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Member rate £492.50
Non-Member rate £985.00
Save £45 Loyalty discount applied automatically*
Save 5% on each additional course booked
*If you attended our Methods School in the last calendar year, you qualify for £45 off your course fee.
Monday 22 ꟷ Friday 26 March 2021
2 hours of live teaching per day
This course is taking place twice in one day
09:30-12:00 and 14:00-16:30 CET
This course provides a highly interactive online 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 (the Instructor plus one highly qualified Teaching Assistant) can cater to the specific needs of each individual.
The goal of this course is to provide an accessible entry into the world of R. It 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 I aim 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:
R can read in any file format and 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 heavily intertwined.
We 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 we 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. It is not the most intuitive part, and I 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, we will export our 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.
This course will use the interactive learning environment Google Colab, for which you'll need a Google account.
We will use Slack for instant messaging, to keep in touch generally, and to facilitate discussions and Q&A.
The live element of the class is around 10 hours total across the week: this includes live coding, Q&A with the Instructor and TA, coffee breaks and getting to know each other sessions.
I will give you coding challenges to work through using the knowledge gained from the ‘live’ course elements. We can discuss solutions during the live sessions, if needed.
This course assumes no knowledge of R, or of any other programming language. All you need is a Google account.