#### 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 binomial random variates

To model the success or failure of several independent trials, one can generate samples from binomial distribution. In this recipe, we will discuss how to generate binomial random variates with R.

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

### How to do it…

Please perform the following steps to create a binomial distribution:

1. First, we can use `rbinom` to determine the frequency of drawing a six by rolling a dice 10 times:

```> set.seed(123)
> rbinom(1, 10, 1/6)
[1] 1
```
2. Next, we can simulate 100 gamblers rolling a dice 10 times, and observe how many times a six is drawn by each gambler:

```> set.seed(123)
> sim <- rbinom(100,10,1/6)
> table(sim)
sim
0  1  2  3  4  5
17 36 23 18  4  2
```
3. Additionally, we can simulate 1,000 people tossing a coin 10 times, and compute the number of heads at each tossing:

```> set.seed(123)
> sim2 <- rbinom(1000,10,1/2)

> table(sim2)
sim2
0   1   2   3   4   5   6   7...```