Introduction to R

 

14,15 October 2019

 

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 2 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 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.

Maximum number of participants: 12.

 

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: 2 days.

Location: GIGA B34 +5

Workload: 2 days x 8 hours per day = 16 hours.

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

 

Course Syllabus/schedule

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 break

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

 

Day 2

9:00- 11:00     Plot basics: scatter plot, bar plot, histogram, boxplot. Intro to ggplot2

11:00- 13:00   Theoretical distributions, sampling and control structures

13:00- 14:00   Lunch break

14:00- 18:00   Challenge (practice)