polars (R Polars)

Polars is a blazingly fast DataFrame library, written in Rust.

The goal of Polars is to deliver fast, efficient data processing that:

Utilizes all available cores on your machine.
Optimizes queries to reduce unneeded work/memory allocations.
Handles datasets much larger than your available RAM.
Follows a consistent and predictable API.
Adheres to a strict schema (data-types should be known before running

the query).

This polars R package provides the R bindings for Polars. It can be used to process Polars DataFrames and other data structures, convert objects between Polars and R, and can be integrated with other common R packages.

To learn more, read the online documentation for this R package, and the user guide for Python/Rust Polars.

Install

The recommended way to install this package is from the R-multiverse community repository:

r
Sys.setenv(NOT_CRAN = "true")
install.packages("polars", repos = "https://community.r-multiverse.org")

More recent (development) version may be installed from the rpolars R-universe repository:

r
Sys.setenv(NOT_CRAN = "true")
install.packages('polars', repos = c("https://rpolars.r-universe.dev", "https://cloud.r-project.org"))

Usage

To avoid conflicts with other packages and base R function names, many of polars’ functions are hosted in the pl environment, and accessible via the pl$ prefix. Most of the functions for polars objects should be chained with the $ operator.

Additionally, the majority of the functions are intended to match the Python Polars API.

These mean that Polars queries written in Python and in R are very similar.

For example, writing the example from the user guide of Python/Rust in R:

r
library(polars)
Prepare a CSV file
csv_file <- tempfile(fileext = ".csv")
write.csv(iris, csv_file, row.names = FALSE)
Create a query plan (LazyFrame) with filtering and group aggregation
q <- pl$scancsv(csvfile)$filter(pl$col("Sepal.Length") > 5)$group_by(
  "Species",
  .maintain_order = TRUE
)$agg(pl$all()$sum())
Execute the query plan and collect the result as a Polars DataFrame
df <- q$collect()
df #> shape: (3, 5) #> ┌────────────┬──────────────┬─────────────┬──────────────┬─────────────┐ #> │ Species    ┆ Sepal.Length ┆ Sepal.Width ┆ Petal.Length ┆ Petal.Width │ #> │ ---        ┆ ---          ┆ ---         ┆ ---          ┆ ---         │ #> │ str        ┆ f64          ┆ f64         ┆ f64          ┆ f64         │ #> ╞════════════╪══════════════╪═════════════╪══════════════╪═════════════╡ #> │ setosa     ┆ 116.9        ┆ 81.7        ┆ 33.2         ┆ 6.1         │ #> │ versicolor ┆ 281.9        ┆ 131.8       ┆ 202.9        ┆ 63.3        │ #> │ virginica  ┆ 324.5        ┆ 146.2       ┆ 273.1        ┆ 99.6        │ #> └────────────┴──────────────┴─────────────┴──────────────┴─────────────┘

There are also some functions to manipulate polars objects using base R and some popular other packages.

r
Subset a Polars DataFrame using the [ operator
df[1:2, 1:2]
#> shape: (2, 2)
#> ┌────────────┬──────────────┐
#> │ Species    ┆ Sepal.Length │
#> │ ---        ┆ ---          │
#> │ str        ┆ f64          │
#> ╞════════════╪══════════════╡
#> │ setosa     ┆ 116.9        │
#> │ versicolor ┆ 281.9        │
#> └────────────┴──────────────┘
Execute a query plan and collect the result as a tibble data frame
tibble::as_tibble(q)
#> # A tibble: 3 × 5
#>   Species    Sepal.Length Sepal.Width Petal.Length Petal.Width
#>   <chr>             <dbl>       <dbl>        <dbl>       <dbl>
#> 1 setosa             117.        81.7         33.2         6.1
#> 2 versicolor         282.       132.         203.         63.3
#> 3 virginica          324.       146.         273.         99.6

The Get Started vignette (vignette("polars")) provides a more detailed introduction.