The package implements list comprehensions as purely syntactic sugar with a minor runtime overhead. It constructs nested for-loops and executes the byte-compiled loops to collect the results.
remotes::install_github("dirkschumacher/listcomp")install.packages("listcomp")This is a basic example which shows you how to solve a common problem:
library(listcomp)
head(gen_list(c(x, y), x = 1:100, y = 1:100, z = 1:100, x < 5, y < 5, z == x + y))
#> [[1]]
#> [1] 1 1
#>
#> [[2]]
#> [1] 1 2
#>
#> [[3]]
#> [1] 1 3
#>
#> [[4]]
#> [1] 1 4
#>
#> [[5]]
#> [1] 2 1
#>
#> [[6]]
#> [1] 2 2gen_list(c(x, y), x = 1:10, y = x:5, x < 2)
#> [[1]]
#> [1] 1 1
#>
#> [[2]]
#> [1] 1 2
#>
#> [[3]]
#> [1] 1 3
#>
#> [[4]]
#> [1] 1 4
#>
#> [[5]]
#> [1] 1 5This is how the code looks like:
lst_verbose <- function(expr, ...) {
deparse(listcomp:::translate(rlang::enquo(expr), rlang::enquos(...)))
}
lst_verbose(c(x, y), x = 1:10, y = x:5, x < 2)
#> [1] "{"
#> [2] " .lc_result <- list()"
#> [3] " .lci_x <- 1:10"
#> [4] " for (x in .lci_x) for (y in x:5) {"
#> [5] " if (!(x < 2)) {"
#> [6] " next"
#> [7] " }"
#> [8] " .lc_result[[length(.lc_result) + 1]] <- c(x, y)"
#> [9] " }"
#> [10] " .lc_result"
#> [11] "}"You can also burn in external variables
z <- 10
gen_list(c(x, y), x = 1:!!z, y = x:5, x < 2)
#> [[1]]
#> [1] 1 1
#>
#> [[2]]
#> [1] 1 2
#>
#> [[3]]
#> [1] 1 3
#>
#> [[4]]
#> [1] 1 4
#>
#> [[5]]
#> [1] 1 5It also supports parallel iteration by passing a list of named sequences
gen_list(c(i, j, k), list(i = 1:10, j = 1:10), k = 1:5, i < 3, k < 3)
#> [[1]]
#> [1] 1 1 1
#>
#> [[2]]
#> [1] 1 1 2
#>
#> [[3]]
#> [1] 2 2 1
#>
#> [[4]]
#> [1] 2 2 2The code then looks like this:
lst_verbose(c(i, j, k), list(i = 1:10, j = 1:10), k = 1:5, i < 3, k < 3)
#> [1] "{"
#> [2] " .lc_result <- list()"
#> [3] " .lci_k <- 1:5"
#> [4] " {"
#> [5] " parallel_seq <- list(i = 1:10, j = 1:10)"
#> [6] " for (.lc_ps_it in seq_along(parallel_seq[[1]])) {"
#> [7] " i <- parallel_seq[[\"i\"]][[.lc_ps_it]]"
#> [8] " j <- parallel_seq[[\"j\"]][[.lc_ps_it]]"
#> [9] " for (k in .lci_k) {"
#> [10] " if (!(i < 3)) {"
#> [11] " next"
#> [12] " }"
#> [13] " {"
#> [14] " if (!(k < 3)) {"
#> [15] " next"
#> [16] " }"
#> [17] " .lc_result[[length(.lc_result) + 1]] <- c(i, "
#> [18] " j, k)"
#> [19] " }"
#> [20] " }"
#> [21] " }"
#> [22] " }"
#> [23] " .lc_result"
#> [24] "}"It is quite fast, but the order of filter conditions also greatly determines the execution time. Sometimes, ahead of time compiling is slower than running it right away.
bench::mark(
a = gen_list(c(x, y), x = 1:100, y = 1:100, z = 1:100, x < 5, y < 5, z == x + y),
b = gen_list(c(x, y), x = 1:100, x < 5, y = 1:100, y < 5, z = 1:100, z == x + y),
c = gen_list(c(x, y), x = 1:100, y = 1:100, z = 1:100, x < 5, y < 5, z == x + y, .compile = FALSE),
d = gen_list(c(x, y), x = 1:100, x < 5, y = 1:100, y < 5, z = 1:100, z == x + y, .compile = FALSE)
)
#> Warning: Some expressions had a GC in every iteration; so filtering is disabled.
#> # A tibble: 4 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 a 16.09ms 17.19ms 58.8 112KB 39.2
#> 2 b 4.04ms 4.13ms 227. 112KB 35.9
#> 3 c 273.06ms 273.08ms 3.66 280B 22.0
#> 4 d 785.56µs 813.97µs 1182. 280B 28.0How slow is it compared to a for loop and lapply for a very simple example?
bench::mark(
a = gen_list(x * 2, x = 1:1000, x**2 < 100),
b = gen_list(x * 2, x = 1:1000, x**2 < 100, .compile = FALSE),
c = lapply(Filter(function(x) x**2 < 100, 1:1000), function(x) x * 2),
d = {
res <- list()
for (x in 1:1000) {
if (x**2 >= 100) next
res[[length(res) + 1]] <- x * 2
}
res
},
time_unit = "ms"
)
#> # A tibble: 4 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <dbl> <dbl> <dbl> <bch:byt> <dbl>
#> 1 a 1.95 2.00 494. 56.7KB 45.8
#> 2 b 0.390 0.404 2452. 280B 38.5
#> 3 c 0.308 0.326 3037. 15.8KB 69.2
#> 4 d 0.163 0.174 5705. 0B 56.4- lc Uses a similar syntax as
listcomp - comprehenr Uses a similar
code generation approach as
listcompbut with a different syntax. - listcompr Uses a
similar syntax as
listcompand offers special generator functions for lists, vectors, data.frames and matrices.
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