Hertie School's 2025 Summer R and Math Bootcamp

Taught by Jackson Luckey at the Hertie School in Berlin in summer 2025.

Before the first day, please install R and RStudio.

Slides

1. Overview: Overview of what we will cover during the bootcamp.
2. Git: Introduces Git to get everyone to the point that they can use this repo throughout the bootcamp.
3. Intro to R: Introduces the basics of base R (objects, vectors, functions, and dataframes).
4. Intro to Data Visualization: Introduces the basics of data visualization with R using ggplot2.
5. Working with Dataframes with dplyr: Covers the main dplyr verbs (functions) such as filter(), select(), mutate(), summarize(), and group_by().
6. Vectors and Matrices: Overview of vectors and matrices in R. Unused in the exercises but will be helpful for Math for Data Science.
7. Working with Dataframes in Base R: A quick look at working with Dataframes in base R. Useful for getting a sense of what dplyr replaces.

Slides from Intro to Data Science

On Saturday and Sunday I used some slides from an older version of the Intro to Data Science course.

On Saturday I used part of the Tidyverse slides.

On Sunday I used the version control / Git slides.

You might also look through the command line slides.

Exercises

The prerequisites (unless stated otherwise) are the Intro to Data Visualization and Working with Dataframes with dplyr slides.

• dplyr and ggplot with NYC Flights: Practice using dplyr and ggplot2 with a dataset of all flights departing NYC in 2013.
• Leader Assassination as a Natural Experiment: Practice using dplyr and ggplot2 to investigate the effects of leader assassinations on democracy and war. Adapted from Quantiative Social Science: An Introduction with Tidyverse.

Sources

This course borrows liberally from Simon Munzert's Introduction to Data Science (IDS) course as the bootcamp primarily serves as preparation for the course.

I also took inspiration, examples, and exercises from:

• R Intro in the R Manual
• R for Data Science
• Quantiative Social Science: An Introduction
• Quantiative Social Science: An Introduction with Tidyverse
• Data Analysis for Social Science: A Friendly and Practical Introduction