mutate_if and mutate_at functionality in one line

Question

Is it possible to (1) use select helpers and (2) check type of columns to apply a function?

For example this will not work - I would like to convert to factor all integer columns the names of which do not contain "VAL":

dane_usa_nr%>% 
  mutate_at(vars(!contains("VAL")) & is.integer , as.factor)

Is it possible with some other syntax in dplyr?

Answer 1

Try this. It'll mutate all columns not containing "VAL". It'll then check if it is integer. If it is, it'll use as.factor() . If it isn't, it'll simply return the column.

dane_usa_nr%>% 
  mutate_at(vars(!contains("VAL")), function(x) if (is.integer(x)) as.factor(x) else x)

Answer 2

Not exactly a dplyr way to do this but you can create a logical vector which satisfies the condition needed.

inds <- (!grepl("VAL", names(dane_usa_nr))) & (sapply(dane_usa_nr, is.integer))

and then use this vector in mutate_if

library(dplyr)
dane_usa_nr %>%  mutate_if(inds, as.factor)

Or lapply

dane_usa_nr[inds] <- lapply(dane_usa_nr[inds], as.factor)

---

We can also get the indices and use it in mutate_at

inds <- intersect(grep("VAL", names(dane_usa_nr), invert = TRUE), 
                  which(sapply(dane_usa_nr, is.integer)))

dane_usa_nr %>% mutate_at(inds, as.factor)

The base R implementation remains the same in this case.