Presenter: David Smith Presented to SURF (Sydney R User Group), June 25 2015

2. • Introduction to R
• Applications of R at Microsoft
• R Products at Microsoft
• What’s coming for R at Microsoft
• Q&A

3. April 6, 2015
“This acquisition will help customers use advanced analytics within Microsoft data platforms.“

4. INTRODUCTION
TO R

5. • Most widely used data analysis software
• Most powerful statistical programming language
• Create beautiful and unique data visualizations
• Thriving open-source community
• Fills the talent gap
www.revolutionanalytics.com/what-is-r

7. • 1993: Research project in Auckland, NZ
• 1995: Released as open-source software
• 1997: R core group formed
• 2000: R 1.0.0 released
• 2003: R Foundation formed in Austria
• 2004: First international user conference
• 2007: Revolution Analytics founded
• 2009: New York Times article on R
• 2013: Revolution R Open released
• 2015: Microsoft acquires Revolution
Analytics 7
Photo credit: Robert Gentleman

8. 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

9. New York Times, June 25 2009
(3 hours after Michael Jackson’s death)

10. R AT
MICROSOFT

11. What
happened?
Why did
it happen?
What will
happen?
How can we
make it happen?
Traditional BI Advanced Analytics

12. • System monitoring & alerting
• Capacity Planning

13. • TruSkill Matchmaking System
• Player Churn
• Game design
• In-game purchase optimization
• Fraud detection
• Player communities

14. MICROSOFT
PRODUCTS
WITH R

15. • 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
15

16. • Built on latest R engine
• 100% compatible with
• Designed to work with RStudio
16

17. • Multithreaded library replaces
standard BLAS/LAPACK algorithms
• High-performance algorithms
• Sequential  Parallel
• No need to change any R code
• Included with RRO binary
distributions
17
More at Revolutions blog

18. Adapted from http://xkcd.com/234/
CC BY-NC 2.5

19. • Static CRAN mirror
• Daily CRAN snapshots
mran.revolutionanalytics.com/snapshot
• Easily write and share scripts synced to a specific snapshot
19
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

20. • Easy to use: add 2 lines to the top of each script
• For the package author:
• For a script collaborator:
20

21. • Download
Revolution R Open
• Learn about R and
RRO
• Daily CRAN
snapshots
• Explore Packages
• Explore Task Views
21

22. Trends

23. R FOR
BIG DATA

24. • Toolkits for data scientists and numerical analysts to create custom
parallel and distributed algorithms
• Mainly useful for “embarrassingly parallel” problems, where
parallel components work with small amounts of data
• Big Data Predictive Analytics mostly not embarrassingly parallel
Details at projects.revolutionanalytics.com
24

25. is….
the only big data big analytics platform
based on open source R
the defacto statistical computing language for
modern analytics

27.  Naïve Bayes
 Data import – Delimited, Fixed, SAS, SPSS,
OBDC
 Variable creation & transformation
 Recode variables
 Factor variables
 Missing value handling
 Sort, Merge, Split
 Aggregate by category (means, sums)
 Min / Max, Mean, Median (approx.)
 Quantiles (approx.)
 Standard Deviation
 Variance
 Correlation
 Covariance
 Sum of Squares (cross product matrix for set
variables)
 Pairwise Cross tabs
 Risk Ratio & Odds Ratio
 Cross-Tabulation of Data (standard tables & long
form)
 Marginal Summaries of Cross Tabulations
 Chi Square Test
 Kendall Rank Correlation
 Fisher’s Exact Test
 Student’s t-Test
 Subsample (observations & variables)
 Random Sampling
Data Step Statistical Tests
Sampling
Descriptive Statistics
 Sum of Squares (cross product matrix for set
variables)
 Multiple Linear Regression
 Generalized Linear Models (GLM) exponential
family distributions: binomial, Gaussian, inverse
Gaussian, Poisson, Tweedie. Standard link
functions: cauchit, identity, log, logit, probit. User
defined distributions & link functions.
 Covariance & Correlation Matrices
 Logistic Regression
 Classification & Regression Trees
 Predictions/scoring for models
 Residuals for all models
Predictive Models  K-Means
 Decision Trees
 Decision Forests
 Gradient Boosted Decision Trees
Cluster Analysis
Classification
Simulation
Variable Selection
 Stepwise Regression
 Simulation (e.g. Monte Carlo)
 Parallel Random Number Generation
Combination
New in
v7.3
 PEMA-R API
 rxDataStep
 rxExec
Coming
in v7.4

29. • ETL
• Marketing channel data
• Behavioral variables
• Promotional data
• Overlay data
• Exploratory data analysis
• Time-to-event models
• GAM survival models
• Scoring for inference
• Scoring for prediction
• 5 billion scores per day
per retailer
CUSTOM DATA
FORMAT
CUSTOM VARIABLES
(PMML)

30. R IN THE CLOUD

31. • Exposing the expertise of data scientists as APIs
• Bringing the utility of data science to applications
• Addressing the Data Science talent gap

32. Azure: Huge infrastructure scale
19 Regions ONLINE…huge datacenter capacity around the world…and we’re growing
 100+ datacenters
 One of the top 3 networks in the world (coverage, speed, connections)
 2 x AWS and 6x Google number of offered regions
 G Series – Largest VM available in the market – 32 cores, 448GB Ram, SSD…
Operational Announced
Central US
Iowa
West US
California
North Europe
Ireland
East US
Virginia
East US 2
Virginia
US Gov
Virginia
North Central US
Illinois
US Gov
Iowa
South Central US
Texas
Brazil South
Sao Paulo
West Europe
Netherlands
China North *
Beijing
China South *
Shanghai
Japan East
Saitama
Japan West
OsakaIndia West
TBD
India East
TBD
East Asia
Hong Kong
SE Asia
Singapore
Australia West
Melbourne
Australia East
Sydney
* Operated by 21Vianet

36. http://blog.revolutionanalytics.com/2015/06/r-build-keynote.html/

37. WHAT’S
COMING FOR R
AT MICROSOFT

38. 40

39. Data Scientist
Interact directly with data
Built-in to SQL Server
Data Developer/DBA
Manage data and
analytics together
SQL Server 2016
Built-in in-database analytics
Example Solutions
• Fraud detection
• Salesforecasting
• Warehouse efficiency
• Predictive maintenance
Relational Data
Analytic Library
T-SQL Interface
Extensibility
?
R
RIntegration
010010
100100
010101
Microsoft Azure
Machine Learning Marketplace
New R scripts
010010
100100
010101
010010
100100
010101
010010
100100
010101
010010
100100
010101
010010
100100
010101

40. rows
minutes
R on a
server
pulling data
via SQL
R on a server
Invoking RRE
ScaleR Inside
the EDW

42. Thank you
Download Revolution R Open:
mran.revolutionanalytics.com
More at:
blog.revolutionanalytics.com
David Smith
R Community Lead
Revolution Analytics
@revodavid
davidsmi@microsoft.com

44. 46
More at deployr.revolutionanalytics.com