This book introduces you to the world of data visualization by talking about the basic features of ggplot2. Learn all about setting up the R environment and then begin exploring features of ggplot2. Grammar of graphics and geometric objects are the fundamentals you must know before you dive deep into the plotting techniques. Read and discover what are layers, scales, coordinates, and themes, and explore how you can use them to transform your data into aesthetical graphs. Learn the simple plots, such as histograms, and then some advanced plots, such as superimposing plots and density plots. Learn to plot trends, correlations, and statistical summaries. After reading this book, your data visualizations will wow clients.
After completing this book, you will be able to:
- Set up the R environment, RStudio, and explain structure of ggplot2
- Distinguish types of 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, aesthetic visualizations with ggplot2 analysis methods
- Logically and systematically explore complex relationships
- Compare variables in a single visual, with advanced plotting methods
This book is meant for professionals, who work with data and R, and for students, who want to enhance their data analysis skills by adding informative and professional visualizations. We assume that readers know basics of the R language, its commands, and objects.
Chapter 1, Basic Plotting in ggplot2, will help you to understand Kubernetes patterns which would be presented with the examples from Kubernetes itself and external applications.
Chapter 2, Grammar of Graphics and Visual Components, will help you to cover accessing Kubernetes API with raw HTTP queries to complex libraries with both in-cluster and out-cluster examples.
Chapter 3, Advanced Geoms And Statistics, will present extension capabilities of Kubernetes with custom resource definitions, custom controllers, dynamic admission controllers, and custom schedulers.
For an optimal experience, we recommend the following hardware configuration:
- Processor: Intel Core i5 or equivalent
- Memory: 4GB RAM
- Storage: 35 GB available space
You'll also need the following software installed in advance:
- OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit, or Windows 10 64-bit
- Browser: Google Chrome, Latest Version
- Installing R:
- To install the R package and libraries, go to http://cran.us.r-project.org/ in your browser.
- Click on Download R for Windows, click on base, then click on Download R 3.5.0 for Windows (or any newer version that appears).
- Install R. Leave all default settings as they are in the installation options.
- Installing RStudio:
- To install RStudio, go to http://rstudio.org/download/desktop.
- Choose the default installation.
- Open RStudio after installation. It uses the underlying R package and will open it automatically in the IDE.
You can download the example code files for this book from your account at www.packtpub.com. If you purchased this book elsewhere, you can visit www.packtpub.com/support and register to have the files emailed directly to you.
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- Log in or register at www.packtpub.com.
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SUPPORTtab. - Click on
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Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:
- WinRAR/7-Zip for Windows
- Zipeg/iZip/UnRarX for Mac
- 7-Zip/PeaZip for Linux
The code bundle for the book is also hosted on GitHub at https://github.com/TrainingByPackt/Applied-Data-Visualization-with-R-and-ggplot2. In case there's an update to the code, it will be updated on the existing GitHub repository.
We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!
There are a number of text conventions used throughout this book.
CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "Use the ggplot(df_vanc,aes(x=Vancouver)) + geom_bar() command to obtain the following chart."
A block of code is set as follows:
df_t <- read.csv("data/historical-hourly-weather-data/temperature.csv")
ggplot(df_t,aes(x=Vancouver))+geom_histogram()
ggplot(df_t,aes(x=Miami))+geom_histogram();Activity: These are scenario-based activities that will let you practically apply what you've learned over the course of a complete section. They are typically in the context of a real-world problem or situation.
Feedback from our readers is always welcome.
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