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

• Lecture videos

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
• Lecture videos

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
• Exercises for class
• Solutions to the 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 6: Visualization

• 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 videos
• Code for the videos and recap exercise solutions
• Data for video lecture and recaps
• Lecture notes
• Data for lecture notes
• Exercise data

Note: The exercise Wrangling2 is an optional extension with new data sets on which you can practice your data wrangling skills.

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: Exploratory data analysis

• Solutions to the in-class exercises
• Intermediate results: tidy data
• Script used during the in-person lecture

Session 11: 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 and questions

• Script from the lecture

Session 13: Introduction to data analysis

Mandatory reading

• James et al. (2021): Introduction and Section 2.1.

Session 14: Sampling

• Script from the lecture and solution to exercises

Mandatory reading

• Tutorial on iteration and Monte Carlo Simulations
• Statistical Inference via Data Science, Chapter 7: Sampling

Sessions 15 - 18: Linear regression

• Exercise solutions and figures in slides
• Additional exercises
• Guess the correlation browser game

Mandatory readings

• Statistical Inference via Data Science, Chapter 5: Basic regression
• Statistical Inference via Data Science, Chapter 6: Multiple regression

Further readings

• James et al. (2021): Chapter 3

Final recap session

• Tasks
• Data
• Possible solutions
• In-class solutions