Books
R Programming
R for Graduate Students
Link to book
Written by a behavioral neuroscience HDR student, this beginner’s guide to R covers:
- Installation and set-up
- Keyboard shortcuts
- R vocabulary and naming conventions
- Basic data manipulation (
mutate,filter,summarize)
- An introduction to plotting with
ggplot
R for Data Science (2e)
Link to book
A comprehensive resource for learning R and the tidyverse. Topics include:
- Data cleaning, transformation, and visualisation
- Writing functions
- Creating reproducible workflows
R Graphics Cookbook
Link to book
Offers recipes for creating and customising visualisations using R, including ggplot2 and base R.
Features practical examples and data preparation techniques.
Advanced R
Link to book
Designed for intermediate users, it covers advanced topics such as:
- Environments and debugging
- Object-oriented programming
- Functional programming
R Packages (2e)
Link to book
A detailed guide to creating and maintaining R packages.
Includes topics like documentation and deployment.
Mastering Shiny
Link to book
Focused on building Shiny web applications in R, this book covers:
- Reactive programming
- App design and deployment
Statistics
R for Statistical Analysis
Link to book
Statistical course covering basic statistics and how to perform statistical analysis in R. Introduces basic statistics in R, including:
- an introduction to using R;
- performing exploratory data analysis;
- descriptive statistics and probability;
- Correlation and regression analysis;
- Inferential statistics and hypothesis testing.
Statistical Inference via Data Science
Link to book
Online book covering data science and statistics in R. Begins with data visualisation and wrangling. Covers several statistics topics including – linear regression, multiple regression, sampling, estimation and confidence intervals, and hypothesis testing.
JABSTB: Statistical Design and Analysis of Experiments with R
Link to book
Online book designed for undergraduate biostats students. Includes both R and statistical basics: sampling, data/variable classification, types of errors, p-values, data distributions, statistics for categorical variables, nonparametric statistical tests, t-tests, ANOVA, survival analysis and more. Final chapter (50) covers RNA-seq with edgeR/limma.
Introduction to bioinformatics workbook
Link to book Online workbook covering multiple topics related to bioinformatics. Includes videos, detailed explanations and worked examples with links to additional resources. Very extensive covering:
- Command line basics and useful programs (includes Unix basic, introduction to HPC and Bioawk)
- Project Management (Introduction to Slack, GitHub and Markdown)
- Introduction to BLAST
- Experimental Design
- Data acquisition and wrangling (including file transfer, FASTA manipulation; Manipulating Excel data sheets and NCBI SRA)
- Bioinformatics Terminology (Includes section on file formats)
- RNA Sequencing (including section on DGE with DESeq2 and single cell analysis)
- Genome assembly and Annotation (multiple sections and tools)
- Comparative genomics
- Variant Discovery (including FreeBays and GATK Best Practices for DNA-seq)
- Metagenomics
- Genome Repeat Identification
- ATAC-Sequencing
- Data visualisation
Other
Data Munging with R
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