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Monday 6 – Friday 10 February 2023
Minimum 2 hours of live teaching per day
13:00 – 15:30 CET on Monday, Tuesday, Wednesday and Friday
13:45 – 16:15 CET on Thursday
This in-person course provides a highly interactive blended learning environment, using state-of-the-art in-person pedagogical tools. You will have access to online videos and tools before the course. 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.
By the end of this course, you will be able to approach the most common analysis tasks in R with confidence. The aim is to provide an accessible entry into the world of R and show how a range of recent developments make R not just powerful, but accessible to newcomers.
We will cover data cleaning, exploratory data analysis, creating visualisations, and writing entire academic papers using RMarkdown.
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.
@aakos_mThe 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).
When learning a programming language, it is inevitable we learn to write our own functions. This is not hugely intuitive, so this course makes it as accessible as possible, with minimal 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.
We offer a number of online pre-course materials for you to access at your own pace.
Pre-recorded videos help you start exploring R before the live sessions. You can keep all course materials for future reference. The videos will walk you through how to make a local install of R Studio.
During the course week, expect to be in class on campus for over ten hours in total. The Instructors will host Q&A sessions and will designate ‘office hours’, during which you can sign up for a quick one-to-one consultation.
This course assumes no knowledge of R, or of any other programming language. One short reading is required.
Each course includes pre-course assignments, including readings and pre-recorded videos, as well as daily live lectures totalling at least two hours. The instructor will conduct live Q&A sessions and offer designated office hours for one-to-one consultations.
Please check your course format before registering.
Live classes will be held daily for two hours on a video meeting platform, allowing you to interact with both the instructor and other participants in real-time. To avoid online fatigue, the course employs a pedagogy that includes small-group work, short and focused tasks, as well as troubleshooting exercises that utilise a variety of online applications to facilitate collaboration and engagement with the course content.
In-person courses will consist of daily three-hour classroom sessions, featuring a range of interactive in-class activities including short lectures, peer feedback, group exercises, and presentations.
This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc.). Registered participants will be informed at the time of change.
By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, please contact us before registering.