R-Markdown (June 10)
R-Markdown lets you write reports, papers, web pages and slides from Rstudio while combining R-code, R-output, text, figures, tables… In this session we cover
- The Markdown syntax
- Writing R-chunks, controlling their behaviour
- Control of the layout and output options with the YAML
- Writing a technical report output as a HTML or Word document
Git and Github (June 17)
Git is a powerful version control (~ “track change” but much more powerful!) that lets you record all changes made to a project, go back to a previous state of your work, share your work and collaborate with others.
Making R-packages (June 24)
In this session we build a little package from scratch, with time-series temperature data and function to analyse them. We practice the cycle of writing code, generating documentation, running checks and installing the package. We then post a public version of the package on Github so that anyone can install the package.
Workflow Management and Snakemake (July 1)
Tomorrow morning I'll be giving a lesson on workspace and workflow management for bioinformatics. Despite the boring title, this is a true computational biology superpower: instead of a disorganised mess of scripts and strangely named data files, you'll have a tidy, organised, shareable, and reproducible workspace for each project. We cover:
- Workspace organisation and workflows
- snakemake: each step is a rule
- snakemake: rule graphs
- snakemake: a toy bioinformatic workflow
Snakemake Part 2 (July 8)
We'll finish the last little bit of last week's content on workflow management basics, and then cover some more advanced and extremely useful features of the snakemake ecosystem:
- Snakemake: config files and metadata
- Snakemake: automatic interaction with clusters and queuing systems
- Versioning workspaces with git
- Managing installation and versioning of software per-workspace with conda environments
Making Maps in R with ggplot (July 15)
This workshop will be a practical introduction to the basics of making maps in R. Maps can be complex and often seem annoying to work with, but with a little familiarity with R you can create useful and attractive maps quickly and easily. We will focus on some common use cases and not assume anything beyond ggplot2 basics covered previously in this series.