There is one consistent message we hear from customers across industries and around the world: "We would like to reduce our reliance on SAS." In this webinar, we review the top reasons customers cite for moving fromSAS to R; the benefits of open source analytics; the challenges of switching; and the tools you will need to build your own roadmap. We review the key differences between SAS and R from the user's perspective, and provide you with the tools to move forward.

1. Moving from SAS to R
Thomas W. Dinsmore
Seth Mottaghinejad
August 7

2. Poll #1
What analytics platforms are you currently using?
(please check all that apply)
• R/RRE
• SAS
• SPSS
• Tibco/Spotfire
• Other
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Moving from SAS to R
Introduction
Why choose R over SAS?
Concerns about R
How we can help
How to Migrate from SAS to R
Customer Success
Questions

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R: Explosive Growth
Source: Rexer Data Miner Survey, 2007-2013
Use R
R is Primary Tool
70%

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Who uses R for analytics today?
5

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R Penetrates Enterprise Analytics
0
200
400
600
800
1,000
1,200
1,400
1,600
2014 2015 2016 2017 2018
Top 10 Global Bank – Planned Analytic Seats
SAS R

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Why choose R over
SAS?

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Your new analysts already know R.

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R is a comprehensive
analytics platform.

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R is flexible and customizable.

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R is free; SAS is expensive.

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Concerns about R

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“We don’t know what’s in the code.”

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“We’ll be on our own.”

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“R doesn’t scale.”

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“We have a lot of SAS programs.”

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How we can help:
“We don’t know what’s in the code.”
“We’ll be on our own.”
“R doesn’t scale.”
“We have a lot of SAS programs.”
Build Assurance
Technical Support
Distributed Platform
Migration Services

18. Are you currently exploring alternative analytics
platforms?
• Yes
• No
Poll #2

19. Migrating from SAS to R:
What you need to know
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About me
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 Stats background
 Over 5 years R and SAS
experience
 Worked on migrating SAS
projects to R
 Teach a class on R for SAS
users
 Passionate advocate
bringing R to industry

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You may be…
• New to R?
• Using SAS and
• think it is too expensive
• think it does not integrate well with other apps
• have a hard time attracting younger talent
• curious about R
• thinking of migrating to R
• in the process of migrating to R
• want to scale up to a Hadoop cluster or a traditional grid
• Migration from SAS to R is doable.

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SAS vs R and RRE
• Conceptually, SAS and R are very different
languages, yet you can leverage a lot of what you
know. Revolution Analytics has training and services
to help you learn to work in R.
• Often when switching from SAS to R we give up a
pre-packaged solution in return for a flexible, robust
solution that you can easily augment.
• Writing code that works in R is easy, but writing
production-level code in R is more difficult.
• Through its ScaleR package, RRE brings to the table
many of the features that make R an enterprise
solution.

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SAS vs R and RRE
• The explicit logic is the logic for implementing the
project independent of any specific code. The
implicit logic is the logic for implementing the project
based on reading and understanding the code.
• Can you diagram your project’s workflow (explicit
logic)?
• How much do you value adherence to the implicit
logic (SAS code)?
• If you had to rewrite the project, would you write the code in
the same way or is there room for simplifying?
• Was the project the work of a single person or did multiple
people work on it?

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Migrating from SAS: DATA Step
• Working with data (create, transform, and
reshape) is straightforward in R.
• If you use PROC SQL for many of your data
operations, you may be able to bring most of it
into R without having to rewrite it in R code,
although this may not be the most efficient way.
• You must act with caution in some cases, such
as missing data or formatting issues.
• How much data-processing do you perform?
• What sorts of data sets do you import/read from?

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Migrating from SAS: PROCs
• Except for a few notable cases (such as PROC
IMPORT/EXPORT, TRANSPOSE) most PROCs in
SAS are used to summarize data or to analyze data
(modeling and data-mining).
• Some statistical procedures may be implemented in a
slightly different way in SAS vs R, resulting in tiny
differences in computations. If such differences
matter, we can always build on an existing solution or
write our own. Creating a custom-made solution is
far more easier in R than in SAS.
• What statistical procedures do you use?
• Do you want to exactly replicate your SAS code?

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Migrating from SAS: Macros
• Macros in SAS are a “separate” language, built on top
of the SAS language. You write SAS code to
manipulate and analyze data, and you write macros
to manipulate the SAS code itself, with the intention
of automating the code.
• There is no “R macro language”: R is flexible enough
to also be its own macro language. However, in the
case of more advanced macros, this often means that
the converted R code will look very different from its
SAS counterpart.
• Do you use macros? If so, can you provide
examples?

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Migrating from SAS: ODS
• ODS is SAS’s way of exporting results to other
formats. It can be used as part of a pipeline or
for reporting.
• The fact that R is open-source makes it even
easier to create hookups to external formats
(Excel, PDFs, HTML, etc.), which results in even
more flexibility.
• Can you describe your pipeline/workflow?
• What formats do you export to? Who is the
target audience (e.g. internal vs external)?

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Migrating from SAS: What else?
• SAS has more components than covered so far, e.g.
SAS Enterprise Miner, SAS EG, SAS IML, add-ons
for design and analysis of experiments, and more.
• R’s ease of integration with third party tools makes it
very easy to extend its capability.
• What are some specific SAS features that you want
to migrate into R?
• What are some concerns you might have about
migrating?
• How are you preparing for the migration?

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Handover of the R project
• For a successful handover, we need to think of ways to test
the R code to ensure that it can replicate the SAS code.
• Documentation will be provided and some on-site
knowledge transfer may be needed.
• Training is also an option.
• How much of your data will you share?
• Is the data you will share representative of your “best data”
or your “worst data”?
• What are your success criteria?
• Who will be in charge of testing the code?
• How do you envisage the knowledge transfer?

30. In what scenario would you consider supplementing your
production analytics platform? (all that apply)
• New data environment (Hadoop)
• Research / Data Exploration
• Improve performance for production systems
• Lower costs for production systems
• New production application development
Poll #3

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Customer Success

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Old Solution New Solution

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Old Solution New Solution

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Old Solution New Solution

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Moving from SAS to R
Introduction
Why choose R over SAS?
Concerns about R
How we can help
How to Migrate from SAS to R
Customer Success
Questions

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37. Join us in September
Merkle will share their experience building a multi-channel
customer analytics system with RRE, Cloudera and
Tableau
You can register on the Revolution Analytics website next week.
23SEP14 at 10:00 AM, Pacific
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