# tidypolars

------------------------------------------------------------------------

ℹ️ This is the R package “tidypolars”. The Python one is here:
[markfairbanks/tidypolars](https://github.com/markfairbanks/tidypolars)

------------------------------------------------------------------------

## Overview

`tidypolars` provides a [`polars`](https://rpolars.github.io/) 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](https://tidypolars.etiennebacher.com/articles/tidypolars) 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 2.1.5, 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     140.93ms 151.97ms     6.34     2.32MB    0.158
#> 2 tidypolars 145.25ms 163.29ms     5.45     1.24MB    0.545
#> 3 dplyr         1.72s    1.88s     0.519    1.79GB    1.41
#> 4 dtplyr     751.04ms 920.18ms     1.10     1.72GB    2.97
#> 5 collapse   387.92ms 457.01ms     2.20   745.96MB    1.10

# NOTE: do NOT take the "mem_alloc" results into account.
# `bench::mark()` doesn't report the accurate memory usage for packages calling
# Rust code.
```

If you want to do your own benchmarks, please take a look at [How to
benchmark
tidypolars](https://tidypolars.etiennebacher.com/articles/how-to-benchmark)
first for some best practices.

## 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.

``` r

Sys.setenv(NOT_CRAN = "true")
install.packages("tidypolars", repos = c("https://community.r-multiverse.org", 'https://cloud.r-project.org'))
```

The development version contains the latest improvements and bug fixes:

``` r

# install.packages("remotes")
remotes::install_github(
  "etiennebacher/tidypolars",
  repos = c("https://community.r-multiverse.org", 'https://cloud.r-project.org')
)
```

## Related work

Several packages have been developed to handle large data more
efficiently while keeping the `tidyverse` syntax:

- [`arrow`](https://arrow.apache.org/docs/r/): one of the closest
  alternatives to `tidypolars`. Also has lazy evaluation and query
  optimizations, uses Acero in the background to translate `dplyr` code
  and perform computations.
  - **How is tidypolars different?**: Polars (and therefore
    `tidypolars`) uses an unofficial Arrow memory specification. All
    operations are implemented (and optimized) from scratch, meaning
    that query optimizations can be very different from Acero. The list
    of R functions that are translated to the Arrow engine may also
    differ.
- [`collapse`](https://sebkrantz.github.io/collapse/): has very fast
  operations but still needs to import all data into memory, which
  prevents using larger-than-RAM datasets.
  - **How is tidypolars different?**: `tidypolars` provides lazy
    evaluation that is more memory-efficient since it doesn’t import all
    data in memory. It also provides a streaming engine to handle
    larger-than-RAM datasets.
- [`dbplyr`](https://dbplyr.tidyverse.org/): allows using `dplyr` for
  data stored in a relational database by translating R code to SQL
  queries. The performance will therefore depend on the SQL backend
  used.
  - **How is tidypolars different?**: `tidypolars` doesn’t translate R
    code to SQL but directly evaluates it with Polars.
- [`dtplyr`](https://dtplyr.tidyverse.org/): uses `data.table` in the
  background for better performance but needs to import all data in
  memory, which prevents using larger-than-RAM datasets.
  - **How is tidypolars different?**: same as for `collapse`.
- [`duckplyr`](https://duckplyr.tidyverse.org/): one of the closest
  alternatives to `tidypolars`. Uses DuckDB in the background, also
  provides lazy evaluation and query optimizations. Can perform
  operations directly on R `data.frame`s.
  - **How is tidypolars different?**: similar to `arrow`, the list of R
    functions that are optimized in Polars or DuckDB isn’t identical so
    the use case will determine which tool runs the fastest. `duckplyr`
    also relies on a fallback mechanism that will run the code in
    “standard R” if the function cannot be translated. `tidypolars` is
    more conservative and will error in this case, avoiding importing
    data that may crash the session because of its size.
- [`sparklyr`](https://spark.posit.co/): uses Apache Spark in the
  background, requires installing Spark. Can perform distributed
  processing.
  - **How is tidypolars different?**: `tidypolars` doesn’t need
    installing another tool and focuses on processing data on a single
    machine, not on distributed processing.

Therefore, if you need to handle data that is larger than memory, you
have three options: `arrow`, `duckplyr`, and `tidypolars`. The best one
will probably depend on the use case and on your constraints
(e.g. `tidypolars` is available via R-universe but isn’t on CRAN).
Regarding performance, one should refer to the [DuckDB
benchmarks](https://duckdblabs.github.io/db-benchmark/) to compare
tools. Keep in mind that accurately benchmarking data processing tools
is hard; those benchmarks give useful information but don’t necessarily
apply to all contexts.

## 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](https://tidypolars.etiennebacher.com/CONTRIBUTING.html) 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.

# Package index

## Import data

Functions to import data as Polars DataFrames (`read_` functions) and
LazyFrames (`scan_` functions).

- [`read_csv_polars()`](https://tidypolars.etiennebacher.com/reference/from_csv.md)
  [`scan_csv_polars()`](https://tidypolars.etiennebacher.com/reference/from_csv.md)
  : Import data from CSV file(s)
- [`read_ipc_polars()`](https://tidypolars.etiennebacher.com/reference/from_ipc.md)
  [`scan_ipc_polars()`](https://tidypolars.etiennebacher.com/reference/from_ipc.md)
  : Import data from IPC file(s)
- [`read_ndjson_polars()`](https://tidypolars.etiennebacher.com/reference/from_ndjson.md)
  [`scan_ndjson_polars()`](https://tidypolars.etiennebacher.com/reference/from_ndjson.md)
  : Import data from NDJSON file(s)
- [`read_parquet_polars()`](https://tidypolars.etiennebacher.com/reference/from_parquet.md)
  [`scan_parquet_polars()`](https://tidypolars.etiennebacher.com/reference/from_parquet.md)
  : Import data from Parquet file(s)

## Export data

Functions to export Polars DataFrames (`write_` functions) and
LazyFrames (`sink_` functions).

- [`sink_csv()`](https://tidypolars.etiennebacher.com/reference/sink_csv.md)
  : Stream output to a CSV file
- [`sink_ipc()`](https://tidypolars.etiennebacher.com/reference/sink_ipc.md)
  : Stream output to an IPC file
- [`sink_ndjson()`](https://tidypolars.etiennebacher.com/reference/sink_ndjson.md)
  : Stream output to a NDJSON file
- [`sink_parquet()`](https://tidypolars.etiennebacher.com/reference/sink_parquet.md)
  : Stream output to a parquet file
- [`write_csv_polars()`](https://tidypolars.etiennebacher.com/reference/write_csv_polars.md)
  : Export data to CSV file(s)
- [`write_ipc_polars()`](https://tidypolars.etiennebacher.com/reference/write_ipc_polars.md)
  : Export data to IPC file(s)
- [`write_json_polars()`](https://tidypolars.etiennebacher.com/reference/write_json_polars.md)
  : Export data to JSON file(s)
- [`write_ndjson_polars()`](https://tidypolars.etiennebacher.com/reference/write_ndjson_polars.md)
  : Export data to NDJSON file(s)
- [`write_parquet_polars()`](https://tidypolars.etiennebacher.com/reference/write_parquet_polars.md)
  : Export data to Parquet file(s)

## Functions from `dplyr`

- [`count(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/count.polars_data_frame.md)
  [`tally(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/count.polars_data_frame.md)
  [`count(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/count.polars_data_frame.md)
  [`tally(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/count.polars_data_frame.md)
  [`add_count(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/count.polars_data_frame.md)
  [`add_count(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/count.polars_data_frame.md)
  : Count the observations in each group
- [`semi_join(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/semi_join.polars_data_frame.md)
  [`anti_join(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/semi_join.polars_data_frame.md)
  [`semi_join(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/semi_join.polars_data_frame.md)
  [`anti_join(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/semi_join.polars_data_frame.md)
  : Filtering joins
- [`arrange(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/arrange.polars_data_frame.md)
  : Order rows using column values
- [`bind_cols_polars()`](https://tidypolars.etiennebacher.com/reference/bind_cols_polars.md)
  : Append multiple Data/LazyFrames next to each other
- [`bind_rows_polars()`](https://tidypolars.etiennebacher.com/reference/bind_rows_polars.md)
  : Stack multiple Data/LazyFrames on top of each other
- [`compute(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/compute.polars_lazy_frame.md)
  [`collect(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/compute.polars_lazy_frame.md)
  : Run computations on a LazyFrame
- [`cross_join(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/cross_join.polars_data_frame.md)
  [`cross_join(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/cross_join.polars_data_frame.md)
  : Cross join
- [`distinct(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/distinct.polars_data_frame.md)
  [`distinct(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/distinct.polars_data_frame.md)
  [`duplicated_rows()`](https://tidypolars.etiennebacher.com/reference/distinct.polars_data_frame.md)
  : Remove or keep only duplicated rows in a Data/LazyFrame
- [`explain(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/explain.polars_lazy_frame.md)
  : Show the optimized and non-optimized query plans
- [`filter(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/filter.polars_data_frame.md)
  [`filter(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/filter.polars_data_frame.md)
  [`filter_out(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/filter.polars_data_frame.md)
  [`filter_out(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/filter.polars_data_frame.md)
  : Keep or drop rows that match a condition
- [`left_join(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/mutating-joins.md)
  [`right_join(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/mutating-joins.md)
  [`full_join(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/mutating-joins.md)
  [`inner_join(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/mutating-joins.md)
  [`left_join(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/mutating-joins.md)
  [`right_join(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/mutating-joins.md)
  [`full_join(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/mutating-joins.md)
  [`inner_join(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/mutating-joins.md)
  : Mutating joins
- [`group_by(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/group_by.polars_data_frame.md)
  [`ungroup(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/group_by.polars_data_frame.md)
  [`group_by(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/group_by.polars_data_frame.md)
  [`ungroup(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/group_by.polars_data_frame.md)
  : Group by one or more variables
- [`group_split(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/group_split.polars_data_frame.md)
  : Grouping metadata
- [`group_vars(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/group_vars.polars_data_frame.md)
  [`group_vars(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/group_vars.polars_data_frame.md)
  [`group_keys(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/group_vars.polars_data_frame.md)
  [`group_keys(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/group_vars.polars_data_frame.md)
  : Grouping metadata
- [`mutate(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/mutate.polars_data_frame.md)
  [`mutate(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/mutate.polars_data_frame.md)
  : Create, modify, and delete columns
- [`pull(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/pull.polars_data_frame.md)
  [`pull(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/pull.polars_data_frame.md)
  : Extract a variable of a Data/LazyFrame
- [`relocate(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/relocate.polars_data_frame.md)
  [`relocate(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/relocate.polars_data_frame.md)
  : Change column order
- [`rename(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/rename.polars_data_frame.md)
  [`rename(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/rename.polars_data_frame.md)
  [`rename_with(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/rename.polars_data_frame.md)
  [`rename_with(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/rename.polars_data_frame.md)
  : Rename columns
- [`rowwise(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/rowwise.polars_data_frame.md)
  [`rowwise(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/rowwise.polars_data_frame.md)
  : Group input by rows
- [`select(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/select.polars_data_frame.md)
  [`select(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/select.polars_data_frame.md)
  : Select columns from a Data/LazyFrame
- [`show_query(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/show_query.polars_data_frame.md)
  [`show_query(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/show_query.polars_data_frame.md)
  : Show the polars code equivalent to the tidypolars pipeline
- [`slice_tail(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/slice_tail.polars_data_frame.md)
  [`slice_tail(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/slice_tail.polars_data_frame.md)
  [`slice_head(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/slice_tail.polars_data_frame.md)
  [`slice_head(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/slice_tail.polars_data_frame.md)
  [`slice_sample(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/slice_tail.polars_data_frame.md)
  : Subset rows of a Data/LazyFrame
- [`summarize(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/summarize.polars_data_frame.md)
  [`summarise(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/summarize.polars_data_frame.md)
  [`summarize(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/summarize.polars_data_frame.md)
  [`summarise(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/summarize.polars_data_frame.md)
  : Summarize each group down to one row

## Functions from `tidyr`

- [`complete(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/complete.polars_data_frame.md)
  [`complete(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/complete.polars_data_frame.md)
  : Complete a data frame with missing combinations of data
- [`drop_na(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/drop_na.polars_data_frame.md)
  [`drop_na(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/drop_na.polars_data_frame.md)
  : Drop missing values
- [`fill(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/fill.polars_data_frame.md)
  : Fill in missing values with previous or next value
- [`pivot_longer(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/pivot_longer.polars_data_frame.md)
  [`pivot_longer(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/pivot_longer.polars_data_frame.md)
  : Pivot a Data/LazyFrame from wide to long
- [`pivot_wider(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/pivot_wider.polars_data_frame.md)
  [`pivot_wider(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/pivot_wider.polars_data_frame.md)
  : Pivot a Data/LazyFrame from long to wide
- [`replace_na(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/replace_na.polars_data_frame.md)
  [`replace_na(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/replace_na.polars_data_frame.md)
  : Replace NAs with specified values
- [`separate(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/separate.polars_data_frame.md)
  [`separate(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/separate.polars_data_frame.md)
  : Separate a character column into multiple columns based on a
  substring
- [`separate_longer_delim_polars()`](https://tidypolars.etiennebacher.com/reference/separate_longer.md)
  [`separate_longer_position_polars()`](https://tidypolars.etiennebacher.com/reference/separate_longer.md)
  : Split a string column into rows
- [`uncount(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/uncount.polars_data_frame.md)
  [`uncount(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/uncount.polars_data_frame.md)
  : Uncount a Data/LazyFrame
- [`unite(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/unite.polars_data_frame.md)
  [`unite(`*`<polars_lazy_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/unite.polars_data_frame.md)
  : Unite multiple columns into one by pasting strings together
- [`unnest_longer_polars()`](https://tidypolars.etiennebacher.com/reference/unnest_longer_polars.md)
  : Unnest a list-column into rows

## Other methods

Other functions or S3 methods.

- [`summary(`*`<polars_data_frame>`*`)`](https://tidypolars.etiennebacher.com/reference/summary.polars_data_frame.md)
  : Summary statistics for a Polars DataFrame

- [`tidypolars_options`](https://tidypolars.etiennebacher.com/reference/tidypolars_options.md)
  :

  `tidypolars` global options

## Other Polars functions

Other Polars-specific functions (most are deprecated).

- [`fetch()`](https://tidypolars.etiennebacher.com/reference/fetch.md)
  **\[deprecated\]** :

  Fetch `n` rows of a LazyFrame

- [`make_unique_id()`](https://tidypolars.etiennebacher.com/reference/make_unique_id.md)
  **\[deprecated\]** : Create a column with unique id per row values

- [`partition_by()`](https://tidypolars.etiennebacher.com/reference/partitioned_output.md)
  [`partition_by_key()`](https://tidypolars.etiennebacher.com/reference/partitioned_output.md)
  [`partition_by_max_size()`](https://tidypolars.etiennebacher.com/reference/partitioned_output.md)
  **\[experimental\]** : Helper functions to export data as a
  partitioned output

## Advanced

Functions for very specific usage.

- [`.tp`](https://tidypolars.etiennebacher.com/reference/dot-tp.md) :
  Get tidypolars function translation without loading their original
  package

# Articles

### Reference

- [List of supported
  functions](https://tidypolars.etiennebacher.com/articles/supported-functions.md):

### Advanced

- [How to benchmark
  tidypolars](https://tidypolars.etiennebacher.com/articles/how-to-benchmark.md):
- [R and Polars
  expressions](https://tidypolars.etiennebacher.com/articles/r-and-polars-expressions.md):