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Data Vis Logo

A set of data visualization resources to accompany the Data Visualization in R and Interactive Data Visualization in R with Shiny workshops at Johns Hopkins University.

License: CC0-1.0

Table of Contents

  1. Open Access Textbooks
  2. Tutorials
  3. Examples and Resources
  4. Packages
  5. Opinions
  6. Getting Practice

General Data Visualization

  • Data Visualization - A practical introduction - The online version of Kieran Healy's book on data visualization in R.
  • Data Visualization with R - Data Visualization with R by Rob Kabacoff. Focused mostly on ggplot2, but also covers interactive data visualization and spatial data visualization in R.
  • R Graphics Cookbook - A practical guide that provides more than 150 recipes to help you generate high-quality graphs quickly, without having to comb through all the details of R’s graphing systems.

ggplot2

Shiny

Base R

ggplot2

  • Visualizing COVID-19 - Visualize the rise of COVID-19 cases globally with ggplot2.
  • Data Carpentry - Data visulization with ggplot2 - A tutorial in ggplot2 focused on ecology data.
  • The Complete ggplot2 Tutorial - An advanced ggplot2 tutorial that covers modifying components, aesthetics, legends, and plot style. Also included is a library of 50 ggplot2 visualizations, with code, for 8 categories: Correlation, Deviation, Ranking, Distribution, Composition, Change, Groups, and Spatial Data.
  • Tufte in R - Producing plots in the style of Edward Tufte in R.

Shiny

  • RStudio Shiny Tutorial - A three-part video tutorial, with accompanying slides and code, by the creators of Shiny. This is an excellent starting place for learning and improving your understanding of Shiny.

General R Visualization

  • R Graph Gallery - A gallery of hundreds of charts in R, with the corresponding code to reproduce them. A great place to see the vast diversity of plots that can be generated in R using a number of libraries such as ggplot2.

Shiny

  • Awesome-RShiny - A curated list of awesome Shiny resources, including resource guides, tutorials, galleries, app examples, and app sharing and hosting.

  • Awesome Shiny Extensions - A curated list of R pacakges that offer extended UI or server components for Shiny, allowing for enhanced functionality and aesthetics over base Shiny apps.

  • shinyapps.io - Share your Shiny applications online. Up to five apps can be shared publicly using a free account.

  • Shiny Galler - R Studio's gallery of Shiny apps, both official demos designed to highlight specific Shiny features and a showcase of user submitted Shiny apps.

ggplot2

  • ggplot2 - A powerful data visulization library in R, based on the Grammar of Graphics.
  • ggprism - The ggprism package provides various themes, palettes, and other useful functions to customise ggplots and give them the ‘GraphPad Prism’ look.
  • ggfortify - This package offers functions to allow automatic visualization of statistical results of popular R packages in ggplot2.

Data Exploration

  • visdat - Create preliminary exploratory data visualisations of an entire dataset to identify problems or unexpected features using 'ggplot2'.
  • DataExplorer - Library for conducting exploratory data analysis, including identifying dataframe memory usage and visualizing missing observations.

3D Visualization

  • RGL - A library for visualizing 3D scatterplots, good for visualizing multivariate data in 3D.
  • plotly - An interactive data visualization library in R that can be used to visualize 3D scatter plots, line plots, surface plots, mesh plots, streamtube plots, isosurface plots, and tri-surf plots.

Interactive Data Visualization

  • plotly - An interactive data visualization library in R for producing interactive, publication-quality graphs.
  • Shiny - Shiny is an R package that makes it easy to build interactive web apps straight from R.
  • leaflet - Integrate and control Leaflet interactive JavaScript maps in R.

Choosing between ggplot2 and base R graphics is not straightforward. Your choice of graphics library will depend heavily on your application and need. These articles are not meant to persuade you in choosing a particular graphics library, but instead help identify some common pitfalls with choosing a library without considering your application and audience.

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A curated list of data visualization resources in R.

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