Workshop: Julia Basics

UC Julia Users Group, UC Davis DataLab
Winter 2024
Authors: Derek Devnich (UCM), Ezra Morrison (UCD), Carl Stahmer (UCD), Nick Ulle (UCD)
Helpers: Neal Fultz (UCLA)
Maintainer: Nick Ulle <naulle@ucdavis.edu>

• Reader

⚠️ This workshop is still in development!

This workshop is an introduction to the Julia programming language for people familiar R, Python, or MATLAB. Compared to those languages, Julia code typically runs orders of magnitude faster but has a similar level of abstraction, so you can focus on your research problem rather than hardware minutiae. Julia also provides out-of-the-box Unicode support, an easy-to-use package manager, multithreading facilities, a macro system, and a rich type system to optimize and prevent bugs in your code. Workshop topics include a concise overview of Julia's syntax and features, an end-to-end introduction to using built-in functions and contributed packages to read, summarize, and visualize tabular data, real-world examples where we've found Julia beneficial.

After this workshop, learners will be able to describe Julia's strengths and weaknesses relative to other programming languages and get started using Julia in their own research projects.

Learners must already be proficient in a language like R or Python. All learners will need access to an internet-connected computer with the latest versions of Zoom and Julia.

Contributing

The course reader is a live webpage, hosted through GitHub, where you can enter curriculum content and post it to a public-facing site for learners.

To make alterations to the reader:

1. Check in with the reader's current maintainer and notify them about your intended changes. Maintainers might ask you to open an issue, use pull requests, tag your commits with versions, etc.

2. Run git pull, or if it's your first time contributing, see Setup.

3. Edit an existing chapter file or create a new one. Chapter files may be either Markdown files (.md) or Jupyter Notebook files (.ipynb). Either is fine, but you must remain consistent across the reader (i.e. don't mix and match filetypes). Put all chapter filess in the chapters/ directory. Enter your text, code, and other information directly into the file. Make sure your file:

   • Follows the naming scheme ##_topic-of-chapter.md/ipynb (the only exception is index.md/ipynb, which contains the reader's front page).
   • Begins with a first-level header (like # This). This will be the title of your chapter. Subsequent section headers should be second-level headers (like ## This) or below.

   Put any supporting resources in data/ or img/.

4. Run the command jupyter-book build . in a shell at the top level of the repo to regenerate the HTML files in the _build/.

5. When you're finished, git add:

   • Any files you edited directly
   • Any supporting media you added to img/

   Then git commit and git push. This updates the main branch of the repo, which contains source materials for the web page (but not the web page itself).

6. Run the command ghp-import -n -p -f _build/html in a shell at the top level of the repo to update the gh-pages branch of the repo. This uses the ghp-import Python package, which you will need to install first (pip install ghp-import). The live web page will update automatically after 1-10 minutes.

Setup

Python Packages

We recommend using mamba by way of Miniforge to manage Python dependencies. The env.yaml file in this repo contains a list of packages necessary to build the reader. After installing Miniforge, you can create a new environment with all of the packages listed in that file with this shell command:

mamba env create --file env.yaml

Julia Packages

To build the chapters with Julia code, you'll need Julia 1.9 and the IJulia package. You can install the package by running these commands in a Julia prompt:

using Pkg
Pkg.add("IJulia")