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

Preparing your data for analysis with the tidyr package


The tidyr package is another gift from Hadley Wickham. This package provides functions to make your data tidy.

This means that after applying the tidyr package's function, your data you will be arranged as per the following rules:

  • Each column will contain an attribute

  • Each row will contain an observation

  • Each cell will contain a value

These rules will produce a dataset similar to the following one:

This structure, besides giving you a clearer understanding of your data, will let you work with it more easily.

Furthermore, this structure will let you take full advantage of the inner R-vectorized structure. This recipe will show you how to apply the gather function to a dataset in order to transform a dataset and make it comply with the cited rules.

The employed data frame is in the so-called wide format, where each period of observation is stored in columns, with each column representing a year, as follows:

Getting ready

In order to let you apply...