• Introduction to R
  • 1 Welcome
  • 2 What is R ?
  • 3 What is RStudio ?
    • 3.1 RStudio access
    • 3.2 RStudio interface
    • 3.3 Setting up the folder structure for the course
  • 4 Paths and directories
    • 4.1 Tree of directories
    • 4.2 Navigate the tree of directory with the R terminal
  • 5 R basics
    • 5.1 Arithmetic operators
    • 5.2 Simple calculations
    • 5.3 Objects in R
    • 5.4 Assignment operators
    • 5.5 Assigning data to an object
  • 6 Functions
  • 7 R scripts
    • 7.1 Create and save a script
    • 7.2 R syntax
    • 7.3 RStudio tips in the console
    • 7.4 Exercice 1. Getting started.
  • 8 Data types
  • 9 Data structures
    • 9.1 Vectors
      • 9.1.1 Creating a vector
      • 9.1.2 Vector manipulation
      • 9.1.3 Combining vectors
      • 9.1.4 Numeric vector manipulation
      • 9.1.5 Character vector manipulation
    • 9.2 Exercise 2. Numeric vector manipulation
      • 9.2.1 Exercise 2a.
      • 9.2.2 Exercise 2b.
    • 9.3 Exercise 3. Character vector manipulation
      • 9.3.1 Exercise 3a.
      • 9.3.2 Exercise 3b.
    • 9.4 Factors
    • 9.5 Matrices
      • 9.5.1 Creating a matrix
      • 9.5.2 Two-dimensional object
      • 9.5.3 Matrix manipulation
    • 9.6 Data frames
      • 9.6.1 Create a data frame
      • 9.6.2 Data frame manipulation:
    • 9.7 Two-dimensional structures manipulation
      • 9.7.1 Dimensions
      • 9.7.2 Manipulation
    • 9.8 Exercise 4. Matrix manipulation
    • 9.9 Exercise 5. Data frame manipulation
      • 9.9.1 Exercise 5a
      • 9.9.2 Exercise 5b
      • 9.9.3 Exercise 5c
  • 10 Missing values
  • 11 Input / Output
    • 11.1 On vectors
    • 11.2 On data frames or matrices
    • 11.3 Exercise 6.
      • 11.3.1 Exercise 6a. Input / output
      • 11.3.2 Exercise 6b - I/O on data frame: play with the arguments of read.table
      • 11.3.3 Exercice 6c - I/O on a data frame
  • 12 Library and packages
    • 12.1 R base
    • 12.2 R contrib
    • 12.3 Install a package
    • 12.4 Load a package
    • 12.5 Check what packages are currently loaded
    • 12.6 List functions from a package
    • 12.7 RStudio server at CRG
    • 12.8 Exercise 7: Library and packages
  • 13 Regular expressions
    • 13.1 Find simple matches with grep
    • 13.2 Regular expressions to find more flexible patterns
    • 13.3 Substitute or remove matching patterns with gsub
    • 13.4 Predefined variables to use in regular expressions:
    • 13.5 Use grep and regular expressions to retrieve columns by their names
    • 13.6 Exercise 8: Regular expressions
  • 14 Repetitive execution
    • 14.1 Exercise 9: For loop
  • 15 Conditional statement
    • 15.1 Exercise 10: If statement
  • 16 Basic plots in R
    • 16.1 Scatter plots
    • 16.2 Bar plots
    • 16.3 Pie charts
    • 16.4 Box plots
    • 16.5 Histograms
  • 17 How to save plots
    • 17.1 With R Studio
    • 17.2 With the console
    • 17.3 Exercise 11: Base plots
      • 17.3.1 Exercise 11a- scatter plot
      • 17.3.2 Exercise 11b- bar plot + pie chart
      • 17.3.3 Exercise 11c- histogram
  • 18 Plots from other packages
    • 18.1 heatmap.2 function from gplots package
    • 18.2 venn.diagram function from VennDiagram package
  • 19 ggplot2 package
    • 19.1 Getting started
    • 19.2 Scatter plot
    • 19.3 Box plots
    • 19.4 Dot plots
    • 19.5 Bar plots
    • 19.6 Histograms
    • 19.7 About themes
    • 19.8 Saving plots in files
    • 19.9 Exercise 12: ggplot2
      • 19.9.1 Exercise 12a- Scatter plot
      • 19.9.2 Exercise 12b- Box plot
      • 19.9.3 Exercise 12c- Bar plot
      • 19.9.4 Exercise 12d- Histogram
    • 19.10 More about the theme() function
    • 19.11 Volcano plots
  • 20 Introduction to Exploratory Data Analysis & Descriptive Statistics."
    • 20.1 Exploratory Data Analysis
      • 20.1.1 Data dimentionality: functions str(), summary(), head(), tail()
      • 20.1.2 Missing (NA) values in data: functions complete.cases(), na.omit(), all.equal()
      • 20.1.3 Looking at the subset of data
      • 20.1.4 Excercises on data subsetting and missing values
      • 20.1.5 Exploring a particular variable (column): functions unique(), table()
      • 20.1.6 Exploring relationships between variables: functions table(), cut(), and functions for factors levels(), nlevels()
      • 20.1.7 Excercises using unique(), table() and cut()
    • 20.2 Descriptive Statistics
      • 20.2.1 Functions: mean, sd, var, min, max, median, range, IQR, quantile.
      • 20.2.2 Functions tapply() and round()
      • 20.2.3 Excercises
      • 20.2.4 Data visualization: boxplot()
      • 20.2.5 Outliers
      • 20.2.6 How statistics change if to remove outliers
    • 20.3 More basic plots
      • 20.3.1 How to plot box-plots side-by-side on one graph
      • 20.3.2 Excercise: Make a similar boxplot using ggplot()
      • 20.3.3 How to plot together data from two or more vectors of different lengths
      • 20.3.4 How to make a box-plot more informative and customized
      • 20.3.5 Histogram
      • 20.3.6 Control for the size of bins
      • 20.3.7 Two overlaying histograms on one graph
      • 20.3.8 Scatterplot
      • 20.3.9 Function palette()
    • 20.4 Wrapping up everything you have learned in this course
      • 20.4.1 Read data from the file
      • 20.4.2 Remove empty columns
      • 20.4.3 Explore, rename and clean variables
      • 20.4.4 Correct non-numeric values, changing them to NA
      • 20.4.5 Write corrected data frame in the file
      • 20.4.6 Explore and remove outliers
      • 20.4.7 How to change a value of a specific data point
      • 20.4.8 Make a data frame with statistical data
      • 20.4.9 Format a data frame
      • 20.4.10 Write a data frame in the file
      • 20.4.11 Write a table using as a decimal separator instead of a dot a comma
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Introduction to R

12.3 Install a package

  • With RStudio:

  • From the console:

install.packages(pkgs="ggplot2")
  • Install a bioconductor package:
    • For R version >= 3.5.0
# Install Bioconductor package manager
install.packages(pkgs="BiocManager")
# Install Bioconductor package
BiocManager::install("DESeq2")
+ For older R versions
# Source (load into environment) script containing biocLite function
source("http://www.bioconductor.org/biocLite.R")
# Use biocLite function to install Bioconductor package
biocLite("DESeq2")