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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# List comprehensions
<!-- badges: start -->
[](https://github.com/dirkschumacher/listcomp/actions)
[](https://CRAN.R-project.org/package=listcomp)
[](https://github.com/dirkschumacher/listcomp/actions)
<!-- badges: end -->
The package implements [list comprehensions](https://en.wikipedia.org/wiki/List_comprehension) 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
``` r
remotes::install_github("dirkschumacher/listcomp")
```
``` r
install.packages("listcomp")
```
## Example
This is a basic example which shows you how to solve a common problem:
```{r example}
library(listcomp)
head(gen_list(c(x, y), x = 1:100, y = 1:100, z = 1:100, x < 5, y < 5, z == x + y))
```
```{r}
gen_list(c(x, y), x = 1:10, y = x:5, x < 2)
```
This is how the code looks like:
```{r}
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)
```
You can also burn in external variables
```{r}
z <- 10
gen_list(c(x, y), x = 1:!!z, y = x:5, x < 2)
```
It also supports parallel iteration by passing a list of named sequences
```{r}
gen_list(c(i, j, k), list(i = 1:10, j = 1:10), k = 1:5, i < 3, k < 3)
```
The code then looks like this:
```{r}
lst_verbose(c(i, j, k), list(i = 1:10, j = 1:10), k = 1:5, i < 3, k < 3)
```
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.
```{r}
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)
)
```
How slow is it compared to a for loop and lapply for a very simple example?
```{r}
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"
)
```
# Related packages
* [lc](https://github.com/mailund/lc) Uses a similar syntax as `listcomp`
* [comprehenr](https://github.com/gdemin/comprehenr) Uses a similar code generation approach as `listcomp` but with a different syntax.
* [listcompr](https://github.com/patrickroocks/listcompr) Uses a similar syntax as `listcomp` and offers special generator functions for lists, vectors, data.frames
and matrices.