Summary

The article provides an overview of 30 R packages designed for advanced data visualization, covering interactive graphics, correlation matrices, heatmaps, color themes, 3D visualization, and other specialized plots.

Abstract

The article introduces R users to a curated selection of 30 packages that extend the capabilities of R for data visualization. These packages range from those that facilitate the creation of interactive graphics, such as ggvis, shiny, and plotly, to specialized tools for visualizing correlation matrices like corrplot and ggcor. It also includes packages for generating heatmaps with enhanced features, such as ComplexHeatmap and d3heatmap. The article highlights packages that offer a variety of color themes and palettes inspired by TV shows, tech companies, and scientific journals, including tvthemes, ggtech, and ggsci. For 3D visualization, packages like scatterplot3d and rayshader are discussed. Additionally, the article covers other unique plotting capabilities, such as parliament diagrams with ggpol, raincloud plots with ggrain and gghalves, and statistical graphics with ggstatsplot. The packages mentioned aim to enhance the user's ability to create informative, attractive, and publication-quality visualizations.

30 R Packages For Data Visualization That You May Not Know Of

The packages/add-ins we will cover in this article include:

Interactive Graphics: ggvis, esquisse, ggThemeAssist, plotly, shiny, flexdashboard
Correlation Matrix: corrplot, ggcor, ggally
Heatmap: heatmap.2, d3heatmap, pheatmap, ComplexHeatmap, InteractiveComplexHeatmap
Color Theme: tvthemes, ggtech, ggsci, ggthemr, MetBrewer, ghibli, ggcute, ggpattern
3D Visualization: scatterplot3d, plot3d, rayshader, ggthreed
Other Plots: ggpol, raincloud plot (ggrain, gghalves),ggstatsplot (highly recommend!), waffle plot (waffle, ggwaffle), treemapify

1. Interactive Graphics

1.1 ggvis

ggvis creates rich interactive graphics that you can play with locally.

1.2 esquisse add-in

You can create plots by dragging and dropping.

Example from https://github.com/dreamRs/esquisse

1.3 ggThemeAssist

It’s an add-in that gives a graphical interface for editing ggplot2 elements. Highly recommend!

Example from https://github.com/calligross/ggthemeassist

1.4 plotly

plotly’s R graphing library makes interactive, publication-quality graphs.

1.5 shiny

shiny is an R package that makes it easy to build interactive web apps straight from R.

https://learning.nceas.ucsb.edu/2019-11-RRCourse/introduction-to-shiny.html

1.6 flexdashboard

Similar to the shiny package, flexdashboard supports the construction of data visualization dashboards in a simpler way. It visualizes data through R Markdown files and publishes them as visual dashboards in the form of web pages.

2. Correlation Matrix

2.1 corrplot

corrplot provides a visual exploratory tool on correlation matrix

Example from https://cran.r-project.org/web/packages/corrplot/vignettes/corrplot-intro.html

2.2 ggcor

ggcor provides functions to visualize a correlation matrix.

Example from https://github.com/hannet91/ggcor

2.3 ggally

ggally extends ggplot2 by providing several functions including:

ggcor(): for pairwise correlation matrix plot
ggpairs(): for scatterplot plot matrix
ggsurv(): for survival plot

3. HeatMap

Packages can generate heatmaps include the following:

heatmap.2
d3heatmap
pheatmap
ComplexHeatmap
InteractiveComplexHeatmap

Translate from pheatmap to ComplexHeatmap — A Bioinformagician

4. Color Theme

4.1 tvthemes

tvthemes is a collection of various ggplot2 themes and color/fill palettes based on everybody’s favorite TV shows including Avatar, Brooklyn Nine-Nine, Game of Thrones, Parks & Recreation, The Simpsons: theme + palette, and Spongebob.

4.2 ggtech

ggtech package can generate plots with theme colors from tech companies.

d + theme_tech(theme="google") + ggtitle("Google ggplot2 theme") + scale_fill_tech(theme="google")

Example from https://exts.ggplot2.tidyverse.org/ggtech.html

4.3 ggsci

ggsci offers a collection of ggplot2 color palettes inspired by scientific journals include NEJM, JAMA, Lancet, etc.

4.4 ggthemr

ggthemr includes 17 themes, such as flat, dust, light, solarized, etc.

4.5 MetBrewer

Metbrewer is a color palette package inspired by works at the Metropolitan Museum of Art in New York.

https://twitter.com/BlakeRobMills/status/1469749740871077893

4.6 ghibli

The ghibli package provides color palettes inspired by Studio Ghibli founded by Hayao Miyazaki. It provides palettes from 9 films, with a light, medium, and dark variant for each.

Ponyo corresponds to the movie Ponyo on the Cliff

Totoro: My Neighbor Totoro

Marnie: When Marnie Was There

Laputa: Castle in the Sky

Mononoke: Princess Mononoke

Sprited: Spirited Away

Yesterday: Only Yesterday

Kiki: Kiki’s Delivery Service

4.7 ggcute

4.8 ggpattern

ggpattern provides custom ggplot2 geoms which support filled areas with geometric and image-based patterns.

5. 3D Visualization

5.1 scatterplot3d

scatterplot3d is an R package for the visualization of multivariate data in a three dimensional space.

http://www.sthda.com/english/wiki/scatterplot3d-3d-graphics-r-software-and-data-visualization

5.2 plot3d

5.3 rayshader

The rayshader package converts ggplot2 visualizations into 3D.

5.4 ggthreed

6. Other Plots

6.1 ggpol

ggpol adds parliament diagrams and various other visualizations and convenience functions to ggplot2.

https://datavizstory.com/a-parliament-diagram-in-r/

time-series highlight, https://erocoar.github.io/ggpol/

6.2 ggrain, gghalves

ggrain and gghalves allow you to create raincloud plots. A raincloud plot is a combination of a boxplot, density (violin) and individual data points.

6.3 ggstatsplot

ggstatsplot creates graphics with details from statistical tests included in the information-rich plots themselves. Highly recommend!

6.4 waffle, ggwaffle

Waffle charts (waffle, ggwaffle)can be used to communicate parts of a whole for categorical quantities.

6.5 treemapify

In a treemap, each tile represents a single observation, with the area of the tile proportional to a variable.

6.6 ggbump

Bump charts are good to use to plot ranking over time, or other examples when the path between two nodes have no statistical significance.

Thanks for reading! :)