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Monday 18 – Friday 22 July 2022
Minimum of 2 hours' live teaching per day
13:00 – 16:00 CEST
This online course provides a highly interactive teaching and learning environment, using state of the art online pedagogical tools. It is designed for a demanding audience (researchers, professional analysts, advanced students) and capped at a maximum of 16 participants so that the teaching team can cater to the specific needs of each individual.
The goal is to provide an accessible entry into the world of R. The course will enable you to approach the most common analysis tasks in R with confidence.
We will cover data cleaning, exploratory data analysis, creating visualisations, and writing entire academic papers using RMarkdown.
R has an unfortunate reputation as a steep learning curve, but the aim is to dispel this myth and show how a range of recent developments make R not just powerful, but accessible to newcomers.
3 credits Engage fully with class activities
4 credits Complete a post-class assignment
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.
He uses a wide variety of methods in his research, particularly automated text analysis (and attached various machine learning approaches), network analysis and more traditional econometric techniques.
The guiding logic of the course is to give practical knowledge of the whole data analysis workflow:
Monday – Importing data
Tuesday – Data wrangling / cleaning
Wednesday – Visualisation | Exploratory analysis
Thursday – Analysis | Writing our own functions
Friday – Reporting the results
R can read in any file format. We will cover a range of the most commonly used types, including plain txt, csv, Excel xlsx, Stata, Sas, and SPSS.
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 we must learn how to write our own functions. This is not the most intuitive part, and we will focus on making it as accessible as possible without relying on too much computer science / programming jargon. Alongside this, we’ll look at a few statistical applications in R (t-test and OLS regression).
At the end of the course, you will export your results from R or even write an academic paper or report using RMarkdown.
Thankfully, R (and programming in general) is one of the subjects that can work well in a purely online setting for teaching and learning. We will provide RStudio Cloud accounts. All R codes and data will be uploaded into Canvas 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 TA, 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
This course assumes no knowledge of R, or of any other programming language. One short reading is required.