ℹ️ 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 instead.
library(collapse, warn.conflicts = FALSE)
#> collapse 2.0.16, 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
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt>
#> 1 polars 277.93ms 304.59ms 3.17 1.99MB
#> 2 tidypolars 286.78ms 362.65ms 2.35 1.2MB
#> 3 dplyr 3.16s 3.63s 0.257 1.79GB
#> 4 dtplyr 1.58s 1.92s 0.461 1.72GB
#> 5 collapse 702.79ms 948.19ms 0.987 745.96MB
#> # ℹ 1 more variable: `gc/sec` <dbl>
# NOTE: do NOT take the "mem_alloc" results into account.
# `bench::mark()` doesn't report the accurate memory usage for packages calling
# Rust code.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 = c("https://community.r-multiverse.org", 'https://cloud.r-project.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.