ℹ️ This is the R package “tidypolars”. The Python one is here: markfairbanks/tidypolars
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 277.93ms 304.59ms 3.17 1.99MB
#> 2 tidypolars 286.78ms 362.65ms 2.35 1.2MB
#> 3 dplyr 3.16s 3.63s 0.257 1.79GB
#> 4 dtplyr 1.58s 1.92s 0.461 1.72GB
#> 5 collapse 702.79ms 948.19ms 0.987 745.96MB
#> # 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.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'))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.
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.
