-
Notifications
You must be signed in to change notification settings - Fork 3
/
README.Rmd
154 lines (122 loc) · 4.83 KB
/
README.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# tidypolars <a href="https://tidypolars.etiennebacher.com/"><img src="man/figures/logo.png" align="right" height="160" /></a>
<!-- badges: start -->
[](https://github.com/etiennebacher/tidypolars/actions/workflows/check.yml)
[](https://etiennebacher.r-universe.dev/tidypolars)
[](https://app.codecov.io/gh/etiennebacher/tidypolars?branch=main)
<!-- badges: end -->
---
:information_source: This is the R package "tidypolars". The Python one is here: [markfairbanks/tidypolars](https://github.com/markfairbanks/tidypolars)
---
<!-- * [Motivation](#motivation) -->
<!-- * [Installation](#installation) -->
<!-- * [Example](#example) -->
<!-- * [Contributing](#contributing) -->
## 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.
<details>
<summary>Click to see a small benchmark</summary>
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)
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 = ",")
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
)
# NOTE: do NOT take the "mem_alloc" results into account.
# `bench::mark()` doesn't report the accurate memory usage for packages calling
# Rust code.
```
</details>
## 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 eval=FALSE}
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](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.