Outline

R is rapidly becoming the standard platform for data manipulation, visualization and analysis and has a number of advantages over other statistical software packages. A wide community of users contribute to R, resulting in an enormous coverage of statistical procedures, including many that are not available in any other statistical program. Furthermore, it is highly flexible for programming and scripting purposes, for example when manipulating data or creating professional plots. However, R lacks standard GUI menus, as in SPSS for example, from which to choose what statistical test to perform or which graph to create. As a consequence, R is more challenging to master. Therefore, this course offers an elaborate introduction into statistical programming in R. Students learn to operate R, make plots, fit, assess and interpret a variety of basic statistical models and do advanced statistical programming and data manipulation. The topics in this course include regression models for linear, dichotomous, ordinal and multivariate data, statistical inference, statistical learning, bootstrapping and Monte Carlo simulation techniques.

Materials covered:

Daily schedule

Option 1: Without administrator rights or the ability to install R/RStudio

Steps: please sign up for RStudio Cloud. Choose the free plan.

Option 2: Installing R and RStudio from scratch

Steps:

• 1. Install R: R can be obtained here. We won’t use R directly in the course, but rather call R through RStudio. Therefore it needs to be installed.
• 2. Install RStudio Desktop: Rstudio is an Integrated Development Environment (IDE). It can be obtained as stand-alone software here. The free and open source RStudio Desktop version is sufficient.

Option 3: You have an old version of R

Steps: see this manual.

• To update R: The function updateR() in the package installR (Windows) or updateR (Mac) is the easiest route.
• To update RStudio Desktop: Download the new version here.

Materials

To ensure that you work with the latest iteration of the course materials, we advice all course participants to access the materials online.

• Part A: Introduction
  • Lecture A (slides)
  • Lecture A (pdf)
  • Practical A
  • notebook.R
  • markdown.Rmd
• Part B: Working with R: Data types and structures
  • Lecture B (slides)
  • Lecture B (pdf)
  • Practical B
  • dataset_boys.csv
  • example_csv.csv

All lectures are in html format. Practicals are files that guide you through the exercises. Use the files without solutions unless you get stuck. Please ask questions to the instructors if something is not 100% clear.

Useful references

• The base R cheatsheet
• The tidyverse style guide
• The Google R style guide

The above links are useful references that connect to today’s materials.

About rmarkdown

See also this rmarkdown cheat sheet.

Materials

To ensure that you work with the latest iteration of the course materials, we advice all course participants to access the materials online.

• Part C: Programming and reproducibility
  • Lecture C (slides)
  • Lecture C (pdf)
  • Practical C
• Part D: Tidy R
  • Lecture D (slides)
  • Lecture D (pdf)
  • Practical D
  • dataset_boys.csv

All lectures are in html format. Practicals are files that guide you through the exercises. Use the files without solutions unless you get stuck. Please ask questions to the instructors if something is not 100% clear.

Useful references

• The dplyr cheatsheet (mutate, select, etc)
• The tidyr cheatsheet (pivot_longer/wider)
• The purrr cheatsheet (map)
• Data importing and exporting
• The base R cheatsheet
• The tidyverse style guide
• The Google R style guide
• Dependency management: renv

The above links are useful references that connect to today’s materials.