DataScienceTutorials.jl

Latest CI build: with

[336ed68f] CSV v0.6.2
[a93c6f00] DataFrames v0.21.2
[add582a8] MLJ v0.11.4
[a7f614a8] MLJBase v0.13.10
[d491faf4] MLJModels v0.10.0

This repository contains the source code for a set of tutorials introducing the use of Julia and Julia packages such as MLJ (but not only) to do "data science" in Julia.

For readers

You can read the tutorials online.

If you want to experiment on the side and make sure you have an identical environment to the one used to generate those tutorials, please activate and instantiate the environment using this Project.toml file and this Manifest.toml file.

To do so, save both files in an appropriate folder, start Julia, cd to the folder and

using Pkg
Pkg.activate(".")
Pkg.instantiate()

Each tutorial has a link at the top for a notebook or the raw script which you can download by right-clicking on the link and selecting "Save file as...".

Note: you are strongly encouraged to open issues on this repository indicating points that are unclear or could be better explained, help us have great tutorials!

For developers

The rest of these instructions assume that you've cloned the package and have cd to it.

Important first steps

Start by running this line in your REPL (always):

julia> include("start.jl")

When it's time to push updates, only use include("deploy.jl") (assuming you have admin rights) as this also re-generates notebooks and scripts and pushes everything at the right place (see this point).

Note: keep your tutorials short! if there's a tuning step at some point, do it on a high resolution search locally and only show a rough search in the right area in the tutorial otherwise running tutorials can take a long time.

Modifying literate scripts

add packages if you need additional ones (] add ...), make sure to update explicit compat requirements in the Project.toml file
add/modify tutorials in the _literate/ folder
to help display things neatly on the webpage (no horizontal overflow), prefer pprint from PrettyPrinting.jl to display things like NamedTuple
add ; at the end of final lines of code blocks if you want to suppress output

Adding a page

Say you've added a new script A-my-tutorial.jl, follow these steps to add a corresponding page on the website:

copy one of the markdown file available in getting-started and paste it somewhere appropriate e.g.: getting-started/my-tutorial.md
modify the title on that page, # My tutorial
modify the \tutorial command to \tutorial{A-my-tutorial} (no extensions)

By now you have at getting-started/my-tutorial.md

@def hascode = true
@def showall = true

# My tutorial

\tutorial{A-my-tutorial}

The last thing to do is to add a link to the page in _layout/head.html so that it can be navigated to, copy paste the appropriate list item modifying the names so for instance:

<li class="pure-menu-item {{ispage /getting-started/my-tutorial/index.html}}pure-menu-selected{{end}}"><a href="/getting-started/my-tutorial/index.html" class="pure-menu-link">⊳ My tutorial</a></li>

Visualise modifications locally

cd("path/to/DataScienceTutorials")
using Franklin
serve()

If you have changed the code of some of the literate scripts, Franklin will need to re-evaluate some of the code which may take some time, progress is indicated in the REPL.

If you decide to change some of the code while serve() is running, this is fine, Franklin will detect it and trigger an update of the relevant web pages (after evaluating the new code).

Notes:

avoid modifying the literate file, killing the Julia session, then calling serve() that sequence can cause weird issues where Julia will complain about the age of the world...
the serve() command above activates the environment.

Plots

For the moment, plots are done with PyPlot.jl (though you're not restricted to use it). It's best not to use Plots.jl because the loading time would risk making full updates of the webpage annoyingly slow.

In order to display a plot, finish a code block defining a plot with

savefig(joinpath(@OUTPUT, "MyTutorial-Fig1.svg")) # hide

# \figalt{the alt here}{MyTutorial-Fig1.svg}

Please do not use anything else than SVG; please also stick to this path and start the name of the file with the name of the tutorial (to help keep files organised).

Do not forget to add the # hide which will ensure the line is not displayed on the website, notebook, or script.

Troubleshooting

Stale files

It can happen that something went wrong and you'd like to force Franklin to re-evaluate everything to clear things up. To do this, head to the parent markdown file (e.g. my-tutorial.md) and add below the other ones:

@def reeval = true

save the file, wait for Franklin to complete its update and then remove it (otherwise it will reevaluate the script every single pass which can slow things down a lot).

If you get an "age of the world" error, the reeval steps above usually works as well.

If you want to force the reevaluation of everything once, restart a Julia session and use

serve(; eval_all=true)

note that this will take a while.

Merge conflicts

If you get merge conflicts, do

cleanpull()
serve()

the first command will remove all stale generated HTML which may conflict with older ones.

Push updates

Requirements:

admin access to the repo
] add Literate Franklin NodeJS
install highlight.js and gh-pages from within Julia: run(`sudo $(npm_cmd()) i highlight.js gh-pages`)

Assuming you have all that, just run

include("deploy.jl")

This should take ≤ 15 seconds to complete.

If you don't have some of the requirements, or if something failed, just open a PR.

Continuous Integration

To help maintain tutorials, most of them are tested on Travis. However tutorials that include plotting should not be included. Please adjust the file test/runtests.jl accordingly following the example.

adityasaini70 / DataScienceTutorials.jl