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

Exploring time series forecasting with forecast()


The most logical next step after understanding a time series' features and trends is trying to forecast its future development.

As one would imagine, R provides optimal tools to perform this task.

In this recipe, we will leverage the extremely popular forecast package by Professor Rob J Hyndman. The package provides an always increasing number of tools for performing univariate time series forecasting.

You can find out more on the package on Prof. Hyndman's personal site at http://robjhyndman.com/software/forecast/.

Getting ready

As stated earlier, the only package needed to perform this recipe is the forecast package. We therefore need to install it and load it:

install.packages("forecast")
library(forecast)

How to do it...

  1. Apply the stl() function to the nottem dataset:

    nottem_decomposition <- stl(nottem, s.window = "periodic")
    
  2. Forecast five more years:

    forecast <- forecast(nottem_decomposition,h = 5)
    
  3. Plot the forecasted values:

    plot(forecast...