#### 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

## Generating Poisson random variates

Poisson distribution is best to use when expressing the probability of events occurring with a fixed time interval. These events are assumed to happen with a known mean rate, λ, and the event of the time is independent of the last event. Poisson distribution can be applied to examples such as incoming calls to customer service. In this recipe, we will demonstrate how to generate samples from Poisson distribution.

In this recipe, you need to prepare your environment with R installed.

### How to do it…

Please perform the following steps to generate sample data from Poisson distribution:

1. Similar to normal distribution, we can use `rpois` to generate samples from Poisson distribution:

```> set.seed(123)
> poisson <- rpois(1000, lambda=3)
```
2. You can then plot sample data from a Poisson distribution into a histogram:

```> hist(poisson, main="A histogram of a Poisson distribution")
```

Figure 5: A histogram of a Poisson distribution

3. You can then obtain the height...