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

Writing wonderful tufte handouts with the tufte package and rmarkdown


Edward Tufte is one of most inspiring evangelists of effective data visualization of modern times.

His book on effective ways of showing data and telling stories with them has made a great impact on a lot of data-visualization tools and theories.

One of the most typical features of his books is the extensive use of side notes, with both images and text, to further explain concepts introduced in the main body text.

Given the popularity of this layout, R Markdown offers the possibility of creating documents containing side notes, letting you even introduce pieces of R code or plots generated from R code as side notes.

Getting ready

Before working with tufte handouts, we have to install and load the latest version of R Markdown on our computer (refer to the first recipe of this chapter for further information on markdown).

This can be easily done by running the following code:

install.packages(“rmarkdown”)
library(rmarkdown)

How...