grep one pattern over multiple columns

Question

I'm trying to figure out a way for me to use grepl() of only one partial pattern over multiple columns with mutate(). I want to have a new column that will be TRUE or FALSE if ANY of a set of columns contains a certain string.

df <- structure(list(ID = c("A1.1234567_10", "A1.1234567_20"), 
                 var1 = c("NORMAL", "NORMAL"), 
                 var2 = c("NORMAL", "NORMAL"), 
                 var3 = c("NORMAL", "NORMAL"), 
                 var4 = c("NORMAL", "NORMAL"), 
                 var5 = c("NORMAL", "NORMAL"), 
                 var6 = c("NORMAL", "NORMAL"), 
                 var7 = c("NORMAL", "ABNORMAL"), 
                 var8 = c("NORMAL", "NORMAL")), 
            .Names = c("ID", "var1", "var2", "var3", "var4", "var5", "var6", "var7", "var8"), 
            class = "data.frame", row.names = c(NA, -2L))

            ID   var1   var2   var3   var4   var5   var6     var7   var8
1 A1.1234567_10 NORMAL NORMAL NORMAL NORMAL NORMAL NORMAL   NORMAL NORMAL
2 A1.1234567_20 NORMAL NORMAL NORMAL NORMAL NORMAL NORMAL ABNORMAL NORMAL

I tried

df$abnormal %>% mutate( abnormal = ifelse(grepl("abnormal",df[,119:131]) , TRUE, FALSE)))

and about 100 other things. I want the final format to be

             ID   var1   var2   var3   var4   var5   var6     var7   var8    abnormal
1 A1.1234567_10 NORMAL NORMAL NORMAL NORMAL NORMAL NORMAL   NORMAL NORMAL FALSE
2 A1.1234567_20 NORMAL NORMAL NORMAL NORMAL NORMAL NORMAL ABNORMAL NORMAL TRUE

Whenever I try I get false every time

Answer 1

I'd probably do this:

temp = sapply(your_data[columns_you_want_to_check],
              function(x) grepl("suspected", x, ingore.case = TRUE))
your_data$abnormal = rowSums(temp) > 0

I just used your_data since your question switches between df and test.file.

If you really want to use mutate, you could do

df %>%
mutate(abnormal = rowSums(
  sapply(select(., starts_with("var")),
         function(x) grepl("suspected", x, ingore.case = TRUE)
  )) > 0
)

If you need more efficiency, you can use fixed = TRUE instead of ignore.case = TRUE if you can count on the case being consistent. (Maybe convert everything to_lower() first.)

Leave off the > 0 to get the count for each row.