df <- data.frame(A, B, C, D, E)
I would like to create a general code to change the numerical value of columns B and C based on the NA value of column D. The resulting df2 would be:
A <- c("a", "b", "c", "d", "e", "f", "g", "NA", "h", "I")
B <- c(NA, 2, NA, 4, NA, NA, NA, NA, 8, NA)
C <- c(NA, 9, NA, 4, NA, 7, NA, NA, NA, NA)
D <- c(NA, 1, NA, 3, NA, 5, NA, NA, 8, NA)
E <- c(1,2,3,4,5,6,7,8,9,10)
df2 <- data.frame(A, B, C, D, E)
For my code that isn't working I have so far tried the below which give me the error of "unused argument (as.numeric(B))":
df2 <- df %>% na_if(is.na(D), as.numeric(B)) %>%
na_if(is.na(D), as.numeric(C))
Any help with be greatly appreciate. I cannot install library(naniar) so please no solution that use replace_with_na_at.
Thank you!
CodePudding user response:
With dplyr
, we can apply a simple ifelse
statement to both B
and C
using across
and replace with NA
when they meet the condition (i.e., D
is NA
).
library(dplyr)
output <- df %>%
mutate(across(B:C, ~ ifelse(is.na(D), NA, .x)))
Output
A B C D E
1 a NA NA NA 1
2 b 2 9 1 2
3 c NA NA NA 3
4 d 4 4 3 4
5 e NA NA NA 5
6 f NA 7 5 6
7 g NA NA NA 7
8 NA NA NA NA 8
9 h 8 NA 8 9
10 I NA NA NA 10
Test
identical(output, df2)
# [1] TRUE
CodePudding user response:
Base R
A base R solution with Map
and is.na<-
.
A <- c("a", "b", "c", "d", "e", "f", "g", "NA", "h", "I")
B <- c(NA, 2, 3, 4, NA, NA, 5, 6, 8, NA)
C <- c(NA, 9, 8, 4, 5, 7, 5, 6, NA, NA)
D <- c(NA, 1, NA, 3, NA, 5, NA, NA, 8, NA)
E <- c(1,2,3,4,5,6,7,8,9,10)
df <- data.frame(A, B, C, D, E)
df[c("B", "C")] <- Map(\(x, y) {
is.na(x) <- is.na(y)
x
}, df[c("B", "C")], df["D"])
df
#> A B C D E
#> 1 a NA NA NA 1
#> 2 b 2 9 1 2
#> 3 c NA NA NA 3
#> 4 d 4 4 3 4
#> 5 e NA NA NA 5
#> 6 f NA 7 5 6
#> 7 g NA NA NA 7
#> 8 NA NA NA NA 8
#> 9 h 8 NA 8 9
#> 10 I NA NA NA 10
Created on 2022-03-01 by the reprex package (v2.0.1)
dplyr
And a solution with dplyr
, but the same is.na<-
.
library(dplyr)
df %>%
mutate(across(B:C, \(x) {is.na(x) <- is.na(D); x}))
#> A B C D E
#> 1 a NA NA NA 1
#> 2 b 2 9 1 2
#> 3 c NA NA NA 3
#> 4 d 4 4 3 4
#> 5 e NA NA NA 5
#> 6 f NA 7 5 6
#> 7 g NA NA NA 7
#> 8 NA NA NA NA 8
#> 9 h 8 NA 8 9
#> 10 I NA NA NA 10
Created on 2022-03-01 by the reprex package (v2.0.1)