Filter tibble in R when column names (to be filtered) and values are in vectors?

This might be an esoteric question or use-case, but is there a quick way to filter a tibble when the column names and values are inside vectors?

Say I want to filter mpg and hp in mtcars. I could do something like:

filter(mtcars, mpg >= 15 & hp >= 100)

But instead, say I have several filtering cases -- with the columns to be filtered in one vector and the values in another. (In practice, I might have four or five cases in a larger df.)

car_stat <- c('mpg', 'hp')
car_value <- c(15, 100)

Obviously this doesn't work.

filter(mtcars, car_stat >= car_value)

But is there some succinct dplyr/tidyverse way to filter with vectors, or am I resigned to using some loop to break it up into separate vectors, each of length one?

Using your variables and values, you can turn those into filtering expressions. Here we use the base R Map and bquote functions

car_stat <- c('mpg', 'hp')
car_value <- c(15, 100)

criteria <- unname(Map(function(c, v) bquote(.(as.name(c))>=.(v)), car_stat, car_value))
criteria
# [[1]]
# mpg >= 15
# 
# [[2]]
# hp >= 100

This creates a list of expressions that you want for your filter. Then you can inejct them to filter with !!!

dplyr::filter(mtcars, !!!criteria)
#                    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
# 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
# ...

Here is another approach that leverages the env parameter of data.table 1.14.9

library(data.table)
cars = setDT(copy(mtcars))
do.call(
fintersect,
lapply(1:2, \(i) cars[k>=z, env = list(k=car_stat[i], z =car_value[i])])
)

Output:

     mpg cyl  disp  hp drat    wt  qsec vs am gear carb id
1: 21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4  1
2: 21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4  2
3: 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1  4
4: 18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2  5
5: 18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1  6
6: 19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4 10
7: 17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4 11
8: 16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3 12
9: 17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3 13
10: 15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3 14
11: 15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2 22
12: 15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2 23
13: 19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2 25
14: 30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2 28
15: 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4 29
16: 19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6 30
17: 15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8 31
18: 21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2 32