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

Performing time series decomposition using the stl() function


Nearly every phenomenon can be represented as a time series.

It is therefore not surprising that time series analysis is one of most popular topics within data-science communities.

As is often the case, R provides a great tool for time-series decomposition, starting with the stl() function provided within base R itself. This function will be the base of our recipe.

Getting ready

This recipe will mainly use the stl() function, which implements the Loess() method for time-series decomposition.

Using this method, we are able to separate a time series into three different parts:

  • Trend component: This highlights the core trend of the phenomenon if perturbations and external influence were not in place

  • Seasonal component: This is linked to cyclical influences

  • Remainder: This groups all non-modeled (in hypothesis random) effects

As mentioned earlier, this function is provided with every R base version, and we therefore don't need to install...