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An introduction to solving biological problems with R



R is a highly-regarded, free, software environment for statistical analysis, with many useful features that promote and facilitate reproducible research.

In this course, we give an introduction to the R environment and explain how it can be used to import, manipulate and analyse tabular data. After the course you should feel confident to start exploring your own dataset, using the materials and references provided.

Please note that although we will demonstrate how to perform statistical analysis in R, we will not cover the theory of statistical analysis in this course. Those seeking an in-depth explanation of how to perform and interpret statistical tests are advised to attend other specialized courses such as the Statistical analysis using R. Moreover, those with some programming experience in other languages (e.g. Python, Perl) might wish to attend the follow-on Data Analysis and Visualisation in R course.



Aiora Zabala, University of Cambridge

Ashley Sawle, CRUK

Avazeh Ghanbarian, University of Cambridge

Chandra Chilamakuri, CRUK

Cristian Riccio, Sanger Institute

Hugo Tavares, The Sainsbury Laboratory

Jan Attig, Crick Institute

Jing Su, CRUK

Mark Dunning, CRUK

Mukarram Hossain, University of Cambridge

Sandra Cortijo, The Sainsbury Laboratory

Silvia Hadeler, University of Cambridge

Stephanie Owen, CRUK


Audience and Prerequisites

  • No prior programming experience is required, but those attending should be able to use a plain text editor.
  • A very basic knowledge of UNIX would be an advantage, but nothing will be assumed and extremely little will be required.
  • Graduate students, Postdocs and Staff members from the University of Cambridge, Affiliated Institutions and other external Institutions or individuals.


Syllabus, Tools and Resources:

During this course you will learn about:

  • The RStudio interface to R
  • The many ways to access help about R
  • Basic object types in R
  • Importing tabular data into R
  • Manipulating data in R
  • Using in-built functions
  • Statistical testing in R
  • Executing basic data analysis workflows in R
  • Basic Plotting
  • Customizing plots
  • Basic programming with if/else statements and for loops
  • Creating reproducible reports in R


Learning Objectives

After this course you should be able to:

  • Import data and plot graphs
  • Perform statistical tests in R
  • Create a documented and reproducible piece of R code
  • Know how to develop your skills in R after the course
  • Install and use Bioconductor packages



Book Here

Course materials



Day 1
9:30 - 11:00 Introduction to R and its environment
11:00 - 12:30 Data Structures
12:30 - 13:30 Lunch
13:30 - 15:00 Data Analysis Example
15:00 - 17:30 Plotting in R
Day 2
9:30 - 11:00 Further customisation of plots
11:00 - 12:30 Statistics
12:30 - 13:30 Lunch
13:30 - 15:30 Data Manipulation Techniques
15:30 - 17:00 Programming in R
17:00 - 17:30 Further report writing
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