I have a continuous variable within a large dataset (df) that contains both NA’s and entries that should be recoded as NA’s. The variable in question (optime) is an integer. I would like to keep the existing NA’s and recode non-sensical values as NA’s. optime should have values that are typically >120.
df$optime %>% summary()
Gives me:
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
4.0 280.0 358.0 371.9 449.0 1324.0 6
Clearly, the lower values are erroneous. So I would like to replace them:
df <- df %>%
mutate(optime = na_if(optime, <5, NA))
This code has worked to recode cells with 0 values, but I gather that it does not accept conditional operators such as ‘<‘.
I have also tried with if_else:
mutate(optime = if_else(optime <5, NA, optime))
I am certain that this is pretty simple for most users, but I am not finding an answer. Should I use case_when? Thank you.
>Solution :
optime = ifelse(optime <5, NA, optime)