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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.
261
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 dplyr, 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
answered Apr 18 '26 07:04
You might create an expression and then eval.
fx <- \(data, x) {
expr <- mapply(\(x, y) sprintf('%s %%in%% c(%s)', x, toString(y)), names(x), x) |>
paste(collapse=' & ') |> str2lang()
subset(data, eval(expr))
}
Usage on single list
> fx(mtcars, filter_list[[1]])
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
Duster 360 14.3 8 360 245 3.21 3.570 15.84 0 0 3 4
Cadillac Fleetwood 10.4 8 472 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4
Camaro Z28 13.3 8 350 245 3.73 3.840 15.41 0 0 3 4
Ford Pantera L 15.8 8 351 264 4.22 3.170 14.50 0 1 5 4
And on nested list
> lapply(filter_list, fx, data=mtcars)
$filter_1
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
Duster 360 14.3 8 360 245 3.21 3.570 15.84 0 0 3 4
Cadillac Fleetwood 10.4 8 472 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4
Camaro Z28 13.3 8 350 245 3.73 3.840 15.41 0 0 3 4
Ford Pantera L 15.8 8 351 264 4.22 3.170 14.50 0 1 5 4
$filter_2
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
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
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
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 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
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
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