18-22 June 2018

R is the statistical software the most used in the world. It is extremely powerful, free of charge and open source. Despite these benefits, many avoid R, or struggle with it, as writing computer code to do any operation -- a requirement in R -- is at first both difficult and intimidating.

This course aims at overcoming those challenges by providing solid basics in R. At the end of the course, participants should feel much more at ease writing a computer script in the R language which covers the entire spectrum of a statistical analysis: reading data, editing them, plotting them, and analysing them. Because linear models are the dominant statistical tool in many fields, the part of the course focusing on analyses per se (see schedule) will focus on those, but principles seen during the class should greatly help those interested in other kind of analyses as well. The course will be presented over five days and will mix explanations and guided exercises. Students are free to practice with their own datasets during the course.

This course is aimed at scientists from quantitative sciences (e.g. biology, epidemiology, psychology...). It has been created with biologists in mind but it should accommodate scientists from other disciplines. No previous experience with R is required. Participants should have a basic familiarity with statistical terms and concepts.

Eisenacher Str. 1, 10777 Berlin

Monday 18th-Classes from 9:30 to 17:30

Monday is DATA day

After briefly introducing myself and R, this first day will be dedicated to the data. R is software dedicated to data analysis, so mastering the basics of data manipulation in R is essential for further steps. I will explain how to import data into R and how to manipulate them (e.g. from adding or removing rows or columns, to merging tables and using pivot tables). This will be good practice for students to learn the basics of the R language. I will illustrate how to do everything using R base (that is R out of the box), but I will also introduce some efficient R packages (e.g. dplyr) that allow users to perform some operations on large datasets a little faster.