#### Overview of this book

Data analytics with R has emerged as a very important focus for organizations of all kinds. R enables even those with only an intuitive grasp of the underlying concepts, without a deep mathematical background, to unleash powerful and detailed examinations of their data. This book will show you how you can put your data analysis skills in R to practical use, with recipes catering to the basic as well as advanced data analysis tasks. Right from acquiring your data and preparing it for analysis to the more complex data analysis techniques, the book will show you how you can implement each technique in the best possible manner. You will also visualize your data using the popular R packages like ggplot2 and gain hidden insights from it. Starting with implementing the basic data analysis concepts like handling your data to creating basic plots, you will master the more advanced data analysis techniques like performing cluster analysis, and generating effective analysis reports and visualizations. Throughout the book, you will get to know the common problems and obstacles you might encounter while implementing each of the data analysis techniques in R, with ways to overcoming them in the easiest possible way. By the end of this book, you will have all the knowledge you need to become an expert in data analysis with R, and put your skills to test in real-world scenarios.
Preface
Free Chapter
Acquire and Prepare the Ingredients - Your Data
Lessons from History - Time Series Analysis
How does it look? - Advanced data visualization
This may also interest you - Building Recommendations
It's All About Your Connections - Social Network Analysis
Put Your Best Foot Forward - Document and Present Your Analysis
Work Smarter, Not Harder - Efficient and Elegant R Code
Playing Nice - Connecting to Other Systems

# Using the split-apply-combine strategy with plyr

A common analytical pattern is to split data into pieces, apply some function to each piece, and then combine the results back together. The plyr package provides simple functions to apply this pattern, while simplifying the specification of the object types through systematic naming of the functions.

The plyr function name has three parts, XYply, where X specifies what sort of input you're giving , Y specifies the sort of output you want and ply part is common to all function names. X and Y represent one of the following options:

• a = array
• d = data.frame
• l = list
• _ = no output; only valid for Y; for example, useful when you're operating
on a list purely for the side effects, making a plot, or sending output to screen/file

ddply has its input and output as data frames, and ldply takes a
list as input and produces a data...