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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 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.
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).
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.