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List comprehensions

R build status CRAN status R-CMD-check

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.

Installation

remotes::install_github("dirkschumacher/listcomp")
install.packages("listcomp")

Example

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 2
gen_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 5

This 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 5

It 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 2

The 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     28.0

How 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

Related packages

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.