• R tutorials for the course BIOS1140
  • Preface
    • How to use these tutorials
  • I Introductory R course
  • 1 Introduction to R
    • What to expect
    • 1.1 Start using R!
      • 1.1.1 Install R and Rstudio
      • 1.1.2 Getting familiar with R and the console
      • 1.1.3 Scripts
    • 1.2 R essentials
      • 1.2.1 Assigning to objects
      • 1.2.2 Vectors
      • 1.2.3 Strings
      • 1.2.4 Logical values
      • 1.2.5 Functions
      • 1.2.6 Data frames
    • 1.3 Data import
      • 1.3.1 Data formats
      • 1.3.2 Working directories
      • 1.3.3 Importing
    • 1.4 Plotting
      • 1.4.1 Plotting vectors
      • 1.4.2 Customizing your plots
    • 1.5 Installing and loading packages
    • 1.6 Going further
  • 2 Building on your foundations: going further with R
    • What to expect
    • 2.1 Intro to data manipulation with tidyverse
      • 2.1.1 What is the tidyverse?
      • 2.1.2 The dplyr package
    • 2.2 Using dplyr to work with your data
      • 2.2.1 The pipe
      • 2.2.2 Selecting columns with select()
      • 2.2.3 Filtering colums using filter()
      • 2.2.4 Grouped summaries with group_by() and summarise()
      • 2.2.5 Using everything we’ve learned in a single pipe, and a dplyr exercise
    • 2.3 Plotting your data with ggplot2
      • 2.3.1 The three things you need in a ggplot
      • 2.3.2 Storing ggplots in objects
      • 2.3.3 Customizing your plots
      • 2.3.4 Saving your plots
    • 2.4 Reshaping data with pivot_longer()
      • 2.4.1 Wide and long format
      • 2.4.2 Import example data
      • 2.4.3 Reshape the data
      • 2.4.4 Plot the data
    • 2.5 Study questions
    • 2.6 Going further
  • II Evolutionary genetics with R
  • 3 Changes in Allele and Genotype Frequency
    • What to expect
    • 3.1 R programming: for-loops
      • 3.1.1 Motivation: why loop?
      • 3.1.2 How a for-loop works
      • 3.1.3 Indexing with for-loops
      • 3.1.4 Solving our problem
      • 3.1.5 Storing values from a for-loop
    • 3.2 Evolutionary biology
      • 3.2.1 The Hardy-Weinberg Model
      • 3.2.2 Testing for deviations from the Hardy Weinberg Expectation
      • 3.2.3 Simulating genetic drift
    • 3.3 Study questions
    • 3.4 Going further
  • 4 The Theory of Natural Selection
    • What to expect
    • Getting started
    • 4.1 R programming: Making custom functions
      • 4.1.1 Motivation
      • 4.1.2 Function basics
      • 4.1.3 A simple example
      • 4.1.4 Some important function properties
      • 4.1.5 A slightly more useful example
      • 4.1.6 Example: calculating genotype frequencies
      • 4.1.7 Creating a function of the drift simulation
    • 4.2 Evolutionary biology: fitness
      • 4.2.1 Understanding fitness
      • 4.2.2 One-locus model of viability selection
      • 4.2.3 Over and underdominance
    • 4.3 Study questions
    • 4.4 Going further
  • 5 Detecting Natural Selection
    • What to expect
    • Getting started
    • 5.1 More on functions: Vectorisation
      • 5.1.1 The apply() function
      • 5.1.2 The ifelse() function
    • 5.2 Understanding FST - the fixation index
      • 5.2.1 A worked example of FST in humans
      • 5.2.2 Writing a set of FST functions
      • 5.2.3 Applying functions to matrices and data frames
    • 5.3 Visualising FST along a chromosome
      • 5.3.1 Identifying outliers in our FST distribution
    • 5.4 Study questions
    • 5.5 Going further
  • 6 Inferring Evolutionary Processes from Sequence Data
    • What to expect
    • Getting started
    • 6.1 Working with DNA sequence data
      • 6.1.1 Reading sequence data into R.
      • 6.1.2 Exploring DNA sequence data
      • 6.1.3 Calculating basic sequence statistics
    • 6.2 Working with a larger dataset
      • 6.2.1 Sample size and sequence statistics
      • 6.2.2 Inferring evolutionary processes using Tajima’s D
    • 6.3 Calculating statistics at the whole genome level
      • 6.3.1 Reading in variant data
      • 6.3.2 Calculating nucleotide diversity statistics
      • 6.3.3 Visualising nucleotide diversity along the chromosome
      • 6.3.4 Performing a sliding window analysis
    • 6.4 Study questions
    • 6.5 Going further
  • 7 Speciation Genomics
    • What to expect
    • Getting started
    • 7.1 Visualizing complex data
      • 7.1.1 Faceting
    • 7.2 Returning to the sparrow dataset
      • 7.2.1 Reading in the sparrow vcf
      • 7.2.2 Examining the variant data
    • 7.3 Setting up sliding windows
      • 7.3.1 Calculating sliding window estimates of nucleotide diversity and differentiation
      • 7.3.2 Extracting statistics for visualisation
    • 7.4 Visualising the data
      • 7.4.1 Visualising patterns along the chromosome
      • 7.4.2 Interlude: relative vs. absolute measures of nucleotide diversity
      • 7.4.3 Investigating recombination rate variation
    • 7.5 Study questions
    • 7.6 Going further
  • 8 Reconstructing the Past
    • What to expect
    • Getting started
    • 8.1 Phylogenetics in R
      • 8.1.1 Storing trees in R
      • 8.1.2 Plotting trees
      • 8.1.3 A simple example with real data - avian phylogenetics
      • 8.1.4 Constructing trees with R
    • 8.2 Population structure
      • 8.2.1 Village dogs as an insight to dog domestication
      • 8.2.2 Reading the data into R
      • 8.2.3 Performing a PCA
      • 8.2.4 Visualising the PCA
      • 8.2.5 Eigenvalues
      • 8.2.6 The full data set
    • 8.3 Study questions
    • 8.4 Going further
  • 9 Advancing Further in R
    • What to expect
    • Getting started
    • 9.1 Advanced features of RStudio
      • 9.1.1 Projects, projects, projects
      • 9.1.2 Everyone has a history
      • 9.1.3 Tab complete and other hotkeys
    • 9.2 More on data handling: categorising and joining
      • 9.2.1 ifelse() for making categories
      • 9.2.2 Joining
    • 9.3 Lists
    • 9.4 Vectorisation
      • 9.4.1 Using lapply() to vectorise
      • 9.4.2 sapply()
      • 9.4.3 Anonymous functions
    • 9.5 Concluding remarks
    • 9.6 Study questions
    • 9.7 Going further
  • III Assignments
  • Week 1-2 assignment 1
  • Week 3 Assignment 2
  • Week 4 assignment 3
  • Week 5 assignment 4
  • Week 6-7 assignment 5
  • Week 8 assignment 6
  • Week 9 assignment 7
  • Appendix
  • A About assignments and RMarkdown
    • R Markdown
    • Getting started
  • B Technical information
  • Made with bookdown

R tutorials for the course BIOS1140 at the University of Oslo

2.6 Going further

As normal, R has a huge range of freely available resources online that can help you learn more about data manipulation, the tidyverse, ggplot2 and scripting. Here we point you to a few below that you might be interested in.

  • Datacamp has an free introduction to using tidyverse packages to manipulate data
  • Hadley Wickham & Garrett Grolemund have written the definitive, freely available online book on using R for data manipulation - this is the ‘bible’ of the tidyverse approach and includes a section on ggplot2
  • There is also a Datacamp course on ggpot2
  • Winston Chang’s R Graphic’s Cookbook is also an excellent resource for using ggplot2 for data visualisation
  • A detailed software carpentry guide to R scripting