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

Using the DiagrammeR package to produce a process flow diagram in RStudio


Process flow diagrams are powerful tools for process analysis, and having created a way to produce them in R is one among the greatest credits to be given to Rich Iannone and his DiagrammeR package.

Generally speaking, this package leverages HTML widgets to let you build an R diagram of nearly every kind.

Even if more advanced and customizable tools are available as standalone software, DiagrammeR lets you easily integrate different parts of your analysis without leaving R.

Moreover, DiagrammeR is perfectly integrated in RStudio and the Shiny framework, which is one among the hottest tools in R community.

Getting ready

As usual, we first have to install and load the necessary package, that is to say the DiagrammeR package:

install.packages("DiagrammeR")
library(DiagrammeR)

We are now ready to create a data frame that stores nodes and hedges of our process workflow.

Particularly, we will build an example from the healthcare...