R is a powerful language for data science in many disciplines of research with a steep learning curve. The tidyverse group of packages provide a dialect that greatly simplifies data importing, cleaning, processing, and visualization as well as providing reproducible workflows, replacing many intricacies of R with clear and easy to learn syntax.
The course will be run on Tuesday 07 through Friday 10 May, 2023.
The four day course provides a complete introduction to data science in R with the tidyverse. The course will not go deep into statistics but rather getting data ready, some exploratory analysis, visualization and handling models. Preparing data takes up to 90% of the time spent in analysis — speeding this up is the mission of this course.
The Course website details the requirements, schedule and necessary preparations.
Students will receive 1 ECTS in category 1, which requires solving the exercises presented in the course. Students that completed this course in the previous years are not eligible for ECTS.
This iteration of the course will focus on solving practical programming challenges of participants.
Participants must have basic experience in R or intermediate experience in other programming environments for data analysis such as Matlab, Octave or Python. This is not a beginners’ course to programming.
Please see the detailed instructions on our course website
Schedule at glance
Day 1 will review the basics of R and loading data via the readr package as well as Markdown.
Day 2 will introduce tidying and organising data via the tidyr and dplyr packages as well as ggplot2 for visualisation.
Day 3 will look at functional programming tools using the purrr package, which greatly simplifies repeating operations. Many statistical packages have complicated and idiosyncratic data structures. The broom package helps to convert them to consistent data structures.
Participants are encouraged to bring their own data for analysis, convert existing code to tidyverse or perform a project on Day 4.
Registration will open in December 2022.
Aurélien Ginolhac (DLSM)
Roland Krause (LCSB, Elixir-Luxembourg)
Veronica Codoni (LCSB, Elixir-Luxembourg)