CODEWITHSUNDEEP
rdplyrtidyverse
G_T
G_T

Reputation: 1587

Using mutate_at() with negated select helpers e.g(not one_of())

I have data which looks like this:

library(dplyr)    
set.seed(123)

df <- data_frame(X1 = rep(LETTERS[1:4], 6),
                 X2 = rep(1:2, 12),
                 ref = sample(1:50, 24),
                 sampl1 = sample(1:50, 24),
                 var2 = sample(1:50, 24),
                 meas3 = sample(1:50, 24))

With dplyr's scooped commands I can edit and create multiple columns at once e.g:

df %>% mutate_if(is.numeric, funs(new = . - ref))

and if I want to do this to only a subset of columns I can use the select helpers like so:

df %>% mutate_at(vars(one_of(c("X2", "ref"))), funs(new = . - ref))

However in my case I know that my data will always contain the columns X1, X2 and ref but would like to subset the data in such a way to mutate only the columns which are NOT X1, X2 and ref. These other columns will be variable in number and name but always numeric. I thought I could do something like this:

df %>% mutate_at(vars(!one_of(c("X1", "X2", "ref"))), funs(new = . - ref))

or maybe

df %>% mutate_at(vars(one_of(!names %in% c("X1", "X2", "ref"))), funs(new = . - ref))

But neither work. How do you do negative dplyr select helpers?

Upvotes: 6

Views: 2346

Answers (1)

akrun
akrun

Reputation: 887048

The one_of requires - and not !

df %>%
   mutate_at(vars(-one_of(c("X1", "X2", "ref"))), funs(new = . - ref))
# A tibble: 24 x 9
#      X1    X2   ref sampl1  var2 meas3 sampl1_new var2_new meas3_new
#   <chr> <int> <int>  <int> <int> <int>      <int>    <int>     <int>
# 1     A     1    15     33    14    36         18       -1        21
# 2     B     2    39     35    43     1         -4        4       -38
# 3     C     1    20     27     3    23          7      -17         3
# 4     D     2    42     28    21    11        -14      -21       -31
# 5     A     1    44     14    37    18        -30       -7       -26
# 6     B     2     3      7     6    28          4        3        25
# 7     C     1    24     43    25    16         19        1        -8
# 8     D     2    49     39     9     5        -10      -40       -44
# 9     A     1    46     30    45    47        -16       -1         1
#10     B     2    19     50    31    45         31       12        26

Upvotes: 10

Related Questions