Looping over columns with mutate and case_when - r

Question

I want to loop over many columns and under certain conditions, replace values. For example, if disease=0 and treatment=1, replace treatment cell with 99.
Data:
df <- data.frame(id=1:5,
disease1=c(1,1,0,0,0),
treatment1=c(1,0,1,0,0),
outcome1=c("survived", "died", "survived", NA,NA),
disease2=c(1,1,0,0,0),
treatment2=c(1,0,1,0,0),
outcome2=c("survived", "died", "survived", NA,NA))
> df
id disease1 treatment1 outcome1 disease2 treatment2 outcome2
1 1 1 1 survived 1 1 survived
2 2 1 0 died 1 0 died
3 3 0 1 survived 0 1 survived
4 4 0 0 <NA> 0 0 <NA>
5 5 0 0 <NA> 0 0 <NA>
For a single column, case_when works well:
df %>% mutate(treatment=case_when((disease1!=1&treatment1==1)~99, TRUE~treatment1))
For multiple columns, the following works in base R:
for(i in 1:2) {
df[,paste0("treatment",i)] <- ifelse(df[,paste0("disease",i)]!=1&df[,paste0("treatment",i)]==1,99, df[,paste0("treatment",i)])
}
I am looking for a way to do this all in tidyverse and I am having trouble finding the right recipe. Thank you in advance.

Answer 1

Maybe consider putting in long form with pivot_longer, then would be easier to mutate across multiple columns. This would be a "tidier" approach if all disease should be together in one column (and same for treatment in 1 column, and outcome in 1 column).
library(tidyverse)
df %>%
pivot_longer(cols = -id, names_to = c(".value", "number"), names_pattern = "(\\w+)(\\d+)") %>%
mutate(treatment = ifelse(disease == 0 & treatment == 1, 99, treatment))

Answer 2

An option with names_sep in pivot_longer with case_when
library(dplyr)
library(tidyr)
pivot_longer(df, cols = -id, names_to = c('.value', 'number'),
names_sep="(?<=[a-z])(?=[0-9])") %>%
mutate(treatment = replace(treatment, !disease & treatment == 1, 99))
# A tibble: 10 x 5
# id number disease treatment outcome
# <int> <chr> <dbl> <dbl> <chr>
# 1 1 1 1 1 survived
# 2 1 2 1 1 survived
# 3 2 1 1 0 died
# 4 2 2 1 0 died
# 5 3 1 0 99 survived
# 6 3 2 0 99 survived
# 7 4 1 0 0 <NA>
# 8 4 2 0 0 <NA>
# 9 5 1 0 0 <NA>
#10 5 2 0 0 <NA>