4, 11 and 20 October 2023

 

This course is an introduction to the basics in R, one of the leading programming language in data science. In this course you will learn how to program in R and how to use R for data analysis in biomedical science. The course will cover the basics of R programming such as R’s objects, data types and functions. It will introduce you to data manipulation, plotting functions, and briefly, to classical statistical tests and R packages. You will learn how to import/export data as well as how to combine, sort and filter your R objects to wrangle, analyze and visualize data. Rather than covering every R skill you might need, you will build a strong foundation to build on. The course will start with a brief introduction lecture. After that, lessons will consist in demo directly in R intermingled with practical exercises for the students.

 

Aim of the course: To provide an introduction to R language.

 

By the end of the course, the participants should be able to:

  • Discuss the strengths and weaknesses of R language.
  • Describe the basics of R syntax.
  • Describe the basic R programming concepts such as data types, vectors and matrices.
  • Perform operations in R such as creating or importing data frame and other objects as well as combining, sorting and subsetting these objects.
  • Make different kinds of plots.
  • Perform basic programming with R.

 

Target group: PhD candidates in the beginning of their PhD trajectory.

Maximum number of participants: 14.

 

Prerequisites: Working knowledge of English. Participants need to bring their laptop with a recent R version properly installed. Participants are also strongly encouraged to install RStudio.

 

Duration of the course: 3 days.

Location:  Sart-Tilman, to be announced 

Workload: 3 days x 7 hours per day = 21 hours.

Educators:     Benoit Charloteaux (Department of Human Genetics; CHU de Liège),

David Stern (GIGA Bioinformatics Platform, ULiège)

 

Course Syllabus/schedule

 

Day 1 (9:00 – 17:30): R fundamentals

  • General introduction to R and RStudio
  • Main data types and data structures (vectors, matrices, data.frames and lists)
  • Variable creation and manipulation

 

Day 2 (9:00 – 17:30): Exploring datasets

  • Importing/exporting datasets
  • Combining datasets
  • Extraction of summary statistics
  • Generation of publication-ready plots

 

Day 3 (9:00 – 17:30): Moving on with R

  • Introduction to R packages (tidyverse and ggplot2)
  • Control structures and vectorization
  • Challenge (practice)
updated on 9/7/23

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