Generating new columns in an R dataframe based on applying a function across multiple columns

Question

What I'd like to do is apply a function to multiple columns in a dataframe, recording the output as a new column. To make this clearer, I'd like to take a dataframe of the form:

first_name  last_name   age
   Alice       Smith     45
    Bob       Richards   20

to:

first_name  last_name   age  first_name_lower  last_name_lower
   Alice       Smith     45      alice            smith
    Bob       Richards   20       bob            richards

I can do this column-wise with something like:

df$first_name_lower <- apply(df[,c('first_name')], 1, function(x) str_to_lower(x))
df$last_name_lower <- apply(df[,c('last_name')], 1, function(x) str_to_lower(x))

but of course for multiple columns this isn't a particularly elegant solution.

Thanks!

Answer 1

This could work transmute_if takes a predicate and performs and action on all columns satisfying the predicate and throws away all the rest - in this case we use is.character as predicate . Since we want to keep the orignial data we combine both datasets with cbind.
To change the names of the new columns we use select_all to paste "_lower" to end of the column names

dta <- read.table(header = TRUE,sep = ",",stringsAsFactors = FALSE,
                  text = "first_name,last_name,age
Alice,Smith,45
                  Bob,Richards,20")
library(tidyverse)
cbind(dta,
      dta %>%
        transmute_if(is.character,tolower) %>% 
        select_all(funs(paste0(.,"_lower")))))

Hope it helps!

Answer 2

Using tidyverse solution:

library(tidyverse)
mydf %>% 
    mutate(first_name_lower=first_name,
           last_name_lower=last_name) %>% 
    mutate_at(vars=first_name_lower,last_name_lower), ~ str_to_lower(.)))

Whether you don't want to preserve original variables:

mydf %>% 
        mutate_at(vars(first_name_lower, last_name_lower), ~ str_to_lower(.))