I'm trying to convert variables to NA(missing) with conditions
This is my data as example. I have 5 patients ID, If there is '1' in missing variable, outcome1 and outcome2 will be converted to NA(missing).
ID<-c("a","b","c","d","e")
cond1<-as.factor(sample(x=1:7,size=5,replace=TRUE))
cond2<-as.factor(sample(x=1:7,size=5,replace=TRUE))
cond3<-as.factor(sample(x=1:7,size=5,replace=TRUE))
missing<-as.factor(sample(x=0:1,size=5,replace=TRUE))
outcome1<-sample(x=1:10, size=5,replace=TRUE)
outcome2<-sample(x=1:10, size=5,replace=TRUE)
df<-data.frame(ID,cond1,cond2,cond3,missing,outcome1,outcome2)
df
ID cond1 cond2 cond3 missing outcome1 outcome2
1 a 7 1 7 0 6 5
2 b 5 3 7 0 3 1
3 c 4 5 1 1 3 9
4 d 2 2 3 0 7 3
5 e 1 7 4 1 2 7
I've found the replace_with_na_at function, in naniar package. however, it didn't worked.
df%>%
replace_with_na_at(.vars=c("outcome1","outcome2"), condition = ~ hn10_14med$missing==1)
Error: Predicate functions must return a single `TRUE` or `FALSE`, not a logical vector of length 0
Call `rlang::last_error()` to see a backtrace
How to convert variable to NA with conditions? If there is a better way, though not using replace_with_na_at function, you would let me know.
If you want to replace the values in place, just use ifelse:
df[df$missing==1,c("cond1", "cond2")] <- NA