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R Data Visualization Recipes

R Data Visualization Recipes

By : Bianchi Lanzetta
4 (1)
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R Data Visualization Recipes

R Data Visualization Recipes

4 (1)
By: Bianchi Lanzetta

Overview of this book

R is an open source language for data analysis and graphics that allows users to load various packages for effective and better data interpretation. Its popularity has soared in recent years because of its powerful capabilities when it comes to turning different kinds of data into intuitive visualization solutions. This book is an update to our earlier R data visualization cookbook with 100 percent fresh content and covering all the cutting edge R data visualization tools. This book is packed with practical recipes, designed to provide you with all the guidance needed to get to grips with data visualization using R. It starts off with the basics of ggplot2, ggvis, and plotly visualization packages, along with an introduction to creating maps and customizing them, before progressively taking you through various ggplot2 extensions, such as ggforce, ggrepel, and gganimate. Using real-world datasets, you will analyze and visualize your data as histograms, bar graphs, and scatterplots, and customize your plots with various themes and coloring options. The book also covers advanced visualization aspects such as creating interactive dashboards using Shiny By the end of the book, you will be equipped with key techniques to create impressive data visualizations with professional efficiency and precision.
Table of Contents (13 chapters)
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Making simple maps - 1854 London Streets


 Plotting an entire country, district, lakes and forests can be seen as a task for polygons while plotting streets, roads, and rivers a quest for paths to handle. Really informative maps would request much more than this recipe is going to teach but we're getting there. This recipe is going to show you how to plot some 1854 London streets.

Do not get anxious, maps are pretty cool and there is plenty of stuff to learn. Step by step, this chapter will introduce you to a lot of useful ones. This recipe's intention is to show you how maps can simply be translated into paths (and polygons); later, we will be improving this very map to get a result very similar to the one that John Snow (the doctor, not the ranger) breed.

Getting ready

Besides the regulars (ggplot2, ggvis, and plotly), the king (the king being this recipe) demands the HistData package:

> if( !require(HistData)){ install.packages('HistData')}

For now, we're still relying on packages to bring...

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