-
Notifications
You must be signed in to change notification settings - Fork 11
/
Copy path03_data_science_practice.Rmd
411 lines (172 loc) · 22.7 KB
/
03_data_science_practice.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
# Statistical & Data Science Practice {#practice}
First, an excellent list of data science resources:
* [Practical Data Science for Stats - a PeerJ Collection](https://peerj.com/collections/50-practicaldatascistats/)
## Introduction
How does one approach a statistics or data science project?
### A theory of data analysis
Roger Peng, 2018-12-11, ["The Role of Theory in Data Analysis"](https://simplystatistics.org/2018/12/11/the-role-of-theory-in-data-analysis/)
Stephanie C. Hicks and Roger Peng, 2019-03-18, ["Elements and Principles of Data Analysis"](https://arxiv.org/abs/1903.07639v1), arXiv.org [@Hicks_Peng_2019]
Stephanie C. Hicks and Roger Peng, 2019-04-26, ["Evaluating the Success of a Data Analysis"](https://arxiv.org/abs/1904.11907), arXiv.org [@Hicks_Peng_2019b]
### The function of data science: solving business problems
Emily Robinson, 2017-09-27, [Managing Business Challenges In Data Science](https://robinsones.github.io/Managing-Business-Challenges-in-Data-Science/)
### Design thinking >> data science context
Roger Peng, 2019-01-09, [How Data Scientists Think - A Mini Case Study](https://simplystatistics.org/2019/01/09/how-data-scientists-think-a-mini-case-study/)
### Opinionated Analysis Development
An over-arching structure of what a project could (or should?) look like can be boiled down into three features: it is
1. Reproducible and auditable
2. Accurate
3. Collaborative
Hilary Parker, 2017-08-30, [Opinionated Analysis Development](https://peerj.com/preprints/3210/)
- some of Hilary Parker's earlier / supporting thoughts on this topic can be found in [her talk "Opinionated Analysis Development" from rstudio::conf2017 (2017-01-14)](https://www.rstudio.com/resources/videos/opinionated-analysis-development/) ([slides alone at slideshare](https://www.slideshare.net/hilaryparker/opinionated-analysis-development)), as well as the slides from her [keynote at EARL SF (2017-06-15)](https://www.slideshare.net/hilaryparker/opinionated-analysis-development-earl-sf-keynote)
## General practice and workflow
**Jenny Bryan on workflow:**
Jenny Bryan, [Project-oriented workflow](https://www.tidyverse.org/articles/2017/12/workflow-vs-script/)
Jenny Bryan, [Ode to the here package](https://github.com/jennybc/here_here)
Wilson G, Bryan J, Cranston K, Kitzes J, Nederbragt L, Teal TK (2017) Good enough practices in scientific computing. PLoS Comput Biol 13(6): e1005510. https://doi.org/10.1371/journal.pcbi.1005510
Jenny Bryan (2017) [Workflow: you should have one](https://speakerdeck.com/jennybc/workflow-you-should-have-one), Keynote talk at EARL London 2017.
- [Supporting documentation: earl-london-2017-bryan](https://github.com/jennybc/earl-london-2017-bryan#readme)
**other authors**
Keiran Healy, [_The Plain Person's Guide to Plain Text Social Science_](https://kieranhealy.org/files/papers/plain-person-text.pdf) {pdf}
Kass RE, Caffo BS, Davidian M, Meng X-L, Yu B, Reid N (2016) ["Ten Simple Rules for Effective Statistical Practice"](http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004961). PLoS Comput Biol 12(6): e1004961. doi:10.1371/journal.pcbi.1004961
Ray Li (2016) ["7 habits of highly effective data analysis"](http://dataconomy.com/2016/02/7-habits-of-highly-effective-data-analysis/). [Dataconomy.com](Dataconomy.com), 2016-02-16.
Noble, William Stafford (2009-07-31) [A Quick Guide to Organizing Computational Biology Projects](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000424), _PLoS Comput Biol_ 5(7): e1000424.
## Version Control with Git & GitHub
Jenny Bryan, the STAT 545 TAs, Jim Hester [Happy Git and GitHub for the useR](https://happygitwithr.com/)
Mine Çetinkaya-Rundel (2019-07-15) [R & GitHub sitting in a tree...](https://speakerdeck.com/minecr/r-and-github-sitting-in-a-tree-dot-dot-dot)
## R-specific workflow
### Agile
Edwin Thoen, [Agile Data Science with R](https://edwinth.github.io/ADSwR/)
### other
Gabe Becker (2017) [Enhancing reproducibility, comparability and discoverability of results in multi-analyst settings](https://www.slideshare.net/GabrielBecker11/enhancing-reproducibility-comparability-and-discoverability-of-results-in-multianalyst-settings), presentation at EARL (Enterprise Applications of R Language), San Francisco, June 5-7, 2017
* this came to my attention via the Not So Standard Deviations podcast, [episode 40 "It's the CDs All Over Again"](https://www.patreon.com/posts/episode-40-its-11713845) (2017-06-13). The discussion of Gabe Becker's presentation begins at ~13' 10".
- some of Hilary Parker's observations: the topic doesn't get much air time, his talk takes wide view of issues in an organization, trade-offs in collaborative environment (not one analyst); in multi-analyst system (e.g. where both a researcher/biologist and a statistician are both working with the same data) have to reconcile results, there might be parallel studies where results need to be reconciled, >> this creates a need for the data to be created in similar environments.
- Concerns: reproducibity, comparability, discoverability (finding the results), and empowerment. Data scientists skew to emppowerment! But the concerns are in tension--you can increase reproducibility but at cost of empowerment, etc.
- "Most organizations aren't making that judgement call ahead of time...any organization ... if data scientists were there early, you're going to be skewed to agility and high empowerment. Because that's like what we want! ... Data scientists are allergic to process...having any template for results, or ... even having to put things into a specific tool."
- Different systems need different constraints.
- Roger Peng: most organizations don't realize that they are explicitly making these trade-offs, can't maximize all. Have to make a choice, and that's very unsatisfactory for people.
Emily Robinson, [Red Flags in Data Science Interviews](http://hookedondata.org/Red-Flags-in-Data-Science-Interviews/)
<blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">New blog post, co-written with <a href="https://twitter.com/skyetetra?ref_src=twsrc%5Etfw">/@/skyetetra</a>! 12 red flags to watch out for in data science interviews 🚩<a href="https://t.co/hM2E7I46Da">https://t.co/hM2E7I46Da</a> <a href="https://t.co/jFVA7mmjjU">pic.twitter.com/jFVA7mmjjU</a></p>— Emily Robinson (/@/robinson_es) <a href="https://twitter.com/robinson_es/status/1014164367292747778?ref_src=twsrc%5Etfw">July 3, 2018</a></blockquote>
<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
## R packages supporting robust workflow
### `{janitor}`
[{janitor}](sfirke.github.io/janitor/index.html) -- "has simple functions for examining and cleaning dirty data. It was built with beginning and intermediate R users in mind and is optimized for user-friendliness. Advanced R users can already do everything covered here, but with janitor they can do it faster and save their thinking for the fun stuff."
CRAN: [janitor: Simple Tools for Examining and Cleaning Dirty Data](https://cran.r-project.org/web/packages/janitor/index.html)
GitHub: [sfirke/janitor](https://github.com/sfirke/janitor)
### `{packrat}`
[{Packrat} is a dependency management system for R](http://rstudio.github.io/packrat/)
CRAN: [packrat: A Dependency Management System for Projects and their R Package Dependencies](https://cran.r-project.org/web/packages/packrat/index.html)
GitHub: [rstudio/packrat](https://github.com/rstudio/packrat)
** Articles**
Miles McBain (2019-04-09) [A workflow for lightweight R dependency management](https://milesmcbain.xyz/packrat-lite/)
### `{usethis}`
"usethis is a workflow package: it automates repetitive tasks that arise during project setup and development, both for R packages and non-package projects"
[usethis 1.5.0](https://www.tidyverse.org/articles/2019/04/usethis-1.5.0/) (April 2019)
***
## Reproducible research
<blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">This is the future. Show. Your. Damn. Work. <a href="https://t.co/4GWFdXSs17">https://t.co/4GWFdXSs17</a></p>— Chris Albon (/@/chrisalbon) <a href="https://twitter.com/chrisalbon/status/953078784084754433?ref_src=twsrc%5Etfw">January 16, 2018</a></blockquote>
<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
Or:
<blockquote class="twitter-tweet"><p lang="en" dir="ltr">Every scientific project will be redone in its entirety about 10-20 times from start to publication. Plan your work flow accordingly, e.g. a piece of R code that takes a data file and produces the analysis and figure/s. Change the data? Just rerun the code.</p>— Trevor A. Branch (@TrevorABranch) <a href="https://twitter.com/TrevorABranch/status/1157006269292507136?ref_src=twsrc%5Etfw">August 1, 2019</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
[reproducibleresearch.net](http://reproducibleresearch.net/)
Emily Riederer, 2019-08-29, [Resource Round-Up: Reproducible Research Edition](https://emilyriederer.netlify.com/post/resource-round-up-reproducible-research-edition/) -- annotated bibliography of some essential resources on the topic.
Justin Kitzes, Daniel Turek, Fatma Deniz (Eds.), [The Practice of Reproducible Research: Case Studies and Lessons from the Data-Intensive Sciences](https://www.practicereproducibleresearch.org/) -- online book with 31 case studies of reproducible research workflows.
Roger Peng (2014-06-06) [The Real Reason Reproducible Research is Important](https://simplystatistics.org/2014/06/06/the-real-reason-reproducible-research-is-important/)
Roger Peng, 2015-06-15, ["The reproducibility crisis in science: A statistical counterattack"](http://onlinelibrary.wiley.com/doi/10.1111/j.1740-9713.2015.00827.x/full), [_Significance_](http://rss.onlinelibrary.wiley.com/hub/journal/10.1111/(ISSN)1740-9713/)
Rich FitzJohn, Matt Pennell, Amy Zanne, Will Cornwell (2014-06-09) [Reproducible research is still a challenge](https://ropensci.org/blog/2014/06/09/reproducibility/) (at [rOpenSci](https://ropensci.org/))
Melissa Assel, MS; Andrew J. Vickers, PhD (2018-02-06) ["Statistical Code for Clinical Research Papers in a High-Impact Specialist Medical Journal"](http://annals.org/aim/article-abstract/2671924/statistical-code-clinical-research-papers-high-impact-specialist-medical-journal), _Annals of Internal Medicine_
V. Orozco, C. Bontemps, E. Maigné, V. Piguet, A. Hofstetter, A. Lacroix,
F. Levert, J.M. Rousselle (2018-07) ["How To Make A Pie: Reproducible Research for Empirical
Economics & Econometrics"](https://www.tse-fr.eu/sites/default/files/TSE/documents/doc/wp/2018/wp_tse_933.pdf)
Jeffrey M. Perkel, ["A toolkit for data transparency takes shape"](https://www.nature.com/articles/d41586-018-05990-5), _Nature_, 2018-08-20.
Daniel Barron (2018-08-13) [How Freely Should Scientists Share Their Data?](https://blogs.scientificamerican.com/observations/how-freely-should-scientists-share-their-data/), _Scientific American_ blog
Karl Broman (2019-02-17) [Collaboratingreproducibly](https://www.biostat.wisc.edu/~kbroman/presentations/rrcollab_aaas2019.pdf), slides for talk given at the AAAS meeting in Washington, DC. (See also https://github.com/kbroman/Talk_AAAS2019)
### Reproducible research with R
[R for Reproducible Scientific Analysis](https://swcarpentry.github.io/r-novice-gapminder/) -- "an introduction to R for non-programmers using gapminder data", part of the Software Carpentries
Jeremy Anglin, [Reproducible analysis with knitr, R Markdown, and RStudio](https://github.com/jeromyanglim/rmarkdown-rmeetup-2012)
Ben Marwick, 20 July 2017, [Reproducible Research Compendia via R packages](https://rawgit.com/benmarwick/Marwick-Berlin-R-users-2017/master/Marwick-Berlin-R-users-2017.html#1), presentation at Berlin R Users
Sharla Gelfand (2020-01-30) [Don't repeat yourself, talk to yourself! Repeated reporting in the R universe](https://sharstudioconf.netlify.com/#1) -- presentation at rstudio::conf 2020
* see also [{redoc}], "a package to enable a two-way R Markdown Microsoft Word workflow. It generates Word documents that can be de-rendered back into R Markdown, retaining edits on the Word document, including tracked changes."
* see also [this list](https://twitter.com/jessenleon/status/1150749113014738946)
### Reproducible data
Greg Finak (2018-09-18) [Building Reproducible Data Packages with DataPackageR](https://ropensci.org/blog/2018/09/18/datapackager/)
Luis Darcy Verde Arregoitia (2018) [Good practices for sharing analysis-ready data in mammalogy and biodiversity research](http://www.italian-journal-of-mammalogy.it/Good-practices-for-sharing-analysis-ready-data-in-mammalogy-and-biodiversity-research,101564,0,2.html), Hystrix It. J. Mamm. 2018;29(2):155–161
### spreadsheets: the anti-reproducible research
Karl Broman and Kara Woo, "Data organization in spreadsheets" [@Broman_Woo_2017].
[Data Organization in Spreadsheets for Social Scientists: Formatting problems](https://datacarpentry.org/spreadsheets-socialsci/02-common-mistakes/index.html) -- DataCarpentry lesson
<blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">I read "Data analysis without scripting" as "Dystopian moonscape of unrecorded user actions". I may not be Tableau's target market. <a href="https://twitter.com/hashtag/rstats?src=hash">#rstats</a></p>— Gordon Shotwell (/@/gshotwell) <a href="https://twitter.com/gshotwell/status/577485681146097664">March 16, 2015</a></blockquote>
<script async src="//platform.twitter.com/widgets.js" charset="utf-8"></script>
Jenny Bryan's [spreadsheets](https://speakerdeck.com/jennybc/spreadsheets) talk given May & June 2016 reframes Shotwell as "Spreadsheets: a dystopian moonscape of unrecorded user actions."
- live in-person (https://pbs.twimg.com/media/CmDykgRWAAE-_MP.jpg)
Ignasi Bartomeus and F Rodriguez-Sanchez, _Non-reproducible workflows: a horror movie_ :
<blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">That awesome video on reproducibility with <a href="https://twitter.com/hashtag/rstats?src=hash&ref_src=twsrc%5Etfw">#rstats</a> by <a href="https://twitter.com/ibartomeus?ref_src=twsrc%5Etfw">/@/ibartomeus</a> and <a href="https://twitter.com/frod_san?ref_src=twsrc%5Etfw">/@/frod_san</a> you can find here: <a href="https://t.co/indBflvupv">https://t.co/indBflvupv</a> <a href="https://t.co/QcdonwVTk8">https://t.co/QcdonwVTk8</a></p>— David Smith (/@/revodavid) <a href="https://twitter.com/revodavid/status/917779772385656832?ref_src=twsrc%5Etfw">October 10, 2017</a></blockquote>
- with more at [Reproducibilidad](http://ecoinfaeet.github.io/2016/07/06/reproducibilidad/)
Gordon Shotwell, 2017-02-02, [R for Excel Users](http://blog.shotwell.ca/post/r_for_excel_users/)
Luis A. Apiolaza, 2017-11-11, [Reducing friction in R to avoid Excel](http://www.quantumforest.com/2017/11/reducing-friction-to-avoid-excel/)
## Collaboration
Amit Bhattacharyya, 2017-11-01, [Become a Better Statistician by Actively Collaborating](http://magazine.amstat.org/blog/2017/11/01/collaborating/) (at [_Amstatnews_](http://magazine.amstat.org/))
Peter Seibel, 2017-11-19, [Repo style wars: mono vs multi](http://www.gigamonkeys.com/mono-vs-multi/)
## Data Informed or Data Driven? Data Science at Work
Ricardo Bion, Robert Chang, and Jason Goodman (2017-08-23) [How R Helps Airbnb Make the Most of Its Data](https://peerj.com/preprints/3182.pdf)
[The Stitch Fix Algorithms Tour](http://algorithms-tour.stitchfix.com/)
Behavioural Insights Team (UK), 2017-12-14, [Using Data Science in Policy](http://www.behaviouralinsights.co.uk/publications/using-data-science-in-policy/)
## Agile practice
[Agile Scrum Guide](http://agile-guide.pathfinder.bcgov/), BC Government DevEx
## Data Quality & Context Compatibility
Or, do your data really mean what you hope they do?
Jacob Harris (2014) ["Distrust your data"](https://source.opennews.org/en-US/learning/distrust-your-data/), 2014-05-24.
Roger Peng (2018) [Context Compatibility in Data Analysis](https://simplystatistics.org/2018/05/24/context-compatibility-in-data-analysis/), 2018-05-30
## File storage and naming conventions
[Sustainability of Digital Formats: Planning for Library of Congress Collections](http://www.digitalpreservation.gov/formats/index.shtml)
Jenny Bryan, [naming things](http://www2.stat.duke.edu/~rcs46/lectures_2015/01-markdown-git/slides/naming-slides/naming-slides.pdf),
Reproducible Science Workshop, 2015
***
## Data practice
Karl Broman, [data organization](http://kbroman.org/dataorg/)
Karl Broman and Kara Woo, "Data organization in spreadsheets" [@Broman_Woo_2017].
<blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">For data scientists thinking about biases in your data, don't start by reading the computer science literature. Read epidemiology instead. You need data street smarts, not mathy book smarts. Otherwise the first data set you meet is going to beat you up and take your lunch money!</p>— Kareem ❤️ statistics (/@/kareem_carr) <a href="https://twitter.com/kareem_carr/status/1094993530097991680?ref_src=twsrc%5Etfw">February 11, 2019</a></blockquote>
<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
### Versioned data
Daniel Falster, Richard G FitzJohn, Matthew W. Pennell, William K. Cornwell (2017-11-10) [Versioned data: why it is needed and how it can be achieved (easily and cheaply)](https://peerj.com/preprints/3401/)
***
## Coding practice
<blockquote class="twitter-tweet"><p lang="en" dir="ltr">Why writing readable code matters:<br><br>It’s like the turn signals in your car: Even though you (the driver) might know where you’re going, you still need to make your intentions clear to everyone else around you.<a href="https://twitter.com/hashtag/DevDiscuss?src=hash&ref_src=twsrc%5Etfw">#DevDiscuss</a> <a href="https://t.co/5GKrYaEz41">https://t.co/5GKrYaEz41</a></p>— Olivia Liddell (/@oliravi) <a href="https://twitter.com/oliravi/status/1181741102447116288?ref_src=twsrc%5Etfw">October 9, 2019</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
[Coding etiquette](https://ourcodingclub.github.io/2017/04/25/etiquette.html), a guide to writing clear, informative and easy-to-use #RStats code by /@/CodingClub
Joel Lee, 2017-12=22, [The Weirdest Programming Principles You’ve Never Heard Of](https://www.makeuseof.com/tag/weird-programming-principles/)
### Style guides for code
Hadley Wickham, [The tidyverse style guide](https://style.tidyverse.org/)
Hadley Wickham, ["Style Guide"](http://adv-r.had.co.nz/Style.html) chapter from [_Advanced R_](http://adv-r.had.co.nz/)
Henrik Bengtsson, [_R Coding Conventions (RCC) - a draft](https://docs.google.com/document/d/1esDVxyWvH8AsX-VJa-8oqWaHLs4stGlIbk8kLc5VlII/edit) (2009)
[_Google's R Style Guide_](https://google.github.io/styleguide/Rguide.xml)
### Version control
Jenny Bryan and the STAT 545 TAs, [_Happy Git and GitHub for the useR_](http://happygitwithr.com/)
John D. Blischak, Emily R. Davenport, Greg Wilson (2016-01-19) ["A Quick Introduction to Version Control with Git and GitHub"](http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004668), _PLoS Computational Biology_.
### Documentation
<blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">"Writing documentation is all about making future you remember things that present you knows future you will forget" -- <a href="https://twitter.com/data_stephanie?ref_src=twsrc%5Etfw">/@/data_stephanie</a> <a href="https://twitter.com/hashtag/rstats?src=hash&ref_src=twsrc%5Etfw">#rstats</a> <a href="https://twitter.com/hashtag/Rladies?src=hash&ref_src=twsrc%5Etfw">#Rladies</a></p>— R-Ladies Chicago (/@/RLadiesChicago) <a href="https://twitter.com/RLadiesChicago/status/963576859152744456?ref_src=twsrc%5Etfw">February 14, 2018</a></blockquote>
Sébastien Rochette, 2019-07-10, [Rmd first: When development starts with documentation](https://www.r-bloggers.com/rmd-first-when-development-starts-with-documentation/)
* see also [RMarkdown]
### Literate programming
The practice of explaining the program logic in a natural language"; it goes beyond what might be called "documentation". In the world of R, the RMarkdown functionality within [RStudio](https://www.rstudio.com/products/rstudio/), including [R notebooks](http://rmarkdown.rstudio.com/r_notebooks.html), is a way to program in this manner.
The concept was introduced by Donald Knuth in 1984; the original article is
> Knuth, Donald E. (1984). ["Literate Programming"](http://www.literateprogramming.com/knuthweb.pdf) (PDF). _The Computer Journal_. British Computer Society. 27 (2): 97–111.
* [literateprogramming.com](http://www.literateprogramming.com/)
* [Wikipedia entry](https://en.wikipedia.org/wiki/Literate_programming)
### Functions in R
Colin Fay, [Playing with R, infix functions, and pizza ](http://colinfay.me/playing-r-infix-functions/)
### Naming variables
"There are only two hard things in Computer Science: cache invalidation and naming things."
-- Phil Karlton
Andy Lester, ["The World's Two Worst Variable Names"](http://archive.oreilly.com/pub/post/the_worlds_two_worst_variable.html)
### Clean coding
Robert C. Martin, 2008, _Clean Code: A Handbook of Agile Software Development_, Prentice Hall.
Robert C. Martin, 2011, _The Clean Coder: A Code of Conduct for Professional Programmers_, Prentice Hall.
### Further reading
Aimee Gott, 2015-07-16, [Developing a R Validation Framework](https://www.baselr.org/wp-content/uploads/sites/4/presentations/BaselR_-_Formalising_R_Development_-_Aimee_Gott_-_20150716.pdf), BaselR
Dani Marillas, 2017-01-25, ["Don’t document your code. Code your documentation."](https://dev.to/raddikx/dont-document-your-code-code-your-documentation)
## General research practice
<blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">I didn't think my "start-up" document for my grad students (the resources I wish *I* had when I started a PhD) would prove to be so popular, so I figured I would share them here. So, here's what I've compiled so far. <a href="https://twitter.com/hashtag/hiddencurriculum?src=hash&ref_src=twsrc%5Etfw">#hiddencurriculum</a> 1/12</p>— Matt Hauer (/@/thehauer) <a href="https://twitter.com/thehauer/status/1021179403680862218?ref_src=twsrc%5Etfw">July 22, 2018</a></blockquote>
***
### things that are no doubt useful and/or interesting but don't really fit anywhere in the existing typology
Louisa Smith , [epi quals study calendar](https://docs.google.com/spreadsheets/d/1HyyfGZhAzVtyJ-tcuBWxKbmsaJ3uMkuXyfzAUYsfemw/edit#gid=0)
* https://twitter.com/louisahsmith/status/1081955868864901120
***