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materials to study and learn about principled data processing

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# training-docs
This repo contains materials to study and learn about principled data processing, including:
- `onboarding` materials to help you get setup with the right tools and procedures
- `demo-tasks` to walk-through how to use some of those tools in practice
- `templates` to serve as outlines for routine files like Makefiles and scripts in python or R
- `checklists` to refer to as you work and contribute to projects (updating a repo, writing a script, adding a new task)
- `languages` and language-specific tips to consider when writing scripts (like scalability, missingness)
- `notebooks` to walk-through various topics in context of a specific language (ie. set operations in python)

## helpful repos
There are a few repos outside of this one that house various tools and/or guidance that may be useful.

- [sample-project](https://github.com/baileyb0t/sample_project)
    - This is a dummy repo to test out git functionality like cloning, pushing, and pull requesting
- [resource-utils/faqs](https://github.com/HRDAG/resource-utils/tree/master/faqs)
    - There's a few help articles related to HRDAG workflow, in particular:
        1. `data-hacking-on-server.md` includes instructions for making ssh keys and running Jupyter notebooks
        2. `safe-logout.txt` instructions for safely disconnecting from eleanor and notebooks 
        3. `data-work-faq.txt` questions we've asked ourselves enough to write down for others
- [resource-utils/notes](https://github.com/HRDAG/resource-utils/tree/master/notes)
    - There's a useful document from a previous intern
        1. `internship_notes_2016.md` includes some walk-throughs, suggested tools, frequently used commands
- [gnutools](https://github.com/tarakc02/gnutools/)
    - A useful guide to using GNU tools more effectively with examples
- [record-hash-comparisons](https://github.com/tarakc02/record-hash-comparisons)
    - An introduction and overview of creating unique identifiers with hashes
- [form-extraction](https://github.com/HRDAG/form-extraction)
    - A place for some common tools and code we use to extract info from different kinds of forms
- [tool-suite](https://github.com/baileyb0t/tool-suite)
    - A home for some tools related to performance improvements and benchmarking
- [dotfiles](https://github.com/tarakc02/dotfiles)
    - An example all kinds of dotfiles you might want to explore and use in your working environment, like `vimrc`, `bash_profile`, `zshrc`, and `gitconfig`

## more helpful topics
on vim:
- [Vim Cheat Sheet](https://vim.rtorr.com/)
- [Vim screencast library](http://vimcasts.org/)
- [**Interactive:** Vim adventures!](https://vim-adventures.com/)

on git:
- [Collaboration with version control](https://datasciencebook.ca/Getting-started-with-version-control.html)
- [Version control for transparency and collaboration](https://ubc-dsci.github.io/reproducible-and-trustworthy-workflows-for-data-science/materials/lectures/02-version-control-1.html)
- [**Interactive:** Learn Git Branching](https://learngitbranching.js.org/?locale=en_US)

on workflow:
- [Filenames and data science project organization, Integrated development environments](https://ubc-dsci.github.io/reproducible-and-trustworthy-workflows-for-data-science/materials/lectures/03-filenames-project-organization.html)
- [Introduction to testing code for data science](https://ubc-dsci.github.io/reproducible-and-trustworthy-workflows-for-data-science/materials/lectures/06-intro-to-testing-code.html)
- [Non-interactive scripts](https://ubc-dsci.github.io/reproducible-and-trustworthy-workflows-for-data-science/materials/lectures/07-scripts.html)
- [data analysis pipelines](https://ubc-dsci.github.io/reproducible-and-trustworthy-workflows-for-data-science/materials/lectures/09-pipelines.html)
- [Reproducible reports](https://ubc-dsci.github.io/reproducible-and-trustworthy-workflows-for-data-science/materials/lectures/08-reproducible-reports.html)
- [Automated testing and continuous integration](https://ubc-dsci.github.io/reproducible-and-trustworthy-workflows-for-data-science/materials/lectures/11-continuous-integration.html)

## other suggested reading
- [Using Statistics to Assess Lethal Violence in Civil and Inter-State War](https://www.annualreviews.org/doi/pdf/10.1146/annurev-statistics-030718-105222#article-denial) (ask Megan for the PDF if you can't download this one for free!)
- [Processing scanned documents for investigations of police violence](https://hrdag.org/tech-notes/processing-scanned-docs-for-investigation-police-violence.html)

## books
- [R for Data Science](https://r4ds.had.co.nz/)
- [Advanced R](https://adv-r.hadley.nz/)
- [R markdown: The definitive guide](https://bookdown.org/yihui/rmarkdown/)
- [Practical Vim](https://bookarchive.net/pdf/practical-vim/)
- [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/)
- [Data Science: A First Introduction](https://datasciencebook.ca/)
- [Reproducible and Trustworthy Workflows for Data Science](https://ubc-dsci.github.io/reproducible-and-trustworthy-workflows-for-data-science/README.html)

# done.

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