A lightweight extension of the base R graphics system, with support for automatic grouping, legends, facets, and various other enhancements.
tinyplot is not yet on CRAN, but can be installed from R-universe.
install.packages("tinyplot", repos = "https://grantmcdermott.r-universe.dev")Our goal is to submit to CRAN within the first few months of 2024, once we have settled on some remaining design choices and features support. You can take a look at the open issues to see what’s currently under consideration. Please feel free to weigh on these if you have opinions. We want end users to have a say in determining the final product.
R users are spoiled for choice when it comes to visualization frameworks. The options include ggplot2 (arguably the most important graphics system of the last decade) and lattice, not to mention a bewildering array of extensions built on top of, around, and in between these amazing packages.
As a result, it is perhaps not surprising that the base R graphics
system sometimes gets short shrift. This is unfortunate, because base R
offers very powerful and flexible plotting facilities. Just type
demo(graphics) or demo(persp) into your R console to get an idea.
Or, take a look at
these
two
excellent tutorials. The downside of this power and flexibility is that
base R plotting can require a fair bit 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 (zero dependency) extension of the base R graphics system
with various convenience features, particularly for representing grouped
data. For example, the core tinyplot() function—or its shorthand alias
plt()—makes it easy to plot different groups of a dataset in a single
function call and highlight these groups using modern colour palettes.
Coincident with this grouping support, tinyplot also produces
automatic legends with scope for further customization. While the
package offers several other enhancements like facets, 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 a valid plot() call
for tinyplot() without any changes to the expected output.
The tinyplot website includes a detailed introductory tutorial, with numerous examples. But here are some quickstart examples of the package in action.
library(tinyplot)Grouped scatterplot with automatic legend:
# 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:
plt(
Sepal.Length ~ Petal.Length | Species,
data = iris,
palette = "dark", pch = 16,
grid = TRUE, frame = FALSE
)
Grouped grouped density plot with automatic legend:
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:
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 for many more examples, including range plots and customization.