Course outline

Slides and exercises

To start the respective exercises execute the following code, substituting ExCode with the code given in the column ‘Exercise code’ in the table above:

learnr::run_tutorial(
  name = "ExCode", 
  package = "DataScienceExercises", 
  shiny_args=list("launch.browser"=TRUE))

Note that a recent version of the DataScienceExercises-package must be installed. For more information on how to install and use the exercises, see the respective tutorial on exercises.

Readings, tutorials, and further material

Session 1: Introduction and installation

Mandatory readings

• Tutorial: installing basic software
• Tutorial: installing the required R packages
• Tutorial: doing exercises

Session 2: Basics of R and R-Studio

Lecture notes and exercise solutions

Mandatory readings

• R for Data Science, Chapter 4: Workflow - basics
• Tutorial: First steps in R
• Tutorial: Functions

Further readings

• R Studio IDE cheat sheet

Session 3: Basic object types

• Lecture videos
• Lecture notes and solutions to intermediate exercises

Mandatory readings

• Tutorial: Vectors

Session 4: Advanced object types

• Session notes

Mandatory readings

• Tutorial: Factors and data frames
• R for Data Science, Chapter 10: Tibbles

Session 5: Recap and practice

• Exercises
• Solutions

Session 6: Visualization

• Lecture script
• Lecture notes

Mandatory readings

• R for Data Science, Chapter 3: Data visualization

Further readings

• ggplot2 cheat sheet

Session 7: Project management and data import

• Example data
• Session notes and solutions to exercises

Mandatory readings

• Tutorial: How to organize your R project
• Tutorial: Read and write data

Sessions 8 and 9: Data wrangling

• Lecture notes
• Data for lecture notes
• Exercise data
• Lecture script from the session and exercise solutions

Mandatory readings

• R for Data Science, Chapter 5: Data transformation
• R for Data Science, Chapter 12: Tidy data

Further readings

• dplyr cheat sheet
• tidyr cheat sheet

Session 10: Quarto/R Markdown

• DesasterMarkdown.pdf
• NicerMarkdown.pdf
• Code for the markdown desaster and a possible solution

Mandatory readings

• The CommonMark markdown tutorial
• Quarto tutorial I: the basics
• Quarto tutorial II: computations
• Quarto tutorial III: authoring quarto documents

Further reading

• Blog introducing Quarto
• Quarto and R Markdown
• The comprehensive Quarto documentation
• Markdown basics
• The R Markdown Cookbook

The practical exercise for this topic can be found here.

Session 12: Recap of data wrangling

• Example data
• Solutions to the exercises

Sessions 13 & 14: Simple linear regression

• Exercise solutions
• Guess the correlation browser game

Mandatory readings

• Statistical Inference via Data Science, Chapter 5: Basic regression

Sessions 15 & 16: Multiple linear regression

• Exercises

Mandatory readings

• Statistical Inference via Data Science, Chapter 6: Multiple regression

Session 17: Recap and practice

TBA