Introduction to R

online course

Learn how to approach the most common analysis tasks in R with confidence, from exporting your results from R to writing an academic paper or report using Rmarkdown.

Need to know

This course assumes no knowledge of R, or of any other programming languages.

You will be required to complete one short reading ahead of the course.

In depth

The guiding logic of the course is to give practical knowledge of the whole data analysis workflow:

Day 1 – Importing data
Day 2 – Data wrangling / cleaning
Day 3 – Visualisation | Exploratory analysis
Day 4 – Analysis | Writing our own functions
Day 5 – Reporting the results

R can read in any file format. A range of the most commonly used types, including plain txt, csv, Excel xlsx, Stata, Sas, and SPSS, will be covered over the duration of the course.

Reflecting on the realities of typical research projects, the course focuses on data cleaning and getting data into a shape which allows us to analyse and visualise it properly. The exploratory analysis and data visualisation parts are closely intertwined.

You will learn how to make descriptive statistics, how to group data, and how to explore a given dataset. The course puts strong emphasis on visualisation components, and you will learn to use the ggplot2 package to produce wonderful looking graphs (as an example, most of the Financial Times' charts are made with R in ggplot2).

As part of learning a programming language, it is inevitable that you must learn how to write your own functions. This is not the most intuitive part, and you will focus on making it as accessible as possible without relying on too much computer science / programming jargon. Alongside this, you’ll explore some statistical applications in R (t-test and OLS regression).

At the end of the course, you will export your results from R and have the opportunity to write an academic paper or report using RMarkdown.

How the course will work online

R is one of the subjects that can work well in an online setting for teaching and learning. You will be provided with an RStudio Cloud account, and all R codes and data will be uploaded into the Learning Management System for you.

The live element of the class is around 10 hours in total across the week: this includes live coding, Q&A with the Instructor and Teaching Assistants, coffee breaks and getting-to-know-each-other sessions.

You will work through coding challenges using the knowledge gained from the ‘live’ course elements. Solutions can be presented during the live sessions, if needed.

Course leader

Akos Mate is a research fellow at the Centre for Social Sciences in Hungary. His key research area is the political economy of the European Union and its members’ fiscal governance.

Target group

Advanced students, researchers, and professional analysts.

Course aim

The goal of this course is to make R more accessible to beginners and provide them with the skills and confidence needed to perform common analysis tasks in R. The course covers important topics such as data cleaning, exploratory data analysis, visualization, and academic writing using RMarkdown. By the end of the course, learners should feel comfortable approaching these tasks with R.

While R has a reputation for being difficult to learn, the course aims to dispel this myth by showcasing recent developments that have made R more accessible to newcomers. By the end of the course, learners should be able to use R effectively and confidently for their data analysis needs.

Credits info

4 EC
You can earn up to four credits for attending this course.
3 ECTS credits – Attend 100% of live sessions and engage fully with class activities.
4 ECTS credits – Attend 100% of live sessions, engage fully with class activities and complete a post-class assignment.

Fee info

GBP 478: ECPR Member
GBP 956: ECPR Non-Member

Scholarships

Funding applications for the 2023 ECPR Summer School in Research Methods and Techniques are now closed.
For more details on funding opportunities for ECPR's other events, please visit our website.