#### 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

## Selecting an ARIMA model

Using the exponential smoothing method requires that residuals are non-correlated. However, in real-life cases, it is quite unlikely that none of the continuous values correlate with each other. Instead, one can use ARIMA in R to build a time series model that takes autocorrelation into consideration. In this recipe, we introduce how to use ARIMA to build a smoothing model.

In this recipe, we use time series data simulated from an ARIMA process.

### How to do it…

Please perform the following steps to select the ARIMA model's parameters:

1. First, simulate an ARIMA process and generate time series data with the `arima.sim` function:

```> set.seed(123)
> ts.sim <- arima.sim(list(order = c(1,1,0), ar = 0.7), n = 100)
> plot(ts.sim)
```

Figure 14: Simulated time series data

2. We can then take the difference of the time series:

```> ts.sim.diff <- diff(ts.sim)
```
3. Plot the differenced time series:

```> plot(ts.sim.diff)
```

Figure 15: A differenced time series plot

4. Use the...