choose filter variable based on character vector and index number in a custom dplyr function

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I have a function which takes a character vector with variable names and an index which determines which of the variables is chosen as input for a dplyr::filter(). I have a very clunky way of extracting the variable I need for my filter call (see my approach below). However, I think there is a more concise way, and I am just not seeing it at the moment.

Any help appreciated.

library(dplyr)

myfilter <- function(data, filter_vec, filter_idx) {
  
  # this part here is too verbose  ----
  filter_var <- data %>%
    select(all_of(filter_vec)) %>%
    select(all_of(filter_idx)) %>%
    colnames
  # ------
  
  data %>%
    filter(!! sym(filter_var) == 1)
  
}
# this function call uses the forth variable `am` as filter variable
mtcars %>%
  myfilter(c("mpg", "cyl", "gear", "am"), 4)

#>                 mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#> Mazda RX4      21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
#> Mazda RX4 Wag  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
#> Datsun 710     22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
#> Fiat 128       32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
#> Honda Civic    30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
#> Toyota Corolla 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
#> Fiat X1-9      27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
#> Porsche 914-2  26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
#> Lotus Europa   30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
#> Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
#> Ferrari Dino   19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
#> Maserati Bora  15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
#> Volvo 142E     21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

Created on 2020-10-30 by the reprex package (v0.3.0)

dplyr r
1 Answers

If it is an numeric index with 'filter_idx', use [ to subset the vector. Assuming there will be only one column to apply the filter, we can directly use that in filter by converting to sym and evaluate (!!)

myfilter <- function(data, filter_vec, filter_idx) {
  data %>%
    filter(!! sym(filter_vec[filter_idx]) == 1)
  }

-testing

mtcars %>%
   myfilter(c("mpg", "cyl", "gear", "am"), 4)
#                mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#Mazda RX4      21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
#Mazda RX4 Wag  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
#Datsun 710     22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
#Fiat 128       32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
#Honda Civic    30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
#Toyota Corolla 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
#Fiat X1-9      27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
#Porsche 914-2  26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
#Lotus Europa   30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
#Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
#Ferrari Dino   19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
#Maserati Bora  15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
#Volvo 142E     21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

Another option if there are more columns would be (depending on whether it is any or all), an option is wrap with across (dplyr version >=1.0)

myfilter <- function(data, filter_vec, filter_idx) {
  data %>%
    filter(across(all_of(filter_vec[filter_idx]), ~  .== 1))
  } 

mtcars %>%
   myfilter(c("mpg", "cyl", "gear", "am"), 4)
#                mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#Mazda RX4      21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
#Mazda RX4 Wag  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
#Datsun 710     22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
#Fiat 128       32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
#Honda Civic    30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
#Toyota Corolla 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
#Fiat X1-9      27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
#Porsche 914-2  26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
#Lotus Europa   30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
#Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
#Ferrari Dino   19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
#Maserati Bora  15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
#Volvo 142E     21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

-testing for multiple columns (case where all the columns subset should have 1 for the row)

mtcars %>%
    myfilter(c("mpg", "carb", "gear", "am"), c(2, 4))
#                mpg cyl  disp hp drat    wt  qsec vs am gear carb
#Datsun 710     22.8   4 108.0 93 3.85 2.320 18.61  1  1    4    1
#Fiat 128       32.4   4  78.7 66 4.08 2.200 19.47  1  1    4    1
#Toyota Corolla 33.9   4  71.1 65 4.22 1.835 19.90  1  1    4    1
#Fiat X1-9      27.3   4  79.0 66 4.08 1.935 18.90  1  1    4    1
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