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Workshop: Developing your data science portfolio

UC Davis DataLab
Winter 2023
Instructor: Michele Tobias <mmtobias@ucdavis.edu>
Maintainer: Michele Tobias <mmtobias@ucdavis.edu>

Workshop Description

A digital (online) portfolio complements you CV and other application materials on the job market or in grant applications and demonstrates your skills to potential collaborators and employers. In this workshop, we will discuss reasons you want to create a digital portfolio of your work, methods for creating it, and considerations for carefully curating and presenting your work in an engaging manner.

Learning Objectives

Participants will learn reasons to create a digital portfolio, methods for creating and curating their digtial portofolio, and see examples of digital portfolios for programmers, data scientists, and geospatial scientists.

Prerequisites

There are no prerequisites for this workshop. Recommended: Please have an updated copy of your CV or resume for reference and a list of recent projects or skills (if applicable). Participants should have access to a web browser (for example, Chrome or Firefox).

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 are R Markdown files (.Rmd) at the top level of the repo. Enter your text, code, and other information directly into the file. Make sure your file:

    • Follows the naming scheme ##_topic-of-chapter.Rmd (the only exception is index.Rmd, 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.
    • Uses caching for resource-intensive code (see Caching).

    Put any supporting resources in data/ or img/. For large files, see Large Files. You do not need to add resources generated by your R code (such as plots). The knit step saves these in docs/ automatically.

  4. Run knit.R to regenerate the HTML files in the docs/. You can do this in the shell with ./knit.R or in R with source("knit.R").

  5. Run renv::snapshot() in an R session at the top level of the repo to automatically add any packages your code uses to the project package library.

  6. When you're finished, git add:

    • Any files you added or edited directly, including in data/ and img/
    • docs/ (all of it)
    • _bookdown_files/ (contains the knitr cache)
    • renv.lock (contains the renv package list)
Then `git commit` and `git push`. The live web page will update
automatically after 1-10 minutes.

Caching

If one of your code chunks takes a lot of time or memory to run, consider caching the result, so the chunk won't run every time someone knits the reader. To cache a code chunk, add cache=TRUE in the chunk header. It's best practice to label cached chunks, like so:

```{r YOUR_CHUNK_NAME, cache=TRUE}
# Your code...
```

Cached files are stored in the _bookdown_files/ directory. If you ever want to clear the cache, you can delete this directory (or its subdirectories). The cache will be rebuilt the next time you knit the reader.

Beware that caching doesn't work with some packages, especially packages that use external libraries. Because of this, it's best to leave caching off for code chunks that are not resource-intensive.

Github Actions

GitHub Actions can be set up to automatically render your reader when you push new content to a repo. If you would like to use this function, download the materials in datalab-dev/utilities/render_bookdown_site and follow the instructions there.

Setup

R Packages

This repo uses renv for package management. Install renv according to the installation instructions on their website.

Then open an R session at the top level of the repo and run:

renv::restore()

This will download and install the correct versions of all the required packages to renv's package library. This is separate from your global R package library and will not interfere with other versions of packages you have installed.

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