tidypolars

---

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 2.0.15, 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`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 polars      142.5ms 173.96ms     4.43     4.51MB    0.222
#> 2 tidypolars  161.9ms 206.56ms     4.70     1.78MB    2.00 
#> 3 dplyr          3.8s    4.07s     0.231    1.79GB    0.554
#> 4 dtplyr      810.6ms       1s     0.999    1.72GB    2.82 
#> 5 collapse    400.8ms  493.3ms     1.97   745.96MB    1.33

# 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.

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"))
)

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.