UNL R Workshops
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  • Introduction to R
  • Graphics with ggplot2
  • Data Wrangling
  • Modeling

On this page

  • Timetable
  • Solutions
  • Useful Links
    • R graphics with ggplot2
    • Types of Charts and Chart Styling

Graphics with ggplot2

ggplot2 hex sticker logo

After the end of Graphics with ggplot, we expect you to be able to do the following:

  • Visualize data in the ggplot2 package: do basic plots, as well as know about the layer system, and be able to structure complex graphics
  • Take a data set and use static graphics to look for interesting features.
  • Know about some aspects of human perception and what to avoid when plotting data.

Timetable

Time Notes Lectures and Resources
9 - 9:45 Look at that! basic plots: scatterplots, boxplots, histograms, barcharts and more
some aesthetics: color, shape, …
1-GraphicsIntro.R
9:45 - 10:30 Basics Why is data visualization important?.
Data Types, Formats, and Structures
Formatting your data: A tidy data discussion
2-Basics.R
10:30 - 10:45 Break
10:45 - 12:00 Layers of Graphics More detailed look at the ggplot2 package: layers, geoms.
3-Layers.R
12:00 - 1:00 Lunch Break (on your own)
1:00 - 2:30 What do we see? Discussion of faceting and cognitive aspects of human perception.
4-Perception.R
2:30 - 2:45 Break
2:45 - 3:50 Polishing your plots Finishing touches: options, themes.
5-PolishingPlots.R
3:50 - 4:00 Evaluation Help us make the workshops better!

Solutions

  • Your Turn Solutions

Useful Links

R graphics with ggplot2

  • ggplot2 cheat sheet
  • ggplot2 aesthetics cheat sheet
  • ggplot2 reference guide
  • Combine multiple plots with the patchwork, cowplot, and gridExtra packages
  • ggplot2 Elegant Graphics for Data Analysis by Hadley Wickham, Danielle Navarro, and Thomas Lin Pedersen
  • maps in ggplot2
  • ggplot2 Extensions see also slides by Ashirwad Barnwal

Types of Charts and Chart Styling

  • The R Graph Gallery
  • R Graph Catalog
  • The Data Vis Project
  • Data Visualization Catalogue
  • The pros and cons of chart taxonomies
  • Data Visualization Style Guidelines