use dplyr mutate to create new columns based on a vector of column names

Question

I would like to take the log of some columns, and create new columns that are all named log[original column name].

The code below works, but how can I pass the vecto

Answer 1

Ignoring the "use dplyr" part...

require(data.table)    

mtcars <- as.data.table(mtcars)

mtcars[, paste0('log', columnstolog) := lapply(.SD, log), .SDcols = columnstolog]

Answer 2

You could also make use of rowwise from dplyr package:

mtcars %>% 
   rowwise %>%
   mutate(logmpg = log(mpg),
       logcyl = log(cyl))


# A tibble: 32 x 13
     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb   logmpg   logcyl
   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
 1  21.0     6 160.0   110  3.90 2.620 16.46     0     1     4     4 3.044522 1.791759
 2  21.0     6 160.0   110  3.90 2.875 17.02     0     1     4     4 3.044522 1.791759
 3  22.8     4 108.0    93  3.85 2.320 18.61     1     1     4     1 3.126761 1.386294
 4  21.4     6 258.0   110  3.08 3.215 19.44     1     0     3     1 3.063391 1.791759
 5  18.7     8 360.0   175  3.15 3.440 17.02     0     0     3     2 2.928524 2.079442
 6  18.1     6 225.0   105  2.76 3.460 20.22     1     0     3     1 2.895912 1.791759
 7  14.3     8 360.0   245  3.21 3.570 15.84     0     0     3     4 2.660260 2.079442
 8  24.4     4 146.7    62  3.69 3.190 20.00     1     0     4     2 3.194583 1.386294
 9  22.8     4 140.8    95  3.92 3.150 22.90     1     0     4     2 3.126761 1.386294
10  19.2     6 167.6   123  3.92 3.440 18.30     1     0     4     4 2.954910 1.791759
# ... with 22 more rows

Answer 3

Use mutate_at, if you can bear with _log being appended to the original column names:

mtcars %>% mutate_at(columnstolog, funs(log = log(.)))

#    mpg cyl  disp  hp drat    wt  qsec vs am gear carb  mpg_log  cyl_log disp_log   hp_log
#1  21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4 3.044522 1.791759 5.075174 4.700480
#2  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4 3.044522 1.791759 5.075174 4.700480
#3  22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1 3.126761 1.386294 4.682131 4.532599
#4  21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1 3.063391 1.791759 5.552960 4.700480
#5  18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2 2.928524 2.079442 5.886104 5.164786
# ...