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

Looking at your data using the plot() function


The plot() function is one of most powerful functions in base R. The main point of using the plot() function is that it will always try to print out a representation of your data. It basically tries to figure out which kind of representation is the best, based on the data type. This will let you easily and quickly get a first view of the data you are working with.

Behind the scenes, the power of the plot() function comes from being packed with a number of methods developed for specific types of object.

So, when an object is passed as an argument to plot(), it looks for the most appropriate method within the ones available and uses it to represent data stored within the object.

It is even possible to further expand the plot() function, as is regularly done in various packages, adding new methods for specific types of object by running setMethod() on it. This is out of the scope of this recipe, but you can find a good explanation in the R language...