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Un contenu sur notre site web Un membre du personnel Une formation Une publication scientifique
Fermer
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Un contenu sur notre site web Un membre du personnel Une formation Une publication scientifique

15-16 October 2018

This course is an introduction to the basics in R programming. R is an interpreted programming language developed for statistical computing and graphics. It comes along with a free software environment. With over two million users worldwide, R is 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 from R’s object, data types and functions to classical statistical tests, plotting functions and R packages. You’ll 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’ll build a strong foundation to build on. The course will start with a one-hour 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 the 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 kind of plots
  • perform basic programming with R

Target group

PhD candidates in the beginning of their PhD trajectory. Group is limited to 20 participants.

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

Location

GIGA B34 +5

Duration of the course and workload

2 days; 2 days x 8 hours per day = 16 hours

Educator

Benoit Charloteaux  (GIGA-Genomics Core Facility, ULiège)

Course Program

Day 1

9:00 – 10:00   General introduction to R: why R? How? Strengths and weaknesses. The R environment. How to get help

10:00- 13:00   Vectors and matrices. Declaration, manipulation, extraction, tests, operations using common functions, vector recycling.

13:00- 14:00    Lunch pause

14:00- 18:00   Data.frames and lists. Import/export, manipulation, tests, sorting and subsets

Day 2

9:00- 11:00     Control structures and vectorization

11:00- 12:00   Statistical functions

12:00- 13:00   Randomizations and theoretical distributions

13:00- 14:00   Lunch pause

14:00- 18:00   Plots, plots, plots!

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