R in a Nutshell Reviewed By Manoj Rengarajan of Bookpleasures.com

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Manoj Rengarajan

**Reviewer Manoj
Rengarajan **holds a Master of Financial Engineering - University of
California, Berkeley and he works in the investment management
industry and specializes in providing economic and investment outlook
and strategy for global equity and government bond markets. He has an
educational background in financial engineering, business, and
engineering, and professional interests include business,
finance, economics, technology and related areas.

By Manoj Rengarajan

Published on November 15, 2010

Publisher:
O'Reilly

ISBN: 978-0-596-80170-0

R is a leading open source statistical analysis software that has gained acceptance both among academic and business users in the past several years.

R in a Nutshell is a concise but still comprehensive desktop reference to R.

Author: Joseph Adler

ISBN: 978-0-596-80170-0

R is a leading open source statistical analysis software that has gained acceptance both among academic and business users in the past several years.

R in a Nutshell is a concise but still comprehensive desktop reference to R.

Author: Joseph Adler

Publisher:
O'Reilly

ISBN: 978-0-596-80170-0

Click Here To Purchase R in a Nutshell: A Desktop Quick Reference

R
is a leading open source statistical analysis software that has
gained acceptance both among academic and business users in the past
several years.

R in a Nutshell is organized into four parts.
The first part covers the basics of the R language starting with how
to install R for different operating systems and details of the user
interface. Also, for Microsoft Excel users, there is guidance on how
to use R with Excel.

A key strength of R is the availability
of a wide range of freely available comprehensive packages. The book
explains how to take maximum advantage of the reusability
capabilities inherent in R.

Using R to its fullest requires
some knowledge of the language internals. The next section covers the
features of R as a programming language. Starting with an overview of
the language the book describes the R syntax, objects, symbols and
environment and functions.

In addition, advanced features of
the R language including support for object-oriented programming is
discussed clearly. Practical guidance for real life performance
issues when analyzing very large data sets is also covered in
detail.

A significant barrier for a new user is getting data
input into the system and getting output in the required format.
Also, a slightly experienced user will aim at connecting to the
system from different databases. With many sources of data available
online from the internet, the book gives an example of real time
retrieval of online data from Yahoo! Finance.

There is a
detailed part on the data preparation and visualization aspects for
analysis. While preparation of data possibly takes a significant
percent of the total time, many other books attach less importance in
dealing with data preparation issues.

The next two chapters
present an overview of data visualization features in R starting with
the basics and then followed by an extensive explanation of lattice
graphics.

While many of the books on using R make use of the
data sets that come as part of the software, the book has examples
outside the standard data sets which highlights the different
contexts in which R can be used.

A good coverage of basic
statistical concepts is provided which covers correlation,
covariance, probability distributions, techniques such as principal
components analysis, factor analysis, experimental design, and other
standard statistical tests.

The section on regression covers a
range of techniques including semi-parametric and nonparametric
methods as well machine learning algorithms and is well explained by
several examples. While the book’s emphasis is on R, there is
extensive explanation on interpreting the results of the statistical
tests.

Several data mining techniques for association and
clustering including logistic regression, linear discriminant,
classification tree, neural network and support vector machines are
covered in good detail.

The chapter on time series covers
standard models such as ARIMA.The final chapter deals with the
Bioconductor project, beginning with an example based on a data set
from the Gene Expression Omnibus website and build on this
example.

R in a Nutshell is a concise but still comprehensive
desktop reference to R. A key highlights of the book is that it
highlights the power of the software to handle statistical analysis
in different contexts using comprehensive data sets and code
examples. The book is strongly recommended for both for the novice as
well as the experienced user interested in serious data analysis.

Click Here To Purchase R in a Nutshell: A Desktop Quick Reference