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It is helpful to study the data analysis code of experts. I've recently been perusing github and there are a number of people sharing data analysis code there. This includes a few R Packages (which of course are available directly from CRAN), but also several examples of reproducible research, particularly using R (see this R list on github).

  • Who are good people to follow on github to learn about best practice in data analysis?
  • Optionally, what kind of code do they share and why is this useful?
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Hadley Wickham. He has several exploratory data analysis projects on Github that you can look at (e.g., "data-baby-names"), and given the awesomeness of ggplot2/plyr/reshape, I have a default (but admittedly blind) trust in his best practices, particularly with respect to his own packages.

Plus, you get an early heads up on other projects he's working on!

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    $\begingroup$ (+1) He's also working on a set of tutorials on Advanced R development, very handy! $\endgroup$
    – chl
    Commented Nov 11, 2010 at 8:30
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    $\begingroup$ @Jeromy In fact, it seems this is merely a way to draft his future textbook (check HW's past tweets). $\endgroup$
    – chl
    Commented Nov 11, 2010 at 9:26
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I also follow John Myles White's GitHub repository. There are several data-oriented projects, but also interesting stuff for R developers:

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Diego Valle Jones. His Github, especially analysis of homicides in Mexico is really interesting.

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If you are dealing with clinical data (e.g., medical imaging, EMR, or physiologic monitoring data), you can follow Ramesh Sridharan (@rameshvs), Matteo Fumagalli (@mfumagalli), and José Ignacio Orlando (@ignaciorlando). They are great on that. Although you may be looking for something more broad in terms of data analysis, clinical data analysis has great fundaments for best practices, since it is a critical domain. Hence, you can try to look more into this field. Additionally, you can look at some literature. You have some interesting literature coming from this field [3, 4], however; if you want to take a look at medical data analysis for breast cancer you can also follow my (@FMCalisto) work [1, 2].

References

[1] Calisto, F. M., Santiago, C., Nunes, N., & Nascimento, J. C. (2022). BreastScreening-AI: Evaluating medical intelligent agents for human-AI interactions. Artificial Intelligence in Medicine, 127, 102285.

[2] Calisto, F. M., Nunes, N., & Nascimento, J. C. (2020, September). BreastScreening: on the use of multi-modality in medical imaging diagnosis. In Proceedings of the international conference on advanced visual interfaces (pp. 1-5).

[3] Knight, R., Vrbanac, A., Taylor, B. C., Aksenov, A., Callewaert, C., Debelius, J., ... & Dorrestein, P. C. (2018). Best practices for analysing microbiomes. Nature Reviews Microbiology, 16(7), 410-422.

[4] McGinnis, J. M., Olsen, L., Goolsby, W. A., & Grossmann, C. (Eds.). (2011). Clinical data as the basic staple of health learning: Creating and protecting a public good: Workshop summary. National Academies Press.

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