This document provides a summary of the history of Revolution Analytics from 2007 to 2014. It discusses key events and milestones such as: - The founding of Revolution Analytics in 2007. - The launch of their Revolutions Blog in 2008 and growth in popularity of R. - Releases of new versions of Revolution R Enterprise in 2009 and 2010, along with growth in the number of R user groups. - Head to head performance comparisons of Revolution R against SAS in 2010 and 2011 that demonstrated Revolution R's reduced total cost of ownership. - The release of RHadoop in 2011 to support analytics on Hadoop and databases. - Recognition as a visionary in Gartner's Magic

1. A 5-minute history
David M Smith
Chief Community Officer
@revodavid
Sponsor Presentation, useR! 2014

2. 2
2007: The Beginning

3. 3
2008: Revolutions Blog

4. R in the News
2009
New York Times:
Data Analysts
Captivated by R’s
Power
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5. 5
2009
Revolution R
Enterprise
version 3
First R Debugging
IDE

6. 6
2010: User Group Sponsorships
141 R User Groups

7. Rows of data 1 billion 1 billion
Parameters “just a few” 7
Time 80 seconds 44 seconds
Data location In memory On disk
Nodes 32 5
Cores 384 20
RAM 1,536 GB 80 GB
Double
45%
1/6th
5%
5%
Revolution R is faster on the same amount of data, despite using approximately a 20th as many cores, a 20th as much RAM, a
6th as many nodes, and not pre-loading data into RAM.
Bottom Line: Revolution R Enterprise Performance = Greatly Reduced TCO
*As published by SAS in HPC Wire, April 21, 2011
Logistic Regression:
7
2010: Head to Head with SAS

8. 8
2011: RHadoop
github.com/RevolutionAnalytics/RHadoop

9. 2012: Clusters, Hadoop and Databases
Write Once  Deploy Anywhere
rxSetComputeContext("local") # DEFAULT
rxSetComputeContext(RxHadoopMR(<data, server environment arguments>))
# Summarize and calculate descriptive statistics from the data airDS data set
adsSummary <- rxSummary(~ArrDelay+CRSDepTime+DayOfWeek, data = airDS)
# Fit Linear Model
arrDelayLm1 <- rxLinMod(ArrDelay ~ DayOfWeek, data = airDS); summary(arrDelayLm1)
rxSetComputeContext(RxHpcServer(<data, server environment arguments>))
rxSetComputeContext(RxLsfCluster(<data, server environment arguments>))
Same code to be run anywhere …..
Local System
(default)




Set the desired compute context for code execution…..
rxSetComputeContext(RxTeradata(<data, server environment arguments>))


10. 10
2013
Shaking up the
industry
A Gartner Magic Quadrant
Visionary

11. 11
2014: Technical Support for Open Source R
AdviseR™ from Revolution Analytics
Technical support for open source R, from the R experts.
 10x5 email and phone support
 Support for R, validated packages, and third-party software
connections
 On-line case management and knowledgebase
 Access to technical resources, documentation and user forums
 Exclusive on-line webinars from community experts
 Guaranteed response times
Also available: expert hands-on and on-line training for R, from
Revolution Analytics AcademyR.
www.revolutionanalytics.com/AdviseR
www.revolutionanalytics.com/AcademyR
R SUPPORT
12 MONTHS
$795
PER USER

12. … and beyond!
Continued growth and demand for R
 R is the highest paid IT skill
– Dice.com, Jan 2014
 R most-used data science language after SQL
– O’Reilly, Jan 2014
 R is used by 70% of data miners
– Rexer, Sep 2013
 R is #15 of all programming languages
– RedMonk, Jan 2014
 R growing faster than any other data science
language
– KDnuggets, Aug 2013
 More than 2 million users worldwide
R Usage Growth
Rexer Data Miner Survey, 2007-2013
70% of data miners report using R
R is the first choice of more
data miners than any other
software
Source: www.rexeranalytics.com

13. Thank you
Revolution Analytics is the leading commercial
provider of software and support for the
popular open source R statistics language.
www.revolutionanalytics.com, 1.855.GET.REVO, Twitter: @RevolutionR
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