https://github.com/tjmahr/madr_pipelines

Slides and materials for my talk to the Madison R Users Group
https://github.com/tjmahr/madr_pipelines

data-science dplyr magrittr presentation r

Last synced: 2 days ago
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Slides and materials for my talk to the Madison R Users Group

• Host: GitHub
• URL: https://github.com/tjmahr/madr_pipelines
• Owner: tjmahr
• License: gpl-2.0
• Created: 2015-03-19T16:47:37.000Z (over 9 years ago)
• Default Branch: master
• Last Pushed: 2015-06-17T20:26:06.000Z (over 9 years ago)
• Last Synced: 2023-06-28T12:20:40.384Z (over 1 year ago)
• Topics: data-science, dplyr, magrittr, presentation, r
• Language: HTML
• Homepage:
• Size: 1.26 MB
• Stars: 31
• Watchers: 2
• Forks: 9
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Let the Data Flow: Pipelines in R with dplyr and magrittr

## Abstract 

> Pipelines were the best thing to happen in R in 2014. They let us transform 

messy, inside-out code like `sort(unique(round(xs, 2)))` into a clear chain of 

transformations like `xs %>% round(2) %>% unique %>% sort`. In this talk I lead 

a tutorial on how to use pipelines for data-cleaning, transformation and 

presentation with the packages `magrittr` and `dplyr`. For beginners, I also 

review some of the essential R functions to make the most of pipelines.

>

> Tristan is a PhD student in Communication Sciences and Disorders. He uses in 

R in the [Learning To Talk lab](http://learningtotalk.org) to model 

eye-tracking and speech perception data. [@tjmahr](https://twitter.com/tjmahr), 

[github.com/tjmahr](https://github.com/tjmahr). 

## Slides

I prepared three sets of slides:

* [Pipelines in R](http://rpubs.com/tjmahr/pipelines_2015)

* [dplyr: verbs for manipulating data-frames](http://rpubs.com/tjmahr/dplyr_2015)

* [Making pretty regression tables with pipes](http://rpubs.com/tjmahr/prettytables_2015)

## Resources

* [magrittr vignette](http://cran.r-project.org/web/packages/magrittr/vignettes/magrittr.html)

* [RStudio Data-Wrangling Cheatsheet](http://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf)

* [Core R vocabulary](http://adv-r.had.co.nz/Vocabulary.html)

* [Awesome R](https://github.com/qinwf/awesome-R)

* [Pipelines for Data Analysis](https://www.youtube.com/watch?v=40tyOFMZUSM) (dpylr/magrittr talk by Hadley Wickham)

* [Best Practices for Scientific Computing](http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001745)

* [Data Science on the Command Line](http://datascienceatthecommandline.com/)

* [Unix Commands for Data Science](http://www.gregreda.com/2013/07/15/unix-commands-for-data-science/)

 

### Packages

* [magrittr](https://github.com/smbache/magrittr) for pipelines

* [dplyr](https://github.com/hadley/dplyr) for data-frame functions

* [broom](http://cran.r-project.org/web/packages/broom/index.html)

* [stringr](http://cran.r-project.org/web/packages/stringr/index.html) for 

  string manipulation functions

* [pipeR](https://github.com/renkun-ken/pipeR) an alternative pipeline package

  (that I haven't tried yet).

## License

Obviously, the GPL-2 license applies only to the code and words I wrote, which 

are in the `.Rpres` and `.md` files and are reproduced with markup in the 

`.html` files.