#### 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

## Understanding uniform distributions

If we roll a fair dice, the likelihood of drawing any side is equal. By plotting the samples in a bar plot, we can see bars with equal heights. This type of distribution is known as uniform distribution. In this recipe, we will introduce how to generate samples from a uniform distribution.

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

### How to do it…

Please perform the following steps to generate a sample from uniform distribution:

1. First, we can create samples from uniform distribution by using the `runif` function:

```> set.seed(123)
> uniform <- runif(n = 1000, min = 0, max = 1)
```
2. We can then make a histogram plot out of the samples from the uniform distribution:

```> hist(uniform, main = "1,000 random variates from a uniform distribution")
```

Figure 1: A histogram of 1,000 random variates from a uniform distribution

3. For uniform distribution, we also can find the density at each point equals `1`, and the density function...