Book Image

R for Data Science Cookbook (n)

By : Yu-Wei, Chiu (David Chiu)
Book Image

R for Data Science Cookbook (n)

By: Yu-Wei, Chiu (David Chiu)

Overview of this book

This cookbook offers a range of data analysis samples in simple and straightforward R code, providing step-by-step resources and time-saving methods to help you solve data problems efficiently. The first section deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation is provided, illustrating how to use the “dplyr” and “data.table” packages to efficiently process larger data structures. We also focus on “ggplot2” and show you how to create advanced figures for data exploration. In addition, you will learn how to build an interactive report using the “ggvis” package. Later chapters offer insight into time series analysis on financial data, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction. By the end of this book, you will understand how to resolve issues and will be able to comfortably offer solutions to problems encountered while performing data analysis.
Table of Contents (19 chapters)
R for Data Science Cookbook
About the Author
About the Reviewer

Forecasting time series

After using HoltWinters to build a time series smoothing model, we can now forecast future values based on the smoothing model. In this recipe, we introduce how to use the forecast function to make a prediction on time series data.

Getting ready

In this recipe, you have to have completed the previous recipe by generating a smoothing model with HoltWinters and have it stored in a variable, m.pre.

How to do it…

Please perform the following steps to forecast Taiwan Semiconductor's future income:

  1. Load the forecast package:

    > library(forecast)
  2. We can use the forecast function to predict the income of the next four quarters:

    > income.pre <- forecast.HoltWinters(m.pre, h=4)
    > summary(income.pre)
    Forecast method: HoltWinters
    Model Information:
    Holt-Winters exponential smoothing with trend and additive seasonal component.
    HoltWinters(x = m)
    Smoothing parameters:
     alpha: 0.8223689
     beta : 0.06468208
     gamma: 1
    a  1964.30088
    b   ...