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

Creating a dynamic force network with the visNetwork package


The visNetwork package is one among the most popular packages in the R community, mainly because it lets you display networks and interact with them without having to invest too much time.

This recipe will get you up and running with this package, showing you all that you need to know to start exploring your network in a fully interactive way.

Getting ready

In order to get started with this recipe, you will need to install and load the visNetwork package to actually produce your network visualizations.

We will also use the jsonlite package to parse the dataset we will use from the JSON format to the data frame:

install.packages(c("visnetwork","jsonlite"))
library(visnetwork)
library(jsonlite)

How to do it...

  1. Download the dataset from the Web and assign it to the Energy object:

    URL <- paste0("https://cdn.rawgit.com/christophergandrud/networkD3/",
      "master/JSONdata/energy.json")
    Energy <- jsonlite::fromJSON(URL)
  2. Now create the nodes...