Overview

Develop informative and aesthetic visualizations that enable effective data analysis in less time Key Features Discover structure of ggplot2, grammar of graphics, and geometric objects Study how to design and implement visualization from scratch Explore the advantages of using advanced plots Book DescriptionApplied Data Visualization with R and ggplot2 introduces you to the world of data visualization by taking you through the basic features of ggplot2. To start with, you’ll learn how to set up the R environment, followed by getting insights into the grammar of graphics and geometric objects before you explore the plotting techniques. You’ll discover what layers, scales, coordinates, and themes are, and study how you can use them to transform your data into aesthetical graphs. Once you’ve grasped the basics, you’ll move on to studying simple plots such as histograms and advanced plots such as superimposing and density plots. You’ll also get to grips with plotting trends, correlations, and statistical summaries. By the end of this book, you’ll have created data visualizations that will impress your clients. What you will learn Set up the R environment, RStudio, and understand structure of ggplot2 Distinguish variables and use best practices to visualize them Change visualization defaults to reveal more information about data Implement the grammar of graphics in ggplot2 such as scales and faceting Build complex and aesthetic visualizations with ggplot2 analysis methods Logically and systematically explore complex relationships Compare variables in a single visual, with advanced plotting methods Who this book is forApplied Data Visualization with R and ggplot2 is for you if you are a professional working with data and R. This book is also for students who want to enhance their data analysis skills by adding informative and professional visualizations. It is assumed that you know basics of the R language and its commands and objects.

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9781789612158

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Dr. Tania Moulik has a PhD in particle physics. She worked at CERN and the Tevatron at Fermi National Accelerator Laboratory in IL, USA. She has worked with C++, Python, and R. She also worked in big data projects and grid computing. She has a passion for data analysis and likes to share her knowledge with others who would like to delve into the world of data analytics. She especially likes R and ggplot2 as a powerful analytics package.

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