Graphs and visualising data

This course is given within the context of the IMPRS BeSmart Summerschool. The course combines asynchronous and synchronous teaching.
Asynchronous part
  • Videos can be found here.
  • Here is the handout.
  • Exercises: Think about a graph (or two graphs) for your own research project. Describe what the graph is supposed to show and how it might look. Submit this description four days before the synchronous part starts.
Synchronous part
In the synchronous part we will discuss the exercises, your questions and your comments.

For this part we will use RStudio and the software mentioned below.

Graphs and Illustrations can contribute a lot to the success of a scientific paper. In this course we will discuss different ways to use graphs in our research.
Here is a preliminary version of the handout.
  1. Introduction
  2. Graphs with ggplot2
  3. More graphs with ggplot2
  4. Nominal data
  5. Continuous data, distributions
  6. Continuous data, causal relations, other problems
  7. (Lattice)
  • William S. Cleveland, “The Elements of Graphing Data”, AT&T Bell Laboratories, New Jersey, 1994.
  • Deepayan Sarkar, “Lattice — Multivariate Data Visualization with R”. Springer, New-York, 2008.
  • Edward Tufte, “The Visual Display of Quantitative Information”. Bertrams. 2001.
  • Hadley Wickham, “ggplot2: Elegant Graphics for Data Analysis (Use R!)”. Springer, New-York, 2016.
You should try all the examples on your own computer. You should have an up-to-date version of R installed. We will also use the following libraries: aplpack, car, Ecdat, directlabels, dplyr, Hmisc, ggplot2, gridExtra, ggmosaic, ggthemes, ks, lattice, latticeExtra, MASS, mgcv, plotrix, pwt10, reshape2, tidyr, vcd.