⚠️ 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.
See the example below and the “Getting started” vignette for a gentle introduction to tidypolars.
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.
Windows or macOS
install.packages(
'tidypolars',
repos = c('https://etiennebacher.r-universe.dev', getOption("repos"))
)Linux
install.packages(
'tidypolars',
repos = c('https://etiennebacher.r-universe.dev/bin/linux/jammy/4.3', getOption("repos"))
)Example
Suppose that you already have some code that uses dplyr:
library(dplyr, warn.conflicts = FALSE)
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)) |>
head()
#> Sepal.Length Sepal.Width Petal.Length Petal.Width petal_type
#> 1 5.1 3.5 1.4 0.2 long
#> 2 4.9 3.0 1.4 0.2 long
#> 3 4.7 3.2 1.3 0.2 long
#> 4 4.6 3.1 1.5 0.2 long
#> 5 5.0 3.6 1.4 0.2 long
#> 6 5.4 3.9 1.7 0.4 longWith tidypolars, you can provide a Polars DataFrame or LazyFrame and keep the exact same code:
library(tidypolars)
iris |>
as_polars_df() |>
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)) |>
head()
#> shape: (6, 5)
#> ┌──────────────┬─────────────┬──────────────┬─────────────┬────────────┐
#> │ Sepal.Length ┆ Sepal.Width ┆ Petal.Length ┆ Petal.Width ┆ petal_type │
#> │ --- ┆ --- ┆ --- ┆ --- ┆ --- │
#> │ f64 ┆ f64 ┆ f64 ┆ f64 ┆ str │
#> ╞══════════════╪═════════════╪══════════════╪═════════════╪════════════╡
#> │ 5.1 ┆ 3.5 ┆ 1.4 ┆ 0.2 ┆ long │
#> │ 4.9 ┆ 3.0 ┆ 1.4 ┆ 0.2 ┆ long │
#> │ 4.7 ┆ 3.2 ┆ 1.3 ┆ 0.2 ┆ long │
#> │ 4.6 ┆ 3.1 ┆ 1.5 ┆ 0.2 ┆ long │
#> │ 5.0 ┆ 3.6 ┆ 1.4 ┆ 0.2 ┆ long │
#> │ 5.4 ┆ 3.9 ┆ 1.7 ┆ 0.4 ┆ long │
#> └──────────────┴─────────────┴──────────────┴─────────────┴────────────┘If you’re used to the tidyverse functions and syntax, this will feel much easier to read than the pure polars syntax:
library(polars)
# polars syntax
pl$DataFrame(iris)$
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))$
head(6)
#> shape: (6, 5)
#> ┌──────────────┬─────────────┬──────────────┬─────────────┬────────────┐
#> │ Sepal.Length ┆ Sepal.Width ┆ Petal.Length ┆ Petal.Width ┆ petal_type │
#> │ --- ┆ --- ┆ --- ┆ --- ┆ --- │
#> │ f64 ┆ f64 ┆ f64 ┆ f64 ┆ str │
#> ╞══════════════╪═════════════╪══════════════╪═════════════╪════════════╡
#> │ 5.1 ┆ 3.5 ┆ 1.4 ┆ 0.2 ┆ long │
#> │ 4.9 ┆ 3.0 ┆ 1.4 ┆ 0.2 ┆ long │
#> │ 4.7 ┆ 3.2 ┆ 1.3 ┆ 0.2 ┆ long │
#> │ 4.6 ┆ 3.1 ┆ 1.5 ┆ 0.2 ┆ long │
#> │ 5.0 ┆ 3.6 ┆ 1.4 ┆ 0.2 ┆ long │
#> │ 5.4 ┆ 3.9 ┆ 1.7 ┆ 0.4 ┆ long │
#> └──────────────┴─────────────┴──────────────┴─────────────┴────────────┘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
For more serious benchmarks about polars, take a look at DuckDB benchmarks.
library(collapse, warn.conflicts = FALSE)
#> collapse 2.0.11, see ?`collapse-package` or ?`collapse-documentation`
library(dtplyr)
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`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 polars 126.4ms 288.63ms 2.89 19.6KB 0.0722
#> 2 tidypolars 152.53ms 202.25ms 4.37 332.28KB 1.09
#> 3 dplyr 5.62s 6.06s 0.164 1.79GB 0.476
#> 4 dtplyr 835.67ms 1.03s 0.957 1.72GB 2.34
#> 5 collapse 487.95ms 653.16ms 1.50 745.96MB 1.09
# NOTE: do NOT take the "mem_alloc" results into account.
# `bench::mark()` doesn't report the accurate memory usage for packages calling
# Rust code.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/.