R introduction
Contents
R part I training
Slides R part I training
 Slides for the course
 Tutorial with exercises
 Demo exercises of day1
 Demo exercises of day2
 Demo exercises of day3
 Solutions of the exercises of the first day of the training
 Solutions of the exercises of the second day of the training
 Solutions of the exercises of the third day of the training
 Script to calculate average of two vectors
 Script to calculate average of any number of vectors
 Script to calculate standard error of the mean
 Script to calculate confidence intervals
 R script to start the second day of the training
Files R part I training
Data sets
 Cheat sheet
 Babies data set for graphing
 Heatmap data set
 Hormone data set
 GH levels in teens data set
 GH levels in adults data set
 qPCR data set
 DE genes from RNASeq experiment
 Proteins data set
 data set with 4 variables
 Fly genetics data set
 T cell data set
 weight and height data set for the first group exercise
 Hormone concentrations for 2 groups of 9 patients for the second group exercise
 Hormone before and after data set for the second group exercise
 Hormone durig activity data set for the third group exercise
 log10 transformed CNRQs of control samples for qPCR analysis and visualization in R
 log10 transformed CNRQs of treated samples for qPCR analysis and visualization in R
 Microarray data set
 RNASeq data set
 metadata for the RNASeq data set
R scripts
 R script to calculate the average of two vectors to complete
 R script to calculate the average of any number of vectors to complete
 R script to complete for the first exercise: statistical analyses
 R script to complete for the second exercise: recap on graphs + a few new tricks
 R script to complete for the third exercise: advanced graphs
 R script to complete for qPCR analysis
 R script to complete for microarray analysis, check our microarray wiki page for a detailed description of the workflow.
 R script to complete for bulk RNASeq analysis with DESeq2, check our RNASeq wiki page for more info
 R script with solutions for the first exercise: statistical analyses
 R script with solutions for the second exercise: recap on graphs + a few new tricks
 R script with solutions for the third exercise: advanced graphs
 R script with solutions for qPCR analysis
 R script with solutions for microarray analysis
 R script with solutions for bulk RNASeq analysis with DESeq2
 R script for bulk RNASeq analysis with edgeR
 R script for single cell RNASeq analysis with Seurat
 exercise on metagenomics analysis with vegan
R part II training
Slides R part II training
 Chapter 2: Basics
 Chapter 4: Exploring data with graphs
 Chapter 5: Exploring assumptions
 Chapter 6: Correlation
 Chapter 7: Linear regression
 Chapter 9: Comparing two means
 Chapter 10: Oneway ANOVA
 Chapter 12: Twoway ANOVA
 Chapter 13: Randomized block designs
 Chapter 15: Nonparametric statistics
 Chapter 18: Categorical data
 Chapter 20: Varia
Demo scripts
 R script for chapter 4: Graphs
 R script for chapter 5: Assumptions
 R script for chapter 6: Correlations
 R script for chapter 7: Regression
 R script for chapter 8: Regression extended
 R script for chapter 9: Comparing two means
 R script for chapter 10: Generalised linear models
 R script for chapter 12: Factorial selection
 R script for chapter 13: Randomized complete block designs
 R script for chapter 15: Nonparametric tests
 R script for chapter 18: Categorical variables
Exercise scripts
 R script for chapter 4: Graphs
 R script for chapter 5: Assumptions
 R script for chapter 6: Correlations
 R script for chapter 7: Regression
 R script for chapter 9: Comparing two means
 R script for chapter 10: Generalised linear models
 R script for chapter 11
 R script for chapter 12: Factorial selection
 R script for chapter 13: Randomized complete block designs
 R script for chapter 15: Nonparametric tests
 R script for chapter 18: Categorical variables
Solutions to exercises
 Soltions for chapter 4: Graphs
 Solutions for chapter 5: Assumptions
 Solutions for chapter 6: Correlations
 Solutions for chapter 7: Regression
 Solutions for chapter 8: Regression extended
 Solutions for chapter 9: Comparing two means
 Solutions for chapter 10: Generalised linear models
 Solutions for chapter 12: Factorial selection
 Solutions for chapter 13: Randomized complete block designs
 Solutions for chapter 15: Nonparametric tests
 Solutions for chapter 18: Categorical variables
Files R part II training
 zip file containing the data sets for day 1 of the training
 cholesterol simulation data set
 goats data set
 nitrogen data set
Experiment Design training
Files Experiment Design training
 data demo RSM
 data demo variance components
 R script demo and exercse experiment design
 R script field trial
On this page you can find an introduction to R, the statistical programming language.
We will assume that you're working in RStudio, although most of the things we show will also work in the R editor.
RStudio
Although you can work directly in the R editor, most people find it easier to use RStudio on top of R. RStudio is free separate software to make R more userfriendly. It's essentially a graphical user interface for R.
Check out this video tutorial on the RStudio user interface.
Extra info and tips:
 Features of the console that make life easier e.g. retrieving previous commands…
 How to search the History
 Find and replace can be opened using Ctrl+F.
 RStudio supports the automatic completion of words using the Tab key.
For example, if you have an object named relfreq (relative frequencies) in your workspace, you can type r and then Tab and RStudio will automatically show a list of possibilities to complete the full name of the object.
Installation
Install R (and RStudio)
R is available at the CRAN website. Upon choosing a CRAN mirror, you can download R.
R is available for Linux, Mac and Windows.
You can download RStudio from the RStudio website.
Install R packages
Check out this video tutorial on installing packages in R.
Part of the reason that R is so popular is the enormous diversity of packages that are available for R. Packages are collections of R programs that are able to perform a certain analysis, e.g. Matrix is a package that contains all the Rcode you need for creating and working with matrices. R packages are available at the CRAN and Bioconductor websites.
How to install packages in R ? 

Open R or RStudio as administrator and install packages as follows:
Once you start typing the name of a package, RStudio tries to autocomplete it:
Select the package you want to install – in this case ggplot2  and click the Install button.

Issues with installation of R/R packages
Mac: error message: Setting LC_CTYPE failed, using “C” during package installation
 Close RStudio
 Open Terminal
 Type:
defaults write org.Rproject.R force.LANG en_US.UTF8
 Close Terminal
 Start RStudio and retry installation
Creating a project in RStudio
An R project is a folder in RStudio where all your work on one project (e.g. a chapter in your PhD dissertation) is gathered. Projects help you to stay organized. When you create a project R will load data and scripts from and save results to this folder.
How to create a project ? 

Select New Project from the Project dropdown menu in the top right corner.
Next you have to specify if the project should reside in a new or in an existing directory on your computer.

Creating and running scripts
A script is a text file that contains all the commands you will use. You cannot only write and run scripts but you can also save them so next time you need to do a similar analysis you can change and rerun the script with minimal effort. An R project can contain multiple scripts.
Commands
Scripts consist of a list of commands. Commands in R have a certain format:
output < method(list of arguments)
Alternatively, you may use the following format:
output = method(list of arguments)
For example:
p = ggplot(mtcars,(aes(wt,mpg))
In this example ggplot() is the method. It generates a plot so the plot p is the output of the method. Before a function can start the actions and calculations that it encodes, it needs prior information: input data and parameter settings. These are called the arguments of the function. In this example the arguments are:
mtcars: a data frame consisting of 9 columns containing the input data
aes(wt,mpg): defines the columns you want to plot (wt and mpg) and how you want to plot them (wt on the X axis and mpg on the Yaxis)
Creating a new script
How to create a new script in RStudio ? 

Click File in the top menu: New File > R Script

Loading packages in R
You only need to install a package once. But each time you want to use a package you have to load it (activate its functions).
How to load packages in R ? 

Loading a package is done by typing the following command directly in the console or as part of a script:
# load packages library("packagename") 
Getting help / documentation in R
You can find a lot of documentation online:
 The documentation section of the R website
Unfortunately this section is nor easily accessible nor wellstructured and it can be quite a challenge to consult the help files of different R packages and functions online. By far the most userfriendly interface for searching the R documentation is the Rdocumentation website.  Documentation of RStudio
 Quick R: for those who would like to make the transition to R (from SAS, SPSS, Stata)
 Rbloggers: Rnews and tutorials contributed by bloggers
 InsideR: a website created by people who are using R containing examples, howto’s, packages…
Some R commands allow you to consult the documentation:
How to show the documentation of a package ? 

You can ask R to open a browser with a documentation file for a complete package:
help(package=”packagename”) 
You can also ask for information on specific topics, e.g. find out what arguments a method needs...
How to show the documentation of specific topics ? 

You can ask for documentation of individual classes:
help(topicname) Try some examples to see what help() does: help(help) help(matrix) help(print) Alternatively you can use: ? topicname 
Data types in R
How to check which data type a variable belongs to ? 

To know which class an object belongs to:
class(objectname) 
A data frame contains several elements. It essentially has a matrix structure but some of the columns of the data frame might be matrices themselves. The different elements are allowed to contain different data types.
How to list the elements of a data frame ? 

To find out which elements are available for a data frame, use the following command:
names(dataframename) 
All columns in a matrix must have the same data type and length.
How to list the names of the rows or columns of a matrix ? 

To find out which elements are available for a data frame, use the following command:
rownames(matrixname) columnnames(matrixname) 