Chapter 1, Acquire and Prepare the Ingredients – Your Data, covers the activities that precede the actual data analysis task. It provides recipes to read data from different input file formats. Furthermore, prior to actually analyzing the data, we perform several preparatory and data cleansing steps and the chapter also provides recipes for these: handling missing values and duplicates, scaling or standardizing values, converting between numerical and categorical variables, and creating dummy variables.

Chapter 2, What's in There? – Exploratory Data Analysis, talks about several activities that analysts typically use to understand their data before zeroing in on specific techniques to apply. The chapter presents recipes to summarize data, split data, extract subsets, and create random data partitions, as well as several recipes to plot data to reveal underlying patters using standard plots as well as the lattice and ggplot2 packages.

Chapter 3, Where Does It Belong? – Classification, covers recipes for applying classification techniques. It includes classification trees, random forests, support vector machines, Naïve Bayes, K-nearest neighbors, neural networks, linear and quadratic discriminant analysis, and logistic regression.

Chapter 4, Give Me a Number – Regression, is about recipes for regression techniques. It includes K-nearest neighbors, linear regression, regression trees, random forests, and neural networks.

Chapter 5, Can You Simplify That? – Data Reduction Techniques, covers recipes for data reduction. It presents cluster analysis through K-means and hierarchical clustering. It also covers principal component analysis.

Chapter 6, Lessons from History – Time Series Analysis, covers recipes to work with date and date/time objects, create and plot time-series objects, decompose, filter and smooth time series, and perform ARIMA analysis.

Chapter 7, It's All About Your Connections – Social Network Analysis, is about social networks. It includes recipes to acquire social network data using public APIs, create and plot social networks, and compute important network metrics.

Chapter 8, Put Your Best Foot Forward – Document and Present Your Analysis, considers techniques to disseminate your analysis. It includes recipes to use R markdown and KnitR to generate reports, to use shiny to create interactive applications that enable your audience to directly interact with the data, and to create presentations with RPres.

Chapter 9, Work Smarter, Not Harder – Efficient and Elegant R Code, addresses the issue of writing efficient and elegant R code in the context of handling large data. It covers recipes to use the apply family of functions, to use the plyr package, and to use data tables to slice and dice data.

Chapter 10, Where in the World? – Geospatial Analysis, covers the topic of exploiting R's powerful features to handle spatial data. It covers recipes to use RGoogleMaps to get GoogleMaps and to superimpose our own data on them, to import ESRI shape files into R and plot them, to import maps from the maps package, and to use the sp package to create and plot spatial data frame objects.

Chapter 11, Playing Nice – Connecting to Other Systems, covers the topic of interconnecting R to other systems. It includes recipes for interconnecting R with Java, Excel and with relational and NoSQL databases (MySQL and MongoDB respectively).

We have tested all the code in this book for R versions 3.0.2 (Frisbee Sailing) and 3.1.0 (Spring Dance). When you install or load some of the packages, you may get a warning message to the effect that the code was compiled for a different version, but this will not impact any of the code in this book.

This book is ideal for those who are already exposed to R, but have not yet used it extensively for data analytics and are seeking to get up and running quickly for analytics tasks. This book will help people who aspire to enhance their skills in any of the following ways:

In this book, you will find several headings that appear frequently (Getting ready, How to do it, How it works, There's more, and See also).

To give clear instructions on how to complete a recipe, we use these sections as follows:

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "The read.csv() function creates a data frame from the data in the .csv file."

A block of code is set as follows:

> names(auto)

[1] "No"           "mpg"          "cylinders"
[4] "displacement" "horsepower"   "weight"
[7] "acceleration" "model_year"   "car_name"

Any command-line input or output is written as follows:

export LD_LIBRARY_PATH=$JAVA_HOME/jre/lib/server
export MAKEFLAGS="LDFLAGS=-Wl,-rpath $JAVA_HOME/lib/server"

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "Clicking the Next button moves you to the next screen."

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To send us general feedback, simply e-mail <[email protected]>, and mention the book's title in the subject of your message.

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