tidypolars 
:information_source: This is the R package “tidypolars”. The Python one is here: markfairbanks/tidypolars
Overview
tidypolars provides a polars backend
for the tidyverse. The aim of tidypolars is to enable users to keep
their existing tidyverse code while using polars in the background
to benefit from large performance gains. The only thing that needs to
change is the way data is imported in the R session.
See the “Getting started”
vignette for
a gentle introduction to tidypolars.
Since most of the work is rewriting tidyverse code into polars
syntax, tidypolars and polars have very similar performance.
Click to see a small benchmark
The main purpose of this benchmark is to show that `polars` and `tidypolars` are close and to give an idea of the performance. For more thorough, representative benchmarks about `polars`, take a look at [DuckDB benchmarks](https://duckdblabs.github.io/db-benchmark/) instead. ``` r library(collapse, warn.conflicts = FALSE) #> collapse 2.0.15, see ?`collapse-package` or ?`collapse-documentation` library(dplyr, warn.conflicts = FALSE) library(dtplyr) library(polars) library(tidypolars) large_iris <- data.table::rbindlist(rep(list(iris), 100000)) large_iris_pl <- as_polars_lf(large_iris) large_iris_dt <- lazy_dt(large_iris) format(nrow(large_iris), big.mark = ",") #> [1] "15,000,000" bench::mark( polars = { large_iris_pl$ select(c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width"))$ with_columns( pl$when( (pl$col("Petal.Length") / pl$col("Petal.Width") > 3) )$then(pl$lit("long"))$ otherwise(pl$lit("large"))$ alias("petal_type") )$ filter(pl$col("Sepal.Length")$is_between(4.5, 5.5))$ collect() }, tidypolars = { large_iris_pl |> select(starts_with(c("Sep", "Pet"))) |> mutate( petal_type = ifelse((Petal.Length / Petal.Width) > 3, "long", "large") ) |> filter(between(Sepal.Length, 4.5, 5.5)) |> compute() }, dplyr = { large_iris |> select(starts_with(c("Sep", "Pet"))) |> mutate( petal_type = ifelse((Petal.Length / Petal.Width) > 3, "long", "large") ) |> filter(between(Sepal.Length, 4.5, 5.5)) }, dtplyr = { large_iris_dt |> select(starts_with(c("Sep", "Pet"))) |> mutate( petal_type = ifelse((Petal.Length / Petal.Width) > 3, "long", "large") ) |> filter(between(Sepal.Length, 4.5, 5.5)) |> as.data.frame() }, collapse = { large_iris |> fselect(c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width")) |> fmutate( petal_type = data.table::fifelse((Petal.Length / Petal.Width) > 3, "long", "large") ) |> fsubset(Sepal.Length >= 4.5 & Sepal.Length <= 5.5) }, check = FALSE, iterations = 40 ) #> Warning: Some expressions had a GC in every iteration; so filtering is #> disabled. #> # A tibble: 5 × 6 #> expression min median `itr/sec` mem_alloc `gc/sec` #>Installation
tidypolars is built on polars, which is not available on CRAN. This means
that tidypolars also can't be on CRAN. However, you can install it from
R-universe.
Sys.setenv(NOT_CRAN = "true")
install.packages("tidypolars", repos = "https://community.r-multiverse.org")Contributing
Did you find some bugs or some errors in the documentation? Do you want
tidypolars to support more functions?
Take a look at the contributing guide for instructions on bug report and pull requests.
Acknowledgements
The website theme was heavily inspired by Matthew Kay’s ggblend
package: https://mjskay.github.io/ggblend/.
The package hex logo was created by Hubert Hałun as part of the Appsilon Hex Contest.