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.
R for Data Science Cookbook
Credits
www.PacktPub.com
Preface
Free Chapter
Functions in R
Data Preprocessing and Preparation
Visualizing Data with ggplot2
Making Interactive Reports
Simulation from Probability Distributions
Statistical Inference in R
Time Series Mining with R
Index

Decomposing time series

A seasonal time series is made up of seasonal components, deterministic trend components, and irregular components. In this recipe, we introduce how to use the decompose function to destruct a time series into these three parts.

Ensure you have completed the previous recipe by generating a time series object and storing it in two variables: m and m_ts.

How to do it…

Please perform the following steps to decompose a time series:

1. First, use the window function to construct a time series object, m.sub, from m:

> m.sub = window(m, start=c(2012, 1), end=c(2014, 4))
> m.sub
Qtr1 Qtr2 Qtr3 Qtr4
2012 1055 1281 1414 1313
2013 1328 1559 1626 1458
2014 1482 1830 2090 2225
> plot(m.sub)

Figure 6: A time series plot in a quarter

2. Use the decompose function to destruct the time series object m.sub:

> components <- decompose(m.sub)

3. We can then use the names function to list the attributes of components:

> names(components)
[1] "x"        "seasonal...