Book Image

RStudio for R Statistical Computing Cookbook

By : Andrea Cirillo
Book Image

RStudio for R Statistical Computing Cookbook

By: Andrea Cirillo

Overview of this book

The requirement of handling complex datasets, performing unprecedented statistical analysis, and providing real-time visualizations to businesses has concerned statisticians and analysts across the globe. RStudio is a useful and powerful tool for statistical analysis that harnesses the power of R for computational statistics, visualization, and data science, in an integrated development environment. This book is a collection of recipes that will help you learn and understand RStudio features so that you can effectively perform statistical analysis and reporting, code editing, and R development. The first few chapters will teach you how to set up your own data analysis project in RStudio, acquire data from different data sources, and manipulate and clean data for analysis and visualization purposes. You'll get hands-on with various data visualization methods using ggplot2, and you will create interactive and multidimensional visualizations with D3.js. Additional recipes will help you optimize your code; implement various statistical models to manage large datasets; perform text analysis and predictive analysis; and master time series analysis, machine learning, forecasting; and so on. In the final few chapters, you'll learn how to create reports from your analytical application with the full range of static and dynamic reporting tools that are available in RStudio so that you can effectively communicate results and even transform them into interactive web applications.
Table of Contents (15 chapters)
RStudio for R Statistical Computing Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

Changing axes appearance to ggplot2 plot (continous axes)


This recipe shows you how to get control over an axis within a ggplot plot.

The ggplot2 package does a great job of automatically setting the appearance of the axes, but sometimes, even in the early stages of your project, you may want your axis to appear in a specific shape, showing, for instance, a defined number of tickmarks.

This is what this recipe is all about—giving you control over the appearance of your ggplot axes.

In this example, we will use a plot based on the Iris dataset.

The Iris dataset is one of most used datasets in R tutorials and learning sessions, and it is derived from a 1936 paper by Ronald Fisher, named The use of multiple measurements in taxonomic problems.

Data was observed on 50 samples of three species of the iris flower:

  • Iris setosa

  • Iris virginica

  • Iris versicolor

On each sample for features were recorded:

  • length of the sepals

  • width of the sepals

  • length of the petals

  • width of the petals

For a general and brief introduction...