Create new variables with mutate_at while keeping the original ones

Consider this simple example:

library(dplyr)

dataframe <- data_frame(helloo = c(1,2,3,4,5,6),
                        ooooHH = c(1,1,1,2,2,2),
                        ahaaa = c(200,400,120,300,100,100))

# A tibble: 6 x 3
  helloo ooooHH ahaaa
   <dbl>  <dbl> <dbl>
1      1      1   200
2      2      1   400
3      3      1   120
4      4      2   300
5      5      2   100
6      6      2   100

Here I want to apply the function ntile to all the columns that contains oo, but I would like these new columns to be called cat + the corresponding column.

I know I can do this

dataframe %>% mutate_at(vars(contains('oo')), .funs = funs(ntile(., 2)))
# A tibble: 6 x 3
  helloo ooooHH ahaaa
   <int>  <int> <dbl>
1      1      1   200
2      1      1   400
3      1      1   120
4      2      2   300
5      2      2   100
6      2      2   100

But what I need is this

# A tibble: 8 x 5
  helloo   ooooHH   ahaaa cat_helloo cat_ooooHH
     <dbl>    <dbl> <dbl>    <int>    <int>
1        1        1   200        1        1
2        2        1   400        1        1
3        3        1   120        1        1
4        4        2   300        2        2
5        5        2   100        2        2
6        5        2   100        2        2
7        6        2   100        2        2
8        6        2   100        2        2

Is there a solution that does NOT require to store the intermediate data, and merge back to the original dataframe?

Update 2020-06 for dplyr 1.0.0

Starting in dplyr 1.0.0, the across() function supersedes the "scoped variants" of functions such as mutate_at(). The code should look pretty familiar within across(), which is nested inside mutate().

Adding a name to the function(s) you give in the list adds the function name as a suffix.

dataframe %>%
     mutate( across(contains('oo'), 
                    .fns = list(cat = ~ntile(., 2))) )

# A tibble: 6 x 5
  helloo ooooHH ahaaa helloo_cat ooooHH_cat
   <dbl>  <dbl> <dbl>      <int>      <int>
1      1      1   200          1          1
2      2      1   400          1          1
3      3      1   120          1          1
4      4      2   300          2          2
5      5      2   100          2          2
6      6      2   100          2          2

Changing the new columns names is a little easier in 1.0.0 with the .names argument in across(). Here is an example of adding the function name as a prefix instead of a suffix. This uses glue syntax.

dataframe %>%
     mutate( across(contains('oo'), 
                    .fns = list(cat = ~ntile(., 2)),
                    .names = "{fn}_{col}" ) )

# A tibble: 6 x 5
  helloo ooooHH ahaaa cat_helloo cat_ooooHH
   <dbl>  <dbl> <dbl>      <int>      <int>
1      1      1   200          1          1
2      2      1   400          1          1
3      3      1   120          1          1
4      4      2   300          2          2
5      5      2   100          2          2
6      6      2   100          2          2

Original answer with mutate_at()

Edited to reflect changes in dplyr. As of dplyr 0.8.0, funs() is deprecated and list() with ~ should be used instead.

You can give names to the functions to the list you pass to .funs to make new variables with the names as suffixes attached.

dataframe %>% mutate_at(vars(contains('oo')), .funs = list(cat = ~ntile(., 2)))

# A tibble: 6 x 5
  helloo ooooHH ahaaa helloo_cat ooooHH_cat
   <dbl>  <dbl> <dbl>      <int>      <int>
1      1      1   200          1          1
2      2      1   400          1          1
3      3      1   120          1          1
4      4      2   300          2          2
5      5      2   100          2          2
6      6      2   100          2          2

If you want it as a prefix instead, you could then use rename_at to change the names.

dataframe %>% 
     mutate_at(vars(contains('oo')), .funs = list(cat = ~ntile(., 2))) %>%
     rename_at( vars( contains( "_cat") ), list( ~paste("cat", gsub("_cat", "", .), sep = "_") ) )

# A tibble: 6 x 5
  helloo ooooHH ahaaa cat_helloo cat_ooooHH
   <dbl>  <dbl> <dbl>      <int>      <int>
1      1      1   200          1          1
2      2      1   400          1          1
3      3      1   120          1          1
4      4      2   300          2          2
5      5      2   100          2          2
6      6      2   100          2          2

Previous code with funs() from earlier versions of dplyr:

dataframe %>% 
     mutate_at(vars(contains('oo')), .funs = funs(cat = ntile(., 2))) %>%
     rename_at( vars( contains( "_cat") ), funs( paste("cat", gsub("_cat", "", .), sep = "_") ) )