CODEWITHSUNDEEP
rdplyr
symbolrush
symbolrush

Reputation: 7457

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

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

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

I can generate what I expect using:

df %>% group_by(gender) %>% mutate(var_a = ifelse(year == 3, 0, var_a - var_a[year == 3])) %>%
  mutate(var_b = ifelse(year == 3, 0, var_b - var_b[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

However, this is not an option since I have too many columns.

So I tried (with no success):

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

Error in ifelse(year == 3, 0, var_a - var_a[year == 3]) : object 'year' not found

How can I exclude column names in mutate_at (or an alternative) using vars(-col_name) while still reading the data in those columns?

This is related to this one

Upvotes: 3

Views: 1756

Answers (2)

FilipW
FilipW

Reputation: 1525

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])))

Upvotes: 4

Ronak Shah
Ronak Shah

Reputation: 388907

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]))

Upvotes: 5

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