Tutorials
Tutorials will provide a brief introduction to a range of data science tools, many of which we will continue to use regularly throughout the course modules. Tutorial sessions will be woven in between modules and are meant to fit within a single class session. While modules aim to help you master fundamental skills through a hands-on analysis over several weeks, tutorials are meant primarily to give you some context and a flavor for what is possible.
GitHub
Almost everything we do will involve GitHub in some way. This tutorial will provide a brief introduction to our GitHub-based workflow and review common git patterns for resolving issues you may encounter in working collaboratively.
Travis-CI
Continuous Integration (CI) is an important concept data science practice has largely adopted from software engineering, in which a suite of tests are run every time changes are committed (i.e. pushed to GitHub or other version control platform) to a project. We will use the popular Travis-CI platform to check for successful and reproducible rendering of RMarkdown notebooks in your project directories.
Our Travis-CI tutorial will provide more background on what Travis is an how it can be configured and used in a variety of settings.
R
R is a common statistical programming language used to analyze and visualize data. We will use it to introduce the idea of computing and some of the basics of computer programming. You are encouraged to use another language if you prefer for your final project, but assignments will be in R.
RMarkdown
Most of your work will take place inside an RMarkdown notebook. While we will rely primarily on the github_document format, RMarkdown supports a wide variety of output formats that you can use to create pdf documents (including particular journal formats) slideshows, websites, ebooks and interactive tutorials. In this tutorial, we will showcase these formats and introduce how RMarkdown output can be customized through yaml header blocks, knitr chunks, and templates. We will also introduce some of the technology behind RMarkdown formats, including pandoc, LaTeX, and HTML/CSS. See https://rmarkdown.rstudio.com.
R packages
Writing R packages isn’t just for software developers. The R packaging mechanism provides a powerful and very flexible mechanism for organizing research projects, a notion we refer to as the R “compendium” model. This tutorial will introduce you to the fundamentals of the R package structure and it’s ecosystem of tools.
Docker
Docker the engine behind the emerging Open Container standard for creating portable computational environments; important both for reproducibility of existing work and deploying analyses on larger compute systems (see Cloud Tutorial). This tutorial will introduce you to the basics of using Docker with the R environment.