I am trying to apply some new conditions to the existing data frame (as below). I could not use ifelse because the logic is like this:

1. If count<=3: keep as it is
2. If count>3 :
   • If Code != "OFF15", c = "OK10" else c = "OFF15"
3. If count > 3 and code is NA: Code= "OK10"

I could not generate the third condition when using ifelse

Code = c("OFF15","OFF5",NA,"OFF5",NA)
count= c(3,4,4,2,1)
x= data.frame(Code,count)

I could not use ifelse as there is nested list for that. My expected will be:

Code    Count
OFF15     3
OK10      2
OK10      3
OFF5      2
NA        1

CodePudding user response：

You could use dplyr:

library(dplyr)

df %>% 
  mutate(Code = case_when(count < 3 ~ Code,
                          count > 3 & Code != "OFF15" ~ "OK10",
                          TRUE ~ "OFF15"))

This returns

   Code count
1 OFF15     3
2  OK10     4
3 OFF15     3
4  OFF5     2
5  <NA>     1

Note: I named the data.frame df instead of x.

CodePudding user response：

We can use nested ifelses (it is still borderline OK with just one level of nesting, otherwise case_when is almost imperative).

library(dplyr)

df %>% mutate(Code = ifelse(count<=3, Code, ifelse(!Code %in% "OFF15", "OK10", "OFF15"))