R users already know why the R language is the lingua franca of statisticians today: because it's the most powerful statistical language in the world. Revolution Analytics builds on the power of open source R, and adds performance, productivity and integration features to create Revolution R Enterprise. In this webinar, author and blogger David Smith will introduce the additional capabilities of Revolution R Enterprise. VP of Product Development, Dr. Sue Ranney will also provide an overview of the features introduced in Revolution R Enterprise 6.0 including: 1. Big Data Generalized Linear Model, the new RevoScaleR function that provides a fast, scalable, distributable implementation of generalized linear models, offering impressive speed-ups relative to glm on in-memory data frames 2. Platform LSF Cluster Support, which allows you to create a distributed compute context for the Platform LSF workload manager 3. Azure Burst support added to RxHpcServer 4. Updated R engine (R 2.14.2) 5. Ability to use RevoScaleR analysis functions with non-xdf data sources such as SAS, SPSS or text 6. New methods for RxXdfData data sources including head, tail, names, dim, colnames, length, str, and formula 7. New function rxRoc for generating ROC curves

1. Revolution Confidential
R evolution R E nterpris e 6
100% R and More
P res ented by:
David S mith
V P Marketing and C ommunity
S ue R anney
V P P roduct Management
Revolution Confidential

2. Revolution Confidential
P oll Ques tion
Which stats package do you use
most?

3. In today’s webc as t: Revolution Confidential
 About Open-Source R and Revolution R
Enterprise
 What’s New in Revolution R Enterprise 6
 Resources, Q&A
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4. What is R ? Download the White PaperConfidential
R is Hot
Revolution
bit.ly/r-is-hot
 Data analysis software
 A programming language
 Development platform designed by and for statisticians
 An environment
 Huge library of algorithms for data access, data
manipulation, analysis and graphics
 An open-source software project
 Free, open, and active
 A community
 Thousands of contributors, 2 million users
 Resources and help in every domain
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5. From: The R Ecosystem
R Us er C ommunity Revolution Confidential
bit.ly/R-ecosystem
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6. R evolution R E nterpris e is Revolution Confidential
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7. R P roduc tivity E nvironment (Windows )
Revolution Confidential
Script with type
ahead and code Solutions window
snippets for organizing
code and data
Sophisticated
debugging with
breakpoints , variable Objects
values etc. loaded in the
R
Environment
Packages Object
installed and details
loaded
http://www.revolutionanalytics.com/demos/revolution-productivity-environment/demo.htm
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8. P erformanc e: Multi-threaded Math Revolution Confidential
Open Revolution R
Source R Enterprise
Computation (4-core laptop) Open Source R Revolution R Speedup
Linear Algebra1
Matrix Multiply 176 sec 9.3 sec 18x
Cholesky Factorization 25.5 sec 1.3 sec 19x
Linear Discriminant Analysis 189 sec 74 sec 3x
General R Benchmarks2
R Benchmarks (Matrix Functions) 22 sec 3.5 sec 5x
R Benchmarks (Program Control) 5.6 sec 5.4 sec Not appreciable
1. http://www.revolutionanalytics.com/why-revolution-r/benchmarks.php
2. http://r.research.att.com/benchmarks/
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9. A c ommon analytic platform ac ros s big
data arc hitec tures Revolution Confidential
File Based
Hadoop In-database
Cluster
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10. R evoS c aleR on Dis tributed C omputing C lus ters
Revolution Confidential
(Windows HP C S erver, P latform L S F )
Compute
Node
Data
Partition
Compute
Data Node
Partition
BIGData
Master
Node
Partition Compute
DATA Node
Data
Partition
Compute
Node
 Data Step, Statistical Summary, Tables/Cubes, Covariance,
Linear & Logistic Regression, GLM, K-means clustering, …
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11. S c alable dis tributed c omputing with
R evolution R E nterpris e and Hadoop Revolution Confidential
Map-Reduce
RHadoop: http://bit.ly/RHadoop 11

12. In-Databas e E xec ution with IB M Netezza Revolution Confidential
More info: http://bit.ly/R-Netezza
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13. E nterpris e-Wide Deployment Revolution Confidential
Production Research and Development
Revolution R Enterprise Server
+ Hadoop
+ IBM Netezza Data Scientists / Modelers
+ Server cluster
Management End-User Deployment
Console
Excel Web BI
RevoDeployR Server App
Web Services API
Analysts / Corporate Users
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14. Revolution Confidential
 On-Call Technical Support
 Consulting
 Migration | Analytics | Applications | Validation
 Training
 R | Revolution R | Statistical Topics
 Systems Integration
 BI | ERP | Databases | Cloud
www.revolutionanalytics.com/services 14

15. Why R evolution R ? Open-Source
R
RRE6
Workstation
RRE6
Revolution Confidential
Server
Interface with multiple data sources ✓ ✓✓ ✓✓
Exploratory data analysis ✓✓ ✓✓ ✓✓
Wide range of statistical methods ✓✓ ✓✓ ✓✓
Parallel Programming ✓ ✓ ✓✓
Multi-threaded performance ✘ ✓ ✓✓
Big Data Analytics ✘ ✓ ✓✓
Distributed Analytics (Grid / Cluster) ✘ Client ✓✓
Cloud Computing ✘ ✘ ✓✓
Hadoop Integration ✘ Client ✓✓
IBM Netezza Integration ✘ Client ✓✓
Multi-user support ✘ ✘ ✓✓
Scheduled, monitored batch production ✘ ✘ ✓✓
Secure code deployment, management ✘ ✘ ✓✓
Integration into Data Apps ✘ ✘ ✓✓
http://www.revolutionanalytics.com/why-revolution-r/which-r-is-right-for-me.php 15

16. Revolution Confidential
P oll Ques tion
What’s most important to you
about Revolution R Enterprise?

17. Revolution Confidential
What’s new in
R evolution R E nterpris e 6
P res ented by:
S ue R anney
V P P roduct Development
Revolution Confidential

18. R evolution R E nterpris e 6 Revolution Confidential
 Key Areas of Enhancements
 Latest stable release of open-source R (2.14.2)
 High Performance Analytics: Fast, scalable,
distributable, full-featured analysis of huge data
sets
 High Performance Computing: Run arbitrary R
functions in parallel across cores or nodes of a
cluster

19. R 2.14.2 Revolution Confidential
 Incorporation of ‘parallel’ as base package
 ‘foreach’ users can use doParallel backend
 Users of RevoScaleR’s ‘rxExec’ HPC function can
use new compute contexts to run arbitrary R
functions in parallel
 Compute context for the ‘parallel’ package
 Compute context for any ‘foreach’ backend
 Standard functions and packages in R are pre-
compiled into byte-code using ‘compiler’
package
 The benefit in speed depends on the specific
function but code’s performance can improve by a
factor of 2x times or more.

20. High P erformanc e A nalytic s (HPA ) in
R evoS c aleR Revolution Confidential
 High Performance Computing + Data
 Full-featured, fast, and scalable analysis
functions
 Same code works on small and big data
 Same code works on a variety of compute
contexts - a laptop, server, cluster, or the cloud
 Scales approximately linearly with the number
of observations – without increasing memory
requirements
Revolution R Enterprise 20

21. Direc tly A nalyze E xternal Data S ets with
R evoS c aleR HPA F unc tions NE W Revolution Confidential
 The RevoScaleR package provides easy ways to
directly access and analyze external data sets (data
sources)
 Delimited ASCII
 Fixed format ASCII
 SAS data sets (.sas7bdat)
 SPSS data sets (.sav)
 ODBC connections
 No need to have SAS or SPSS installed to access
data in SAS or SPSS file formats.
 Get started on analyses without first importing data
 Still have the option of importing into efficient .xdf file
format
Revolution R Enterprise 21

22. R evoS c aleR : HPA A lgorithms Revolution Confidential
 Descriptive statistics (rxSummary)
 Tables and cubes (rxCube, rxCrossTabs)
 Correlations/covariances (rxCovCor, rxCor,
rxCov, rxSSCP)
 K means clustering (rxKmeans)
 Linear regressions (rxLinMod)
 Logistic regressions (rxLogit)
 Generalized Linear Models (rxGlm) NEW!
 Predictions (scoring) (rxPredict)
Revolution R Enterprise 22

23. Tips for Handling B ig Data in R Revolution Confidential
 Use algorithms that process data in chunks.
 The functions provided with RevoScaleR are
scalable because they process data in ‘chunks.’
 If the number of observations doubles, you can still
perform the same data analyses with the same
amount of memory – it will just take longer
 Use functions optimized for big data
 The implementations of RevoScaleR analysis
algorithms are all optimized for handling big data.
 RevoScaleR analysis functions provide significant
speed improvements over alternatives, even if you
can fit all of your data in memory.
Revolution R Enterprise 23

24. Revolution Confidential
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25. B eyond In-Memory Data A nalys is Revolution Confidential
 RevoScaleR functions can read from data sets on disk in
chunks, so you can increase the number of observations in
the data set beyond what can be analyzed in memory all at
once
 RevoScaleR analysis functions process chunks of data in
parallel, taking greater advantage of your computing
resources (Parallel External Memory Algorithms)
 Multiple cores on a desktop/server
 Cluster/grids have added advantage of more hard drives
for storing & accessing data
 Windows HPC Server Cluster
 “Burst” computations to Azure in the cloud NEW
 IBM Platform LSF Grid NEW
Revolution R Enterprise 25

26. ‘B ig Data’ G eneralized L inear Models NE W Revolution Confidential
 Relaxes the assumptions for a standard
linear model.
 Used in insurance, finance, biotech, and
other industries.
 Example 1: Count data (Poisson)
 Number of vehicles an auto policy holder owns
 Number of credit cards a person holds
 Number of bacterial colonies in a Petri dish
Revolution R Enterprise 26

27. Revolution Confidential
G L M: Other E xamples
 Example 2: Positive values with positive
skew (Gamma)
 Value of auto insurance claims for claims filed
 Example 3: Positive data that also contains
exact zeros (Tweedie Model)
 Data on insured vehicles (claims amount is zero
for many vehicles; range of positive claims
values for others)
 Rainfall data
Revolution R Enterprise 27

28. Revolution Confidential
Quic k Demo Inc orporating rxG L M
 Use 5% Sample of the U.S. 2000 Census to
look at annual property insurance premiums
 Data manipulations: sub-sample data and
modify categorical data
 Perform summary statistics; draw histogram
 Estimate a Tweedie model using rxGlm
 Estimate predictions for targeted demographic
characteristics
 Visualize the results
 Analyze bigger model using a cluster
Revolution R Enterprise 28

29. C loud C omputing with A zure B urs t NE WRevolution Confidential
 Windows Azure is a cloud platform that
enables you to manage computations across
a global network of Microsoft-managed
datacenters
 Revolution R Enterprise 6.0 can burst
computations to Windows Azure from
Windows HPC Server
 Particularly suited to parallel HPC such as
simulations
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30. A S imple S imulation E xample Revolution Confidential
 For each run:
 Generate data with a known distribution
(Using code that accompanies the article "Pure Premium
Regression with the Tweedie Model" by Glenn Meyers,
Actuarial Review, May 2009 )
 Estimate the model using rxGLM
 Compare the means of the estimated coefficients with
the known parameters of the underlying distribution
 Do a small number of runs locally
 Do a large number of runs ‘bursting’ to the Azure cloud
(monitor jobs with HPC Job Scheduler, just as with on-
premises nodes)
Revolution R Enterprise 30

31. Revolution Confidential
P oll Ques tion
What new feature of Revolution R
Enterprise 6 is most interesting to
you?

32. T hank You! Revolution Confidential
 Download slides, replay from today’s webinar
 http://bit.ly/z9xUG9
 Learn more about Revolution R Enterprise
 Overview: revolutionanalytics.com/products
 New feature videos:
http://www.revolutionanalytics.com/products/new-features.php
 Contact Revolution Analytics
 http://bit.ly/hey-revo
June 28: Achieving High-Performing, Simulation-Based
Operational Risk Measurement with RevoScaleR
David Humke, Vice President, The Northern Trust Company
www.revolutionanalytics.com/news-events/free-webinars
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33. Revolution Confidential
The leading commercial provider of software and support for the
popular open source R statistics language.
www.revolutionanalytics.com
+1 (650) 646 9545
Twitter: @RevolutionR
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