Exclude column in `dplyr` `mutate_at` while using data in this column

Question

I want to rescale all variables (but year and gender) in a df by one specific year, grouped by gender:

Answer 1

Use position in mutate_at

library(dplyr)

df %>%
  group_by(gender) %>%
  mutate_at(-c(1, 2), ~ifelse(year == 3, 0, . - .[year == 3]))

#  gender  year var_a var_b
#   <fct>  <int> <dbl> <dbl>
# 1 m          1    -2    -2
# 2 m          2    -1    -1
# 3 m          3     0     0
# 4 m          4     1     1
# 5 m          5     2     2
# 6 f          1    -2    -2
# 7 f          2    -1    -1
# 8 f          3     0     0
# 9 f          4     1     1
#10 f          5     2     2

In case, if you do not know the position of columns beforehand you can first find it

cols <- which(names(df) %in% c("gender", "year"))

df %>%
  group_by(gender) %>%
  mutate_at(-cols, ~ifelse(year == 3, 0, . - .[year == 3]))

---

Or select columns which starts_with

df %>%
  group_by(gender) %>%
  mutate_at(vars(starts_with("var")), ~ifelse(year == 3, 0, . - .[year == 3]))

Answer 2

If you add a ~ before the function you should get the wanted output.

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
set.seed(1)
df <- data.frame(gender = c(rep("m", 5),
                            rep("f", 5)), 
                 year = rep(1:5, 2), var_a = 1:10, var_b = 0:9)
df
#>    gender year var_a var_b
#> 1       m    1     1     0
#> 2       m    2     2     1
#> 3       m    3     3     2
#> 4       m    4     4     3
#> 5       m    5     5     4
#> 6       f    1     6     5
#> 7       f    2     7     6
#> 8       f    3     8     7
#> 9       f    4     9     8
#> 10      f    5    10     9

df %>%
  group_by(gender) %>% 
  mutate_at(vars(-gender, -year),
            ~ifelse(year == 3, 0, . - .[year == 3]))
#> # A tibble: 10 x 4
#> # Groups:   gender [2]
#>    gender  year var_a var_b
#>    <fct>  <int> <dbl> <dbl>
#>  1 m          1    -2    -2
#>  2 m          2    -1    -1
#>  3 m          3     0     0
#>  4 m          4     1     1
#>  5 m          5     2     2
#>  6 f          1    -2    -2
#>  7 f          2    -1    -1
#>  8 f          3     0     0
#>  9 f          4     1     1
#> 10 f          5     2     2

^{Created on 2019-04-29 by the reprex package (v0.2.1)}

EDIT: In older versions of dplyr you would use funs(), but it is soft deprecated as of dplyr 0.8.0

df %>%
  group_by(gender) %>% 
  mutate_at(vars(-gender, -year),
            funs(ifelse(year == 3, 0, . - .[year == 3])))