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Talk at Workshop on Visualization for Big Data: Strategies and Principles, Fields Institute http://www.fields.utoronto.ca/programs/scientific/14-15/bigdata/visualization/
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2015-02-23_bryan-fields-talk

Talk at Workshop on Visualization for Big Data: Strategies and Principles, Fields Institute

http://www.fields.utoronto.ca/programs/scientific/14-15/bigdata/visualization/

Want to see the slides?

Want to see a video of the talk?

Links

The Big Data Brain Drain: Why Science is in Trouble: http://jakevdp.github.io/blog/2013/10/26/big-data-brain-drain/

RStudio IDE: http://www.rstudio.com/products/rstudio/

GitHub: https://github.com

The R Project: http://www.r-project.org

STAT 545: http://stat545-ubc.github.io

Slides from Hadley Wickham's talk in the Simply Statistics Unconference

2014-08-17 NYT article For Big-Data Scientists:, 'Janitor Work' Is Key Hurdle to Insights by Steve Lohr

Data carpentry blog post by David Mimno (response to NYT article re: janitor work)

Daring Fireball's markdown page. Kind of where it all begins, but other references that are more recent and specific to our context are more relevant.

Carson Sievert's talk Reproducible web documents with R, knitr & Markdown

MathJax is an open source JavaScript display engine for mathematics that works in all browsers ... It just works.

MathJax + RStudio specifics:

Pandoc: If you need to convert files from one markup format into another, pandoc is your swiss-army knife.

R Markdown: http://rmarkdown.rstudio.com

knitr: http://yihui.name/knitr/

Simple Markdown and R Markdown example files are here: https://github.com/jennybc/2013-11_sfu

Baumer, B., Cetinkaya-Rundel, M., Bray, A., Loi, L., & Horton, N. J. (2014). R Markdown: Integrating A Reproducible Analysis Tool into Introductory Statistics. Technology Innovations in Statistics Education, 8(1). link

RPubs: http://rpubs.com

RStudio + R Markdown specifics:

"FINAL".doc from PHDCOMICS: http://www.phdcomics.com/comics/archive.php?comicid=1531

Jeromy Anglim's blog post Getting Started with R Markdown, knitr, and Rstudio 0.96 and a Gist containing the source

Yihui Xie blog post about when to use Markdown and when to use LaTeX

Git: http://git-scm.com

GitHub: https://github.com

Ram, K 2013. Git can facilitate greater reproducibility and increased transparency in science. Source Code for Biology and Medicine 2013 8:7. Go to the associated github repo to get the PDF (link at bottom of README) and to see a great example of how someone managed the process of writing a manuscript with git(hub).

Karl Broman's tutorial, aimed at stats / data science types: An introduction to Git/Github.

SourceTree Git client, with a GUI. Available for Windows or Mac.

RStudio + Git(Hub)

xkcd on Git commit messages: http://xkcd.com/1296/

A few R packages on GitHub:

GitHub repos from government, media, and more

Rough notes on GitHub usage in STAT 545: http://stat545-ubc.github.io/bit004_stat545-use-of-github.html

R Graph Catalog: http://shinyapps.stat.ubc.ca/r-graph-catalog/

A few Shiny apps by STAT 545 students:

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