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.
Chapter 1, A Simple Guide to R, is a quick tutorial on getting started with R. You will learn how to install packages, access help on R, construct and edit matrices, create and manipulate data frames, and write and save plots.
Chapter 2, Basic and Interactive Plots, introduces some of the basic R plots, such as scatter, line, and bar charts. We will also discuss the basic elements of interactive plots using the googleVis package in R. This chapter is a great resource for understanding the basic R plotting techniques.
Chapter 3, Heat Maps and Dendrograms, starts with a simple introduction to dendrograms and further introduces the concept of clustering techniques. The second half of this chapter discusses heat maps and integrating heat maps with dendrograms to get a more complete picture.
Chapter 4, Maps, discusses the importance of spatial data and various techniques used to visualize geographic data in R. You will learn how to generate static as well as interactive maps in R. The chapter discusses the topic of shape files and how to use them to generate a cartogram.
Chapter 5, The Pie Chart and Its Alternatives, is a detailed discussion on how to generate pie charts in R. You will also learn about the various criticisms of pie charts and how the pie chart is transformed to overcome them. The chapter also provides you with various alternatives used by data scientists and visualization artists to overcome the limitation of a pie chart.
Chapter 6, Adding the Third Dimension, dives into constructing 3D plots. This chapter also introduces packages such as rgl and animation, which are used to create interactive 3D plots.
Chapter 7, Data in Higher Dimensions, demonstrates the use of visualizations that are used to display data in higher dimension. You will learn the techniques to generate sunflower plots, hexbin plots, Chernoff faces, and so on. This chapter also discusses the usefulness of network, radial, and coxcomb plots, which have been widely used in news.
Chapter 8, Visualizing Continuous Data, illustrates the use of visualizations to display time series data. The chapter also discusses some general concepts related to visualizing correlations, the shape of the distribution, and detection of outliers using box and whisker plots.
Chapter 9, Visualizing Text and XKCD-style Plots, illustrates the use of text in creating effective visualizations. This chapter focuses mainly on techniques to create word clouds, phase tree, and comparison clouds in R. You will also learn how to use the XKCD package to introduce humor in visualizations.
Chapter 10, Creating Applications in R, shows you the techniques to create presentations and R markdown documents for publishing on a blog or a website. The chapter further discusses the XML package used to extract and visualize data as well as using shiny package used to create interactive applications.
You need to download R to generate the visualizations. You can download and install R using the CRAN website available at http://cran.r-project.org/. All the recipes were written using RStudio. RStudio is an integrated development environment (IDE) for R and can be downloaded from http://www.rstudio.com/products/rstudio/. Many of the visualizations are created using R packages and they are discussed in their respective recipes.
In few of the recipes, I have introduced users to some other open source platforms such as ScapeToad, ArcGIS, and Mapbox. Their installation procedures are outlined in their respective recipes.
Having studied economics, I am not a software programmer myself and have written this book for readers new to R and visualization. This book does not delvento complex R code or complex data manipulating techniques, and it is written keeping in mind new and intermediate R users interested in learning about data visualization and data exploration techniques.
The book aims at teaching you the implementation of interactive and animated data visualizations and not just the basic R techniques. However, I have introduced some basic functionalities in Chapter 1, A Simple Guide to R and Chapter 2, Basic and Interactive Plots.
Wherever possible, I have provided references to websites, blogs, and journals, which can be explored to learn more about specific functions, graphics, animations, or even basic functionalities in R.
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:
This section tells you what to expect in the recipe, and describes how to set up any software or any preliminary settings required for the recipe.
This section usually consists of a detailed explanation of what happened in the previous section.
This section consists of additional information about the recipe in order to make the reader more knowledgeable about the recipe.
In this book, you will find a number of styles of text 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: "We have used the png() function to save the plot as a PNG."
Any command line code is written as:
k = matrix((1:4),2,2) l = matrix((5:10),2,3) dim(k) dim(l)
In R it is a general practice to use <- for assignment instead of the = sign. In all the recipes, I have followed the = sign for assignment. You should note that if you refer to blogs or websites related to R, you may encounter the <- sign in the code files.
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: "In order to write a simple function in R, we must first open a new R script by navigating to File | New file."
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