Teaching materials for the R package ggplot2
- Thursday May 15, 2014 under the auspices of the Vancouver R Users Group and hosted at the BC Centres for Disease Control
- Thursday May 14, 2015 as part of the Workshop on Big Data in Environmental Science at UBC
- Tuesday July 19, 2016 Data Carpentry at the University of Zurich
There are some slides.
- Directory for everything re: the slides: ggplot2-tutorial-slides
- Keynote file
- Individual slides as PNGs (scroll through README to see 'em all)
- Slides on Speakerdeck
We do live coding together. Indicative content:
- Scatterplots: demo | R source
- Stripplots: demo | R source
- Exploring distribution of a quantitative variable: demo | R source
- Drawing bars: demo | R source
- Change overall look and feel via themes: demo | R source
- Take control of a qualitative color scheme: demo | R source
- Bubble and line plots, lots of customization: demo | R source
Links and references
- All the figure-making content in my STAT545 course
- ggplot2 on github
- ggplot2 "homepage"
- ggplot2 online docs
- The R Graphics Cookbook by Winston Chang.
- The graphs section of Winston Chang's website Cookbook for R. The book listed above contains much more material, but the website is good too.
- ggplot2: Elegant Graphics for Data Analysis by Hadley Wickham. Book's companion website offers one R script per chapter, providing the code used in the book.
- A quick introduction to ggplot2 by Karthik Ram. Slides and code from a 2 hour talk/hands on presentation for ggplot2 beginners.
- Nine simple ways to make it easier to (re)use your data by Ethan P White, Elita Baldridge, Zachary T. Brym, Kenneth J. Locey, Daniel J. McGlinn, Sarah R. Supp. Ideas in Ecology and Evolution 6(2): 1–10, 2013. doi:10.4033/iee.2013.6b.6.f http://library.queensu.ca/ojs/index.php/IEE/article/view/4608. Section 4 "Use Standard Data Formats" is especially good reading.
- Tidy data by Hadley Wickham. Submitted to The Journal of Statistical Software. Preprint available http://vita.had.co.nz/papers/tidy-data.pdf.
- RStudio's Cheat Sheets: http://www.rstudio.com/resources/cheatsheets/. Especially relevant:
- Data wrangling
- Data visualization
- writing figures to file -- covered in the slides only for now