Preface

Our ability to generate data has improved tremendously with the advent of technology. The data generated has become more complex with the passage of time. The complexity in data forces us to develop new tools and methods to analyze it, interpret it, and communicate with the data. Data visualization empowers us with the necessary skills required to convey the meaning of underlying data. Data visualization is a remarkable intersection of data, science, and art, and this makes it hard to define visualization in a formal way; a simple Google search will prove me right. The Merriam-Webster dictionary defines visualization as "formation of mental visual images". In reality, the term visualization goes beyond the limits of providing visual images by assisting humans in data recording, revealing pattern, exploration of data, and spreading information in a meaningful way.

Jer Thorpe in an interview with Mashable.com (http://mashable.com/2012/12/11/data-visualization-jer-thorp/) introduces the idea of humanizing data:

"…And I think that there's a huge possibility for humans, society as a whole—if we could share that data more usefully, for science and for the construction of cities, and for all these kinds of things, then it becomes much more useful. So in my work, I'm really thinking about how we can give people glimpses into that type of future. Giving people an opportunity to think about data ownership or giving people a visualization so that they can see the kinds of things that can be done with data".

R is an open source platform used to analyze data. It has been widely used as a statistical tool in the past. An individual does not necessarily have to be a programmer to use R. A beginner can use basic R functionalities to manipulate and extract data and create very simple and quick visualizations using the basic graphic tools. An intermediate R user can implement interactive visualizations, perform predictive modeling, or even create animated applications using packages developed by the R community. R will present you with the tools you need to process, manipulate, and communicate with your data, and it is not just limited to statistical analysis.

In this book, you will learn how to generate basic visualizations, understand the limitations and advantages of using certain visualizations, develop interactive visualizations and applications, understand various data exploratory functions in R, and finally learn ways of presenting the data to our audience. This book is aimed at beginners and intermediate users of R who would like to go a step further in using their complex data to convey a very convincing story to their audience.