#### 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

## Visualizing a recursive partitioning tree

From the last recipe, we learned how to print the classification tree in text format. To make the tree more readable, we can use the `plot` function to obtain the graphical display of a built classification tree.

You need to have the previous recipe completed by generating a classification model, and assign the model into variable fit.

### How to do it…

Perform the following steps to visualize the classification tree:

1. Use the `plot` and `text` functions to plot the classification tree:

```> plot(fit, margin= 0.1)
> text(fit, all=TRUE, use.n = TRUE)
```

Figure 8: The classification tree of the customer dataset

2. You can also specify the `uniform`, `branch`, and `margin` parameters to adjust the layout:

```> plot(fit, uniform=TRUE, branch=0.6, margin=0.1)
> text(fit, all=TRUE, use.n = TRUE)
```

Figure 9: The recursive portioning tree in a different layout

### How it works…

Here, we demonstrate how to use the `plot` function to graphically display a classification tree...