subsetr uses tidy evaluation principles developed in rlang to provide subset.data.frame() and [.data.frame() methods which use tidy non-standard evaluation.
For most cases they provide drop in replacements for the existing methods, but can also avoid repetitive subsetting code. Because rlang provides robust lexical scoping they are suitable for use in top level scripts, functions and packages.
# Most existing code will work identically
mtcars[mtcars$hp > 250, "mpg"]
#> [1] 15.8 15.0
# But you can also use NSE to make things simpler
mtcars[hp > 250, mpg]
#> [1] 15.8 15.0
# The `j` argument works the same as the `select` argument in `base::subset()`.
mtcars[hp > 250, mpg:disp]
#> mpg cyl disp
#> Ford Pantera L 15.8 8 351
#> Maserati Bora 15.0 8 301
mtcars[hp > 250, -mpg]
#> cyl disp hp drat wt qsec vs am gear carb
#> Ford Pantera L 8 351 264 4.22 3.17 14.5 0 1 5 4
#> Maserati Bora 8 301 335 3.54 3.57 14.6 0 1 5 8The subset() interface is identical, but it now can also work properly inside all functions.
detach(package:subsetr)
val <<- 400
f <- function(x, val) {
lapply(x, subset, hp > val)
}
f(list(mtcars), 250)
#> [[1]]
#> [1] mpg cyl disp hp drat wt qsec vs am gear carb
#> <0 rows> (or 0-length row.names)
library(subsetr)
f <- function(x, val) {
lapply(x, subset, hp > val)
}
f(list(mtcars), 250)
#> [[1]]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Ford Pantera L 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4
#> Maserati Bora 15.0 8 301 335 3.54 3.57 14.6 0 1 5 8