1. R In Production:
the products
Yasmin Lucero, PhD
Senior Statistician, Gravity-AOL
UserR! 2014

2. Outline
• Internal products
• 1. one-off analysis
• 2. automated reports
• 3. internal R packages
• 4. internal dashboards
• External products
• 1. customer facing web-app
• 2. analytical backend service
• Ops and the managing of an R environment

3. Internal Product 1:
one-off analytical product
http://rpubs.com/nathanesau1/21383
Nathan Esau
Hilary Parker

4. Internal Product 2:
Automated reports
Thursday morning:
Automated Business Reporting with
R (Zhengying (Doro) Lour)
R + bash + email
R + markdown + web server

5. Internal Product 3:
The Internal R package
• Data APIs
• Business specific metrics
• Custom plotting functions
• Custom data manipulation utilities
Thursday Morning:
An R tools platform in Cosmetic Industry (Jean-Francois Collin)

6. Internal Product 4:
The internal dashboard
Gravity-AOL

7. External Product 1:
Customer facing web app
Wednesday afternoon
Rapid Prototyping with R/Shiny at
McKinsey (Aaron Horowitz)
http://www.showmeshiny.com/

8. External Product 2:
analytical back-end
Wed afternoon:
Deploying R into Business Intelligence and Real-time Applications
(Louis Bajuk-Yorgan)
Zillow’s Big Data and Real-time Services in R (Yeng Bun)

9. Artwork
& Brands
Bank
Partner
Transactions
CARD.COM
Site / App
CARD.COM
AdTech Platform
APIs
RTB Ad
Xchgs
CARD.COM
Analytics Platform
Members
Visitors
1
2
3
Details: card.com/useR-2014
predict
deploy
learn
CARD.com

10. More good example applications:
• http://blog.revolutionanalytics.com/2014/06/how-data-
driven-companies-use-r-to-compete.html

11. Ops: Managing an R Environment
• Overall: not complex, but there are pain points:
• R library management
• CRAN, non-CRAN and internal packages
• Version management
• Dependency management (pulling all dependencies)
• Non-R dependencies (especially C++ and Java)
• Hardware specifications: How much RAM is enough?

12. Conclusion: Why R?
• Plotting
• Rich analytical library
• More than a DSL: end to end functionality from data APIs
to web apps
• Solid IDE support
• Sturdy, stable easy to support platform
• Rapid prototyping

13. yasmin.lucero@gmail.com
Thanks.

14. Tools: plotting
• Major frameworks
• Base graphics
• lattice
• ggplot2
• Useful utilties
• grid/gridExtra/gtable
• latticeExtra
• Color: RColorBrewer/munsell/colorspace/dichromat
• gplots (the ‘g’ school)
• plotrix
• Custom plots
• plot.ts
• maps
• igraph (network visualization)
• ggmap
• ggvis: interactive graphics
• rcharts: interactive graphics, wraps js libraries, not on CRAN yet (look on github)
• rgl (3d)/scatterplot3d
• vcd (categorical data)

15. Tools: data manipulation
• Base R features
• Data structures: the data.frame
• Vectorized data manipulation: apply, tapply, lapply…
• Data structures: ts
• Comprehensive, elegant missing data handling (NA)
• Packages
• Wickham school: reshape2/plyr/dplyr/tidyr
• data.table
• Time series: zoo, xts, lubridate
• Spatial data tools: sp/maptools
• The ‘G’ school: gdata

16. Tools: Data interfaces
• Connections: read.table(); url()
• DBI: RpostgresSQL; RMySQL; RSQLite;…
• RODBC; RJDBC: (vertica, redshift)
• Native: rredis; rmongodb; prestodb; RCassandra; Rhadoop; …
• yaml, XML, rjson, RJSONIO,
• MS Excel: xlsx, XLConnect
• SAS, SYSTAT, SPSS, Stata…: foreign
• Rcurl
• RProtoBuf: Efficient cross-language data serialization in R

17. Tools: Package development
• Package development:
• package.skeleton(); tools (base package)
• pkgKitten (CRAN): improvements to package.skeleton
• devtools (CRAN) : miscellaneous and very useful tools
• gtools: various R programming tools
• roxygen2 (CRAN): literate documentation
• testthat/testR: unit testing
• IDEs: RStudio, Eclipse (StatET), TINN-R, Emacs ESS, …

18. Tools: Web development & reporting
• Shiny
• Interactive documents
• Knitr
• Sweave

19. Tools: parallel computing
• parallel: lots of features formerly distributed among
packages have recently been collected into this base R
package
• Revolution analytics
• Map-Reduce: rmr/rhadoop
• H20 (hexadata)
• SparkR (not on CRAN yet, look on github)

20. Tools: big or out of memory computing
• dplyr: supports database backed data structures
• ff: supports file based data
• biglm/bigmemory: shared memory matrices
• HadoopStreaming

21. Tools: memory profiling
• lineprof
• profr
• proftools
• object.size()