Creating a new column based on values obtained from different column, using mutate() and case_when function in R

I am a student relatively new to R and have learnt a lot from browsing here, I have been stuck on something recently which after hours of trying still haven’t been able to figure out what to do. Let’s propose the following data set:

ID Y1 Y2 Y3 Y4

1 0 0 1 1

2 0 0 0 0

3 NA NA NA NA

I want to create a new column where it is filled based upon the following the conditions:

1. If the row contains 1, return 1 regardless of NA or 0
2. If it contains a mix of 0 and NA but not 1, return 0
3. If it only contains NA, return NA

So using the example above I wanted to get the following:

ID Y1 Y2 Y3 Y4 Outcome

1 0 0 1 1 1

2 0 0 0 0 0

3 NA NA NA NA NA

However, the code I tried:

Data2 <- Data %>% mutate(Outcome = case_when( 
                                Data$Y1 == "na" &
                                Data$Y2 == "na" &
                                Data$Y3 == "na" &
                                Data$Y4 == "na" ~ "na"))  %>%                                
          mutate(Outcome = case_when(Data$Y1 == 1 ~ "1", 
                                 Data$Y2 == 1 ~ "1", 
                                 Data$Y3 == 1 ~ "1",
                                 Data$Y4 == 1 ~ "1",
                                 TRUE ~ "No"))

will return with:

ID Y1 Y2 Y3 Y4 Outcome

1 0 0 1 1 1

2 0 0 0 0 0

3 NA NA NA NA 0

which seems to ignore condition 3 where if it only contains na, return na.

Any pointers as to what I done wrong would be greatly appreciated.

Please forgive the formatting, I’m not sure how I could make it prettier as this is the first time I asked a question here.

Many thanks in advance!

[Edit] Thanks to Shah I noticed that there is potential for confusion, for that I apologise. I need give some clarification that this is just a segment of the data set to get the point across. I’m dealing with a big dataset which contains more columns, some of which also have numeric values.

>Solution :

You can try this using dplyr rowwise function which treat each row separately

library(dplyr)

df |> rowwise() |> 
mutate(Outcome = case_when(any(c_across(Y1:Y4) == 1) ~ "1" ,
 all(is.na(c_across(Y1:Y4))) ~ NA_character_ , TRUE ~ "0"))

• output

# A tibble: 3 × 6
# Rowwise: 
     ID    Y1    Y2    Y3    Y4 Outcome
  <int> <int> <int> <int> <int> <chr>  
1     1     0     0     1     1 1      
2     2     0     0     0     0 0      
3     3    NA    NA    NA    NA NA