Tools for Business Analytics
MSBA 320
Dr. Siamak Zadeh
Adjunct Professor
Ageno School of Business
Golden Gate University

Introduction to R: R Basics

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Outline
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Why R, and R Paradigm
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References, Tutorials and links
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R Overview
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R Interface
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R Workspace
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Help
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R Packages
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Input/Output
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Reusing Results

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Why R?
It's free!
It runs on a variety of platforms including Windows, Unix and
MacOS.
It provides an unparalleled platform for programming new
statistical methods in an easy and straightforward manner.
It contains advanced statistical routines not yet available in
other packages.
It has state-of-the-art graphics capabilities.

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R has a Steep
Learning Curve
(steeper for those that knew SAS or other software
before)
First
, while there are many introductory tutorials (covering data
types, basic commands, the interface), none alone are
comprehensive. In part, this is because much of the advanced
functionality of
R
comes from hundreds of user contributed
packages. Hunting for what you want can be time consuming,
and it can be hard to get a clear overview of what procedures
are available.

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R has a Learning Curve
(steeper for those that knew SAS or other software before)
The
second
reason is more transient. As users of statistical
packages, we tend to run one controlled procedure for each
type of analysis. Think of PROC GLM in SAS. We can carefully
set up the run with all the parameters and options that we
need. When we run the procedure, the resulting output may
be a hundred pages long. We then sift through this output
pulling out what we need and discarding the rest.

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R paradigm is different
Rather than setting up a complete analysis at once, the process
is highly interactive. You run a command (say fit a model),
take the results and process it through another command
(say a set of diagnostic plots), take those results and process
it through another command (say cross-validation), etc. The
cycle may include transforming the data, and looping back
through the whole process again. You stop when you feel that
you have fully analyzed the data.

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How to download?
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Google it using R or CRAN
(Comprehensive R Archive Network)
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Tutorials
Each of the following tutorials are in PDF format.
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P. Kuhnert & B. Venables,
An Introduction to R: Software for
Statistical Modeling & Computing
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J.H. Maindonald,
Using R for Data Analysis and Graphics
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B. Muenchen,
R for SAS and SPSS Users
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W.J. Owen,
The R Guide
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D. Rossiter,
Introduction to the R Project for Statistical
Computing for Use at the ITC
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W.N. Venebles & D. M. Smith,
An Introduction to R