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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 56.4listcomplistcomp but with a
different syntax.listcomp and offers special
generator functions for lists, vectors, data.frames and matrices.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.