1. Introducing
Revolution R Open
The Enhanced R Distribution
November 12, 2014

2. In today’s webinar:
R Update
Revolution R Open
The Reproducible R Toolkit
MRAN
Other open-source projects
• DeployR Open
• ParallelR
• Rhadoop
Revolution R Plus
Q&A
David Smith
Chief Community Officer
Revolution Analytics
@revodavid
david@revolutionanalytics.com
Editor, blog.revolutionanalytics.com
Co-author, “Introduction to R”

3. 3
OUR COMPANY
The leading provider
of advanced analytics
software and services
based on open source R,
since 2007
OUR PRODUCT
REVOLUTION R: The
enterprise-grade predictive
analytics application platform
based on the R language
SOME KUDOS
Visionary
Gartner Magic Quadrant
for Advanced Analytics
Platforms, 2014

4. What is R?
 Most widely used data analysis software
• Used by 2M+ data scientists, statisticians and analysts
 Most powerful statistical programming language
• Flexible, extensible and comprehensive for productivity
 Create beautiful and unique data visualizations
• As seen in New York Times, Twitter and Flowing Data
 Thriving open-source community
• Leading edge of analytics research
 Fills the Data Science talent gap
• New graduates prefer R
www.revolutionanalytics.com/what-is-r

5. 5
Poll #1
What software do you use for statistical analysis? (Select all that apply.)
 R
 SAS
 SPSS
 Python
 Other

6. 6
R’s popularity is growing rapidly
More at blog.revolutionanalytics.com/popularity
R Usage Growth
Rexer Data Miner Survey, 2007-2013
• Rexer Data Miner Survey • IEEE Spectrum, July 2014
#9: R
Language Popularity
IEEE Spectrum Top Programming Languages

7. 7
Revolution R Open is:
 Enhanced Open Source R distribution
 Compatible with all R-related software
 Multi-threaded for performance
 Focus on reproducibility
 Open source (GPLv2 license)
 Available for Windows, Mac OS X, Ubuntu,
Red Hat and OpenSUSE
 Download from
mran.revolutionanalytics.com

8. 8
Multi-threaded performance
 Intel MKL replaces standard
BLAS/LAPACK algorithms
 Pipelined operations
– Optimized for Intel, works for all archs
 High-performance algorithms
 Sequential  Parallel
– Uses as many threads as there are
available cores
– Control with:
setMKLthreads(<value>)
 No need to change any R code
 Included in RRO binary distribution
More at Revolutions blog

9. 9
100% Compatibility
 Built on latest R engine
– Currently R 3.1.1, R 3.1.2 in testing
 100% compatible with
– R scripts
– R packages
– Applications with R connections
 Designed to work with Rstudio
– No configuration required
 Replaces existing R application
– Side-by-side installations

10. Reproducibility – why do we care?
Academic / Research
 Verify results
 Advance Research
Business
 Production code
 Reliability
 Reusability
 Collaboration
 Regulation
10
www.nytimes.com/2011/07/08/health/research/08genes.html
http://arxiv.org/pdf/1010.1092.pdf

11. 11
An R Reproducibility Problem
Adapted from http://xkcd.com/234/ CC BY-NC 2.5

12. 12
Reproducible R Toolkit in RRO
 Static CRAN mirror
– CRAN packages fixed with each Revolution R Open update
 Daily CRAN snapshots
– Storing every package version since September 2014
– Binaries and sources
– At mran.revolutionanalytics.com/snapshot
 Easily write and share scripts synced to a specific snapshot
– “checkpoint” package installed with RRO
CRAN
RRDaily
snapshots
http://mran.revolutionanalytics.com/snapshot/
checkpoint
package
library(checkpoint)
checkpoint("2014-09-17")
CRAN mirror
http://cran.revolutionanalytics.com/
checkpoint
server
Midnight
UTC

13. 13
Using checkpoint
 Easy to use: add 2 lines to the top of each script
library(checkpoint)
checkpoint("2014-09-17")
 For the package author:
– Use package versions available on the chosen date
– Installs packages local to this project
• Allows different package versions to be used simultaneously
 For a script collaborator:
– Automatically installs required packages
• Detects required packages (no need to manually install!)
– Uses same package versions as script author to ensure reproducibility

14. 14
MRAN: The Managed R Archive Network
 Download Revolution R
Open
 Learn about R and RRO
 Daily CRAN snapshots
 Explore Packages
– and dependencies
 Explore Task Views

15. Revolution Analytics
Open Source Projects
More at projects.revolutionanalytics.com

16. 16
DeployR Open
 Goal: embed results from R scripts into
existing applications, in real time
 Problem:
– Exposing arbitrary R functions is unwise
– Need to handle concurrent R sessions
 Solution: DeployR Open
– R, on a server, behind a firewall
– Repository Manager defines entry points
• Expose only authorized R functions
– Automatically creates Web Services APIs
– Manages and monitors pool of R sessions
– Separates roles for R and app developer
 DeployR Open: for prototyping integrations
– Revolution R Enterprise adds grid-scaling and
enterprise authentication
More at deployr.revolutionanalytics.com

17. 17
DeployR : Integration
DeployR does not provide any application UI.
3 integration modes embed real-time R results into existing interfaces
Web app, mobile app, desktop app, BI tool, Excel, …
RBroker Framework (tutorial):
Simple, high-performance API for Java, .NET and Javascript apps
Supports transactional, on-demand analytics on a stateless R session
Client Libraries (tutorial):
Flexible control of R services from Java, .NET and Javascript apps
Also supports stateful R integrations (e.g. complex GUIs)
DeployR Web Services API:
Integrate R using almost any client languages

18. Only available in Revolution R Enterprise DeployR
18
DeployR : Security / Scalability Layers
1. Anonymous execution
– Only authorized, user-defined R functions accessible
– No state preserved
2. Basic username / password authentication
– Managed in DeployR Administration Console
3. Enterprise Authentication
– Verifies identify with SSO / LDAP / Active Directory / PAM
4. Adaptive load-balancing grid
– Ensures service availability

19. DeployR Open demo
Fraud detection
19

20. 20
RHadoop and ParallelR
 Toolkits for data scientists and numerical analysts to create custom
parallel and distributed algorithms
 ParallelR: parallel programming for multi-CPU servers and grids
 RHadoop: map-reduce programming in R language
 Mainly useful for “embarrassingly parallel” problems, where parallel
components work with small amounts of data
 Big Data Predictive Analytics mostly not embarrassingly parallel
 80+ pre-built “parallel external memory algorithms” included with
Revolution R Enterprise

21. 21
RHadoop
 Collection of packages for interfacing R and Hadoop
 Client (desktop) R interface to Hadoop:
– rhdfs: Browse, read, write and modify files stored in HDFS
– rhbase: Browse, read, write and modify tables stored in HBASE
– ravro: Read, write and run map-reduce on Apache Avro files in HDFS
 R computations in Hadoop:
– rmr2: write map-reduce tasks in R to run in Hadoop
– plyrmr: R-based data manipulation computations on data in Hadoop
RHadoop Wiki: github.com/RevolutionAnalytics/RHadoop/wiki

22. 22
Word count in RHadoop
 Map:
– Input: lines of text
– Output: words with key value 1
 Reduce:
– Input: Words with several key values
– Output: words with counts
 Map-Reduce:
– Apply map to lines of text
– Gather like words together and count

23. Word count: execution
23
More: Video replay of “Using R with
Hadoop” by Jeffrey Breen
http://bit.ly/W35PLR

24. ParallelR
 foreach replaces for loops
– Minimal code change required
 Choice of parallel backends
– doParallel (base “parallel”)
– doMC (multi-core servers)
– doSNOW (grids)
 Iterations run in parallel
– Speedups depend on backend,
“granularity”
 All iterations run in-memory
24
birthday <- function(n) {
m <- 10000
x <- numeric(m)
for(i in 1:m) {
b <- sample(1:365, n, repl=T)
x[i] <- ifelse(length(unique(b))==n,0,1)
}
mean(x) # est prob of at least 1 match
}
for(j in 1:100) birthday(j)
library("doMC")
registerDoMC(2)
x <- foreach(j=1:100) %dopar% birthday(j)
2-core MacBook Air: 21.9s
2-core MacBook Air: 12.0s

25. Introducing
Revolution R Plus

26. 26
Revolution R Plus includes:
 AdviseR™ Technical Support for:
– Revolution R Open
• Including R, base and recommended packages
– Reproducible R Toolkit
– ParallelR: Parallel programming with R
– RHadoop: R integration with Hadoop
– DeployR Open: Secure deployment of R to applications
 Open Source Assurance for all supported components
– Provides legal indemnity for subscribers
 Workstation subscriptions: $1,800 per year
– Server and Hadoop subscriptions also available

27. 27
AdviseR™ Technical Support
Technical support for R, from the R experts.
 10x5 email and phone support (in your local time zone)
 Full support for R, validated packages, and third-party software
connections
 Notifications of updates and bug fixes
 On-line case management and knowledgebase
 Access to technical resources, documentation and user forums
 Defined service-level agreements for rapid responses
Included with Revolution R Plus and Revolution R Enterprise.

28. 28
Open Source Assurance
 Revolution Analytics will defend Revolution R Plus subscribers should a
third party make an intellectual property claim against covered open
source software with respect to:
– copyrights, patents, trademarks, trade secrets
 Covered software includes:
– Revolution R Open (incl. R base and recommended packages), Reproducible R
Toolkit, DeployR Open, ParallelR, RHadoop
 Revolution Analytics will defend open source software in court
– If necessary, Revolution Analytics will obtain rights, modify, or replace software
found to be infringing
– If a resolution can’t be found, fees paid in past 12 months will be refunded.

29. 29
The Revolution R Product Suite
• Free and open source R distribution
• Enhanced and distributed by Revolution Analytics
Revolution R Open
• Open-source distribution of R, packages, and other components
• Enhanced, supported and indemnified by Revolution Analytics
Revolution R Plus
• Secure, Scalable and Supported Distribution of R
• With proprietary components created by Revolution Analytics
Revolution R Enterprise

30. Revolution R Enterprise (RRE)
The All-Inclusive Big Data Big Analytics Platform
DistributedR
DeployR DevelopR
ScaleR
ConnectR
High-performance open source R plus:
 Data source connectivity to big-data objects
 Big-data advanced analytics
 Multi-platform environment support
 In-Hadoop and in-Teradata predictive modeling
 Visual Studio IDE option
 Secure, Scalable R Deployment
 Technical support, training and services
– 24x7 support option
30
Contact Revolution Analytics for more info: www.revolutionanalytics.com/contact-us

31. 31
Poll #2
Which Revolution Analytics projects do you plan to use (or already use?)
Select all that apply:
1. Revolution R Open (free distribution)
2. Revolution R Plus (paid subscription for support and indemnification)
3. Reproducible R Toolkit (checkpoint package)
4. DeployR Open
5. Rhadoop / ParallelR

32. 32
Wrapping up…
Revolution R Open is available now from
mran.revolutionanalytics.com/download
Explore Revolution Analytics open-source projects at
projects.revolutionanalytics.com
Technical support and open-source assurance with
Revolution R Plus
www.revolutionanalytics.com/plus
David Smith
Chief Community Officer
Revolution Analytics
@revodavid
david@revolutionanalytics.co
m

33. Thank you.
Next up:
Batter Up! Advanced Sports Analytics with R and Storm
December 11, 2014
revolutionanalytics.com/webinars
www.revolutionanalytics.com
1.855.GET.REVO
Twitter: @RevolutionR