#### 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

## Smoothing time series

Time series decomposition allows us to extract distinct components from time series data. The smoothing technique enables us to forecast the future values of time series data. In this recipe, we introduce how to use the `HoltWinters` function to smooth time series data.

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 smooth time series data:

1. First, use `HoltWinters` to perform Winters exponential smoothing:

```> m.pre <- HoltWinters(m)
> m.pre
Holt-Winters exponential smoothing with trend and additive seasonal component.

Call:
HoltWinters(x = m)

Smoothing parameters:
alpha: 0.8223689
beta : 0.06468208
gamma: 1

Coefficients:
[,1]
a  1964.30088
b    32.33727
s1  -51.47814
s2   17.84420
s3  146.26704
s4   70.69912
```
2. Plot the smoothing result:

```> plot(m.pre)
```

Figure 9: A time series plot with Winters exponential smoothed...