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Effective Computing—Resources for Computational Biologists

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# Effective Computing for Computational Biologists*

Effective Computing is about improving your skills and adopting Best
Practices to help make you a better programmer, *and a better
scientist.* This is a curated [short
list](https://www.merriam-webster.com/dictionary/shortlist) of
resources to help you achieve these aims.

*We welcome community contributions.* If you have a suggestion or comment,
please post an
[Issue](https://github.com/stephenslab/effective-computing/issues).

## General

+ [Practical Computing for Biologists](http://practicalcomputing.org).

+ [Effective Computation in Physics](http://physics.codes).

+ [Intro to Scientific Computing for Biologists](http://allesinalab.uchicago.edu/wp-content/uploads/2014/05/IntroSciComp2014.pdf).

+ [Software Carpentry](http://software-carpentry.org/lessons).

+ [Data Carpentry](http://www.datacarpentry.org/lessons).

+ [The Missing Semester of Your CS Education](https://missing.csail.mit.edu).

+ [NCEAS "hacking skills" lessons](https://nceas.github.io/oss-2017).

+ [OHI "Better Science in Less Time" list of
resources](http://ohi-science.org/news/Resources-for-R-and-Data-Science).

## Organizing, documenting and sharing (including version control)

+ [A guide to reproducible code in ecology and
evolution](http://www.britishecologicalsociety.org/publications/guides-to):
skills and tips for making your code more reproducible. (See also
"A Guide to Data Management in Ecology and Evolution.")

+ [Github](http://github.com), [Bitbucket](http://bitbucket.org) and
[Gitlab](http://gitlab.com).

+ [An introduction to the basics of git and
Github](https://doi.org/10.1371/journal.pcbi.1004668).

+ [Pro Git](https://git-scm.com/book).

+ [Git Extras](https://github.com/tj/git-extras).

+ [Markdown](http://daringfireball.net/projects/markdown/syntax).

+ [R Markdown](http://rmarkdown.rstudio.com).

+ [Jupyter notebooks](https://jupyter.org). See
[here](https://github.com/jupyter/jupyter/wiki/Jupyter-kernels) for
the list of >40 programming languges supported by Jupyter notebooks.

+ [workflowr R package](https://github.com/jdblischak/workflowr).

+ Embedding math in webpages: [MathJax](https://math.meta.stackexchange.com/questions/5020/mathjax-basic-tutorial-and-quick-reference).

+ Creating a "permanent resource" for your project:
[Zenodo](http://zenodo.org); [Data Dryad](http://datadryad.org).

+ [A short tutorial](http://kbroman.org/simple_site) by Karl Broman on
creating a website from Markdown using
[Github Pages](https://pages.github.com). See also [this short
tutorial](http://jmcglone.com/notes/2014/05/03/using-github-to-create-and-host-a-personal-website)
on creating a website using Github and Jekyll.

## Programming and software design

+ [Software development is inherently
wicked.](https://blog.codinghorror.com/development-is-inherently-wicked)

+ [Rubber duck debugging](https://en.wikipedia.org/wiki/Rubber_duck_debugging)

+ [The Practical Programmer](https://en.wikipedia.org/wiki/The_Pragmatic_Programmer), a book giving practical tips for software development.

+ [Jeff Leek's guide to developing R
packages](https://github.com/jtleek/rpackages) (cross-listed below).

## Plots and data visualization

+ [D3](https://github.com/d3/d3) ("Data-Driven Documents").

+ It is important to [choose colors wisely](R-color.md) so that they are
visually distinguishable by most people.

## R

+ [CRAN](http://cran.r-project.org).

+ [BioConductor](http://bioconductor.org).

+ [RStudio](http://rstudio.com).

+ [Efficient R Programming](https://csgillespie.github.io/efficientR),
see in particular [Efficient set-up](https://csgillespie.github.io/efficientR/set-up.html).

+ [Software for Data Analysis: Programming
with R](http://dx.doi.org/10.1007/978-0-387-75936-4) by John Chambers.

+ [R for Data Science](http://r4ds.had.co.nz).

+ [Some advanced topics in R](http://adv-r.had.co.nz).

+ [Unofficial Rcpp documentation](https://thecoatlessprofessor.com/programming/unofficial-rcpp-api-documentation): a reference for [Rcpp](https://cran.r-project.org/package=Rcpp) all in one place.

+ [Short guide to writing your own R package](http://r-pkgs.had.co.nz).

+ [Jeff Leek's guide to developing R
packages](https://github.com/jtleek/rpackages) (cross-listed above).

+ [Tutorials](https://github.com/lgatto/TeachingMaterial) on a variety
of topics in R, from basic to advanced.

+ [Google's R programming style guide](https://google.github.io/styleguide/Rguide.xml) aimed at making R code easier to read, share and verify.

+ [Introductory Statistics with R](http://staff.pubhealth.ku.dk/~pd/ISwR.html).

+ [Data Analysis for the Life
Sciences](https://leanpub.com/dataanalysisforthelifesciences) is available
or free and all the examples and data anlyses are done in R. See 
[here](https://genomicsclass.github.io/book) for the R Markdown source
in the book.

+ [STAT Labs: Mathematical Statistics through
Applications](https://www.stat.berkeley.edu/~statlabs) uses R in its
data analysis examples.

+ [Generalized Linear Models course at Princeton University](http://data.princeton.edu/wws509/R) with detailed illustrations of using
  generalized linear models in R.

+ [R tutorials](http://tutorials.iq.harvard.edu) available from the
  Institute For Quantitative Social Science at Harvard.

## Python

+ [Python tutorials](http://tutorials.iq.harvard.edu) available from
  the Institute For Quantitative Social Science at Harvard.

## Julia

+ [Fast Tack to Julia](https://juliadocs.github.io/Julia-Cheat-Sheet):
  a quick and dirty overview of Julia.

+ [The Julia Express](https://github.com/bkamins/The-Julia-Express): a
  concise Julia language introductory manual for programmers.

+ [Julia Manual](https://docs.julialang.org): the official
  documentation for the Julia Language.

## Containers and package managers

+ [Anaconda](https://www.continuum.io).

+ [Explanation](https://bioconda.github.io/faqs.html#conda-anaconda-minconda)
of the difference between Anaconda, conda and Miniconda.

## Linux, the UNIX shell, and bash scripts

+ [Well-maintained Linux guides](http://tldp.org/guides.html), including the
[Advanced Bash-scripting guide](http://tldp.org/LDP/abs/html).
Appendix B of the Advanced Bash-scripting guide is a
[useful reference card](http://tldp.org/LDP/abs/html/refcards.html).

+ [Julia Evans' Bash scripting quirks & safety
tips](https://jvns.ca/blog/2017/03/26/bash-quirks): an accessible
primer on writing bash scripts.

+ [explainshell.com](https://explainshell.com): resource for
understanding shell scripts.

+ [Bash scripting cheat
sheet](http://johnstowers.co.nz/pages/bash-cheat-sheet.html).

+ [ShellCheck](http://www.shellcheck.net) find bugs in your shell
scripts.

+ [TLDR pages](http://tldr.sh): shell commands reference by example.

## macOS (previously Mac OS X)

+ [Mac OS X Setup Guide](http://sourabhbajaj.com/mac-setup) for
development. [More setup
advice](https://github.com/nicolashery/mac-dev-setup).

+ Package managers: [Homebrew](https://brew.sh),
[MacPorts](https://www.macports.org).

## Development tools and environments

+ Text editors:
[emacs](https://www.gnu.org/software/emacs),
[vim](http://www.vim.org),
[Sublime](https://www.sublimetext.com), 
[Atom](https://atom.io).

## Bioinformatics and computational biology resources

+ [Bioconda](https://bioconda.github.io).

## Best Practices

+ [Good Enough Practices in Scientific
Computing](https://doi.org/10.1371/journal.pcbi.1005510).

+ [Best Practices for Scientific
Computing](https://doi.org/10.1371/journal.pbio.1001745).

+ Some more recommended practices:
[Noble, 2009](https://doi.org/10.1371/journal.pcbi.1000424).

+ [10 simple rules for making research software more
robust](https://doi.org/10.1371/journal.pcbi.1005412).

+ [GNU Coding Standards](https://www.gnu.org/prep/standards).

+ Licenses for open source projects:
[choosing a license](https://github.com/github/choosealicense.com);
[how to add a license to a project](https://github.com/github/choosealicense.com/issues/243); 
[licensing data](https://wiki.creativecommons.org/wiki/Data#Which_components_of_databases_are_protected_by_copyright.3F).

+ [Making your code citable](https://guides.github.com/activities/citable-code/).

## Advocacy

+ Ince *et al* (2012) [The case for open computer
programs](https://doi.org/10.1038/nature10836).

+ Morin *et al* (2012) [Shining light into black
boxes](https://doi.org/10.1126/science.1218263).

+ R. D. Peng (2011) [Reproducible Research in Computational
Science](https://doi.org/10.1126/science.1213847).

+ [The state of Jupyter](https://www.oreilly.com/ideas/the-state-of-jupyter)
by Fernando Perez and Brian Granger: How Project Jupyter got here and
where we are headed.

## License

[![CC BY 4.0](https://i.creativecommons.org/l/by/4.0/88x31.png)](http://creativecommons.org/licenses/by/4.0/)

This work is licensed under a [Creative Commons Attribution 4.0
International License](http://creativecommons.org/licenses/by/4.0/).

You must give appropriate attribution: mention that your work is
derived from work that is Copyright (c) Peter Carbonetto and, where
practical, linking to
[this Github repository](https://github.com/pcarbo/effective-computing);
provide a link to the
[license](http://creativecommons.org/licenses/by/4.0/); and indicate
if changes were made. You may do so in any reasonable manner, but not
in any way that suggests the licensor endorses you or your use.

## Credits

People who have contributed to these materials: John Blischak, Peter
Carbonetto, Matthew Stephens.

*Title adapted from [Effective Computation in Physics](http://physics.codes).

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