using `pmap` with `dplyr::filter`

Question

Let's say I want to apply the same function (dplyr::filter) on the same dataset with different filtering conditions.

I can use pmap here to give a list of arguments in .l and the function I want to run in .f. The arguments to filter are .data (which I have specified), and it works with one condition, as expected:

purrr::pmap(.l = list(.data = list(mtcars)), 
            .f = dplyr::filter,
            wt > 3)
#> [[1]]
#>                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#> Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
#> Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
#> Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
#> Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
#> 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
#> Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
#> Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
#> Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
#> Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
#> Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
#> Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
#> Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
#> AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
#> Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
#> Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
#> Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
#> Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8

But I can't figure out how to pass multiple filtering conditions:

purrr::pmap(.l = list(.data = list(mtcars)), 
            .f = dplyr::filter,
            list(alist(wt > 3), alist(wt > 5)))
#> Error: Problem with `filter()` input `..1`.
#> x Input `..1` must be of size 32 or 1, not size 2.
#> ℹ Input `..1` is `alist(wt > 3, wt > 5)`.

How can I do this? I am expecting a list of two dataframes: one filtered with condition wt > 3, and the other with wt > 5.

pmap is for when you want to (quoting the documentation) "iterate over multiple arguments simultaneously". It's interpreting the data frame a list of arguments, and applying filter to each column indivudually. You're holding the .data argument constant and only iterating over 1 argument, the filtering condition, so I think you want map not pmap. — Gregor Thomas
However I'm still not sure how to pair that with the NSE of the arguments... I'm sure someone will come along and demonstrate. — Gregor Thomas
@GregorThomas Thanks, I have edited my question to address your concern. — Indrajeet Patil
If you are expecting 2 data frames, then using pmap with list(mtcars) is not the correct approach. As suggested by @GregorThomas, map is what you want to use instead: purrr::map(.x = c(3, 5), .f = function(x){ mtcars %>% dplyr::filter(wt > x) }) — SavedByJESUS
@SavedByJESUS Thanks! Can you please post your solution, so that I can accept it and close this? — Indrajeet Patil

SavedByJESUS SavedByJESUS · Accepted Answer · 2021-01-30T08:17:09

If you are expecting 2 data frames, then using pmap with list(mtcars) is not the correct approach. As suggested by @GregorThomas, map is what you want to use instead. The element of your code that changes is the values that you would like to use for filtering, so this is what you should "map" instead:

purrr::map(.x = c(3, 5), ~ dplyr::filter(mtcars, wt > .x))

[[1]]
                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
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
Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8

[[2]]
                     mpg cyl disp  hp drat    wt  qsec vs am gear carb
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

using `pmap` with `dplyr::filter`

1 Answers