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https://github.com/grantmcdermott/tinyplot
Lightweight extension of the base R graphics system
https://github.com/grantmcdermott/tinyplot
Last synced: 3 days ago
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Lightweight extension of the base R graphics system
- Host: GitHub
- URL: https://github.com/grantmcdermott/tinyplot
- Owner: grantmcdermott
- License: apache-2.0
- Created: 2023-02-21T18:10:55.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-07T01:44:55.000Z (7 days ago)
- Last Synced: 2024-11-07T02:39:40.342Z (7 days ago)
- Language: R
- Homepage: https://grantmcdermott.com/tinyplot
- Size: 66.3 MB
- Stars: 235
- Watchers: 9
- Forks: 7
- Open Issues: 20
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS.md
- License: LICENSE.md
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README
# tinyplot 
## What
A lightweight extension of the base R graphics system, with support for
automatic grouping, legends, facets, and various other enhancements.
The stable version of **tinyplot** is available on CRAN.
``` r
install.packages("tinyplot")
```
Or, you can grab the latest development version from R-universe.
``` r
install.packages("tinyplot", repos = "https://grantmcdermott.r-universe.dev")
```
## Why
R users are spoiled for choice when it comes to visualization
frameworks. The options, of course, include **lattice** and **ggplot2**;
say nothing of the bewildering array of extensions built around, on top
of, and in between these amazing packages.[^1]
Given the wealth of options, it is perhaps understandable that even avid
R users can overlook the base R **graphics** system. This is
unfortunate, because base R offers very powerful and flexible plotting
facilities.[^2] The downside of this power and flexibility is that base
R plotting can require a lot of manual tinkering. A case in point is
plotting grouped data with an appropriate legend. Doing so with the
generic `plot()` function can require several function calls or a loop,
fiddling with your plot regions, and then generating the legend
manually.
The **tinyplot** package aims to remove this overhead. It provides a
lightweight extension of the base R graphics system that preserves the
same core logic and syntax, but with numerous convenience features to
make base R plotting a more user-friendly experience. For example, the
core `tinyplot()` function—or its shorthand alias `plt()`—makes it easy
to plot grouped datasets and generate automatic legends in a single
function call. Or, you can display groups in separate facets without
having to worry about manually setting plot regions. While **tinyplot**
offers these and various other enhancements, it tries as far as possible
to be a drop-in replacement for the equivalent base plotting function.
Users should generally be able to swap out a valid `plot()` call for
`tinyplot()`/`plt()` without any changes to the expected output.
It is worth emphasizing that **tinyplot** requires *only* base R. It has
zero recursive dependencies and we have been careful to keep its
installation size down to a minimum. **tinyplot** should thus provide an
attractive and lightweight option for package developers (or regular R
users!) who would like to produce convenient and sophisticated plots,
combined with a minimal footprint.
## Quickstart
The **tinyplot** website includes a detailed [introductory
tutorial](https://grantmcdermott.com/tinyplot/vignettes/intro_tutorial.html),
with numerous examples. But here are some quickstart examples of the
package in action.
``` r
library(tinyplot)
```
Grouped scatterplot with automatic legend:
``` r
# with(iris, tinyplot(x = Petal.Length, y = Sepal.Length, by = Species)) # atomic
tinyplot(Sepal.Length ~ Petal.Length | Species, data = iris) # formula
```
If you would prefer to save on a few keystrokes, you can use the
shorthand `plt()` alias instead instead of typing out `tinyplot()` in
full. Here’s the same plot with this shorthand alias, plus a few
aesthetic tweaks:
``` r
plt(
Sepal.Length ~ Petal.Length | Species,
data = iris,
palette = "dark", pch = 16,
grid = TRUE, frame = FALSE
)
```
Grouped grouped density plot with automatic legend:
``` r
plt(
~ Petal.Length | Species,
data = iris,
type = "density",
palette = "dark", fill = "by",
grid = TRUE,
main = "Distribution of petal lengths by species"
)
```
Grouped scatterplot with (continuous) gradient legend, combined with
facet layout:
``` r
plt(
Sepal.Length ~ Petal.Length | Sepal.Length, data = iris,
facet = ~Species, facet.args = list(bg = "grey90"),
pch = 19,
main = "Faceted Species!",
grid = TRUE, frame = FALSE
)
```
Hopefully, these have been enough to pique your interest. Head over to
the [intro
tutorial](https://grantmcdermott.com/tinyplot/vignettes/intro_tutorial.html)
for many more examples, including range plots and customization.
[^1]: Both **lattice** and **ggplot2** are built on top of the **grid**
package, which was incoporated into the base R distribution way back
in R 2.0.0. **ggplot2**, in particular, is arguably the most
important and influential (high-level) graphics library of the last
two decades, across any programming language.
[^2]: Just type `demo(graphics)` or `demo(persp)` into your R console to
get an idea. Or, take a look at
[these](https://github.com/karoliskoncevicius/r_notes/blob/main/baseplotting.md)
[two](https://quizzical-engelbart-d15a44.netlify.app/2021-2022_m2-data-2_visu-2_practice#1)
excellent tutorials.