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.
The R Cookbook is aimed as
a task oriented collection of solutions to enhance the productivity
of a R user significantly. Getting started with R deals with
downloading and installing the R system with pointers on how to
efficiently navigate the R system. There are recipes on how to get
help from the system and how to effectively use the resources
available on the web.
With respect to the basics of the language, the book starts with handling variables and working on simple functions to set, list and deleting variables. Also, there are tips on operations with vectors, computing basic statistics and operator precedences and how to use functions.
Navigating the R system deals with a set of fundamental tips for handling the R workspace including setting and retrieving the working directory, getting the command history, installing packages, running scripts and batch scripts, and to work with environment variables. These tips are useful not just for the beginner but also for the experienced user who wants to use these features in a more professional setting.
Input/ Output is one of the key bottlenecks for using a programming language. The section on this key aspect gives an array of examples from simple reading and writing to files to dealing with error conditions, handling files with complex structures and how to access databases such as MySQL from the R system.
Several recipes for data structures support in R are illustrated with commonly used operations in standard data structures such as vectors and lists. Handling matrices and data frames are given their due importance given the heavy use of these structures in R programming.
Data manipulation is demonstrated in the context of applying functions to a subset of a data structure. With many users having a need to manipulate strings and dates, there is a set of recipes which deal with handling strings and dates which most readers should find very helpful.
The chapters on probability and general statistics showcase the strength of the R statistical system and how it can be used. Calculating probability of discrete and continuous distributions, converting data to z-scores, testing for normality, testing correlation for significance and other commonly used statistical tests are demonstrated.
Linear regression and ANOVA techniques are demonstrated with examples of simple and multiple linear regression, and use of regression and summary statistics.Commonly used operations for doing time series analysis including creation and plotting, merge, filling, lagging, moving averages, auto correlation and ARIMA modules are covered.
Graphics is another key strengths of the R system. The chapter on graphics shows how to use several commonly used charts such as the scatter plot, bar charts, histograms, and how to chart a function.
In the final part of the book, a set of very useful tricks for analyzing data including visualization, row and column handling, timing codes and suppressing warnings and error messages are given which completes the wide array of tools the book touches upon.
The R Cookbook lives up to the expectation of being a practical recipe book for data analysis using the R system and more. The range of recipes spans across most commonly and more advanced level tasks that should appeal to both the beginner as well as the more advanced user. The book should be a great resource for data analysts who use R on a day to day basis.