rdataframelistdplyr

Filter data.frame by list

I want to be able to filter a given data.frame by a dynamic list.

Lets say I have a list of filters like this

filter_list = list(filter_1 = list(vs = c(0), carb = c(1,4)),
                   filter_2 = list(cyl = c(4,6)))

Is there a way to filter a data.frame like mtcars in such a way, that it is equivalent too

library(dplyr)

mtcars %>%
  filter(vs %in% c(0) & carb %in% c(1,4) |
           cyl %in% c(4,6))

using the filter_list form above? So each element of the filter_list is evaluated as or and each item of the element of the filter list is evaluated as an and.

I tried using a loop, but it isn't working as intended:

df = mtcars
for(f in filter_list){
  vars = names(f)
  i = 1
  for(n in f){
    df = filter(df, !!vars[[i]] %in% n)
    i = i +1
  }
}

This just returns a empty data.frame. The or condition is also violated with the loop-approach.

Solution

  • We can use expand.grid() to create a data frame from each nested, ragged list of conditions, at which point this is essentially a join. To get a sense of this approach, here it is applied on the first filter:

    expand.grid(filter_list$filter_1)
#   vs carb
# 1  0    1
# 2  0    4

    As you've tagged , we can inner_join(), taking advantage of default joining on columns with the same names. As we want rows which meet filter1 or filter2, bind_rows() of the resulting list of matches. Ensure we don't twice include those rows that meet both filters with distinct().

    library(dplyr)
# Create rownames column as dplyr join strips them
mtcars <- tibble::rownames_to_column(mtcars, "car")
lapply(filter_list, \(filter) expand.grid(filter) |>
    inner_join(mtcars, y = _)) |>
    bind_rows() |>
    distinct(car, .keep_all = TRUE)

    Output:

                       car  mpg cyl  disp  hp drat    wt  qsec vs am gear carb
1            Mazda RX4 21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
2        Mazda RX4 Wag 21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
3           Duster 360 14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
4   Cadillac Fleetwood 10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
5  Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
6    Chrysler Imperial 14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
7           Camaro Z28 13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
8       Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
9           Datsun 710 22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
10      Hornet 4 Drive 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
11             Valiant 18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
12           Merc 240D 24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
13            Merc 230 22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
14            Merc 280 19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
15           Merc 280C 17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
16            Fiat 128 32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
17         Honda Civic 30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
18      Toyota Corolla 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
19       Toyota Corona 21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
20           Fiat X1-9 27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
21       Porsche 914-2 26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
22        Lotus Europa 30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
23        Ferrari Dino 19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
24          Volvo 142E 21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2