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Curriculum for Data Challenge Lab (2019-04)
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Data Challenge Lab

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This repository contains the draft curriculum for the Stanford Data Challenge Lab.

The curriculum is composed of small units and bigger challenges. A unit is a set of guided readings and a handful of low-level exercises (in an .Rmd) designed to test that you've understood the material. Challenges are integrative tasks that may take several hours to complete and are contextualised with real data problems. (In terms of Bloom's taxonomy, exercises tend be remember/understand/apply, and challenges are analyse/evaluate/create.)

The units and challenges will be composed in several ways. So far, we have focused on a curriculum, a temporal ordering of unit designed to lay out the material into a 5-credit, 10-week course. Challenges are currently private; you must be enrolled in the course to see them.



There are two key elements to the structure of the site: syllabus.yml and the units directory.


syllabus.yml has two elements:

  • cur-week, the current week of the course, used to control what units are shown on the index page. This will need to be updated once per week.

  • weeks, a list of the unit names, which defines the structure of the course.


The units/ directory contains the information each individual unit. A unit can either be a .yml file or a .Rmd file. Use a .yml file if the unit links primarily to other readings; use .Rmd if the reading contains new content or you want to use R code. A .yml is created for .Rmd, so make sure you are editing the right file, or your changes will be overwritten the next time you modify the site.

Both .Rmd and .yml have three key metadata elements:

  • title: a brief human-readable description of the unit

  • theme: the major them to which the unit belongs

  • needs: an array of units which should be taken prior to this unit. This is used to generate the overview graph, and cross-links between units.

title: Data basics
theme: explore
needs: [setup]

.yml units also require a description. This should be written in markdown and I recommend using the yaml | formatting helper which will preserve whitespace in the output file.

desc: |
  Exploratory data analysis is partly a set of techniques, but is mostly a
  mindset: you want to remain open to what the data is telling you.

(The .yml files generated from .Rmd files will have a desc field that contains the result of rendering the .Rmd)

Both .yml and .Rmd files can include readings. These come in two basics forms: either a reading from one of the predefined book, or a link to a website. You can optionally include the author name for websites, and either form can have an additional description. A few examples are listed below:

- book: r4ds-7.5

- href:
  text: Parameterized reports

- href:
  text: What is a reprex?
  author: Jenny Bryan
  desc: Some general advice on writing reprexes

- book: r4ds-12.5
  desc: >
    (explicit vs implicit).
    We haven't covered the vocabulary of "tidy data" yet, but be aware that
    different ways of organisation the same data may make explicit missing
    values that were previously implicit in the data.

Build process

If you are using RStudio, press Cmd + Shift + B to rebuild the site. Alternatively, execute scripts/build.R by hand. To make the build process as speedy as possible, files are only updated if the input file is more recent than the output file.

The repository is structured as a package, so you can use devtools::load_all() to make all functions available for interactive use. The one function that you may want to use (and isn't exposed any other way) is clean(): it deletes all rendered documents from docs/ so you can rebuild the site from scratch.

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