Introduction to R - Part III Systems Biology

This is a general introduction to R for data analysis.

Our practicals will be very hands-on, focusing on learning the necessary syntax to allow you to do data analysis in R, from data manipulation to visualisation. We will focus on tabular data, which is general enough to allow you to apply these skills to a wide range of problems.

If you have any queries please contact Hugo Tavares (hugo.tavares@slcu.cam.ac.uk) or post an issue on our GitHub repository.

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Setup

All necessary software and data will be available on the training machines at the Bioinformatics Training Room (Craik-Marshall Building).

However, you are welcome to use your own laptop, in which case you need to:

• Download and install R (here)
• Download and install RStudio (here)
• Open RStudio and go to Tools > Install Packages and paste this into the “Packages” field: tidyverse

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INT1.23 / INT1.26 / INT1.30

These lessons will cover the basics of using R with RStudio.

• Introduction to R/tidyverse for Data Analysis

INT1.33

• Data Structures, Functions and Loops

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Further resources

• Extra materials: Exploratory analysis of gene expression data (these materials illustrate many of the functions we covered in this course, using a different dataset relevant for bioinformatic applications)
• One page summary of functions
• Summary of dplyr functions and their equivalent in base R
• Cheatsheets for dplyr, ggplot2 and more
  • dplyr cheatsheet
  • ggplot2 cheatsheet
• Data-to-Viz website with great tips for choosing the right graphs for your data (along with R/Python code)

Reference Books

• Holmes S, Huber W, Modern Statistics for Modern Biology - covers many aspects of data analysis relevant for biology/bioinformatics from statistical modelling to image analysis.
• Peng R, Exploratory Data Analysis with R - an more general introduction to exploratory data analysis techniques.
• Grolemund G & Wickham H, R for Data Science - a good follow up from this course if you want to learn more about tidyverse packages.
• Wickham H, Advanced R - more advanced topics in how R works internally, how object-oriented programming works, etc.
• McElreath R, Statistical Rethinking - an introduction to statistical modelling and inference using R (a more advanced topic, but written in an accessible way to non-statisticians). Also see the lecture materials.
• Barabási A, Network Science - an introduction to network analysis (it’s a general book, not focused on R. But see the igraph package).
• James G, Witten D, Hastie T & Tibshirani R, Introduction to Statistical Learning - an introductory book about machine learning using R (also advanced topic).
  • Also see this course material for a practical introduction to this topic.
• Lovelace R, Nowosad J, Muenchow J, Geocomputation with R - using R for visualisation and analysis of spatial data.

Other Languages and Packages

• For fast data frame manipulation and calculations, especially for very large data (millions of observations) see the data.table package.
• For bioinformatic applications, see the Bioconductor repository - these packages typically have excellent documentation in the form of vignetes. See these materials for an overview of the Bioconductor project.
• For data analysis in Python:
  • Some of the equivalent functionality of base R to manipulate data can be achieved in python using a combination of these packages: pandas (data frames), matplotlib (plotting), numpy (matrix/arrays)
  • For a ggplot2-like package see plotnine
  • For a dplyr-like package see siuba
  • For high-performant data frame operations see datatable

Other Topics

• Version Control with Git (Software Carpentry course)
• The Unix Shell

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Acknowledgements

Many of these materials were inspired/developed by the Data Carpentry organisation.