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 260.22ms 317.05ms 3.03 19.2KB
#> 2 tidypolars 305.11ms 362.84ms 2.21 157.66KB
#> 3 dplyr 2.85s 3.19s 0.290 1.79GB
#> 4 dtplyr 1.36s 2.53s 0.416 1.72GB
#> 5 collapse 662.73ms 825.88ms 1.21 745.96MB
#> # 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.