# Title: Master R Notebooks and Random Number Generation

### Introduction to R Notebooks

Welcome back to another exciting installment in our R programming series! In this blog post, we will explore R Notebooks and dive deeper into generating random numbers in R. If you haven’t already, check out our YouTube channel, Cradle To Graver, for more informative tutorials.

## Video

## Recommended Books

To further enhance your understanding of R programming and data manipulation, we recommend the following books (as an Amazon Associate, I may earn a small commission from these links):

- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
- Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall Street
- The Kaggle Book: Data analysis and machine learning for competitive data science
- Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python

### Getting Started with R Notebooks

To create a new R Notebook, follow these steps:

- Open RStudio
- Click on “File”
- Select “New File”
- Choose “R Notebook”

Now that you have created a new R Notebook, let’s begin by adding some essential elements:

**Headings**: Use the pound symbol (#) followed by a space to create headings in markdown cells. For example:`# Random Number Generator`

**Code chunks**: To insert a code chunk, press`Ctrl + Option + I`

(Mac) or`Ctrl + Alt + I`

(Windows). Name your code chunks for better organization, like so: ``{r random_number_chunk}`

**Bullet points**: To create bullet points in markdown cells, start a line with an asterisk (*) followed by a space. For example:`* Item 1`

## Recommended Books

To further enhance your understanding of R programming and data manipulation, we recommend the following books (as an Amazon Associate, I may earn a small commission from these links):

- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
- Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall Street
- The Kaggle Book: Data analysis and machine learning for competitive data science
- Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python

### Random Number Generation in R

In R, there are two main functions to generate random numbers:

`runif()`

: Generates random numbers with decimal places between specified minimum and maximum values`sample()`

: Generates random integers by taking a sample of numbers from a specified pool

To generate random numbers with `runif()`

, use the following syntax:

`runif(n, min, max)`

`n`

: The number of random numbers you want to generate`min`

: The minimum value (default is 0)`max`

: The maximum value (default is 1)

For example, to generate 10 random numbers between 10 and 40:

`runif(10, 10, 40)`

To generate random integers with `sample()`

, use the following syntax:

`sample(x, size, replace = FALSE)`

`x`

: A vector of numbers to sample from`size`

: The number of items to choose from the vector`x`

`replace`

: Whether sampling should be with replacement (default is`FALSE`

)

For example, to generate 10 random integers between 10 and 40:

`sample(10:40, 10)`

### Using Seeds for Reproducible Random Numbers

In data analysis, generating random numbers that can be reproduced is often necessary. To achieve this, R provides the `set.seed()`

function, which initializes the random number generator with a specified seed value.

To set a seed, call the `set.seed()`

function with a specific integer:

`set.seed(345)`

Any random numbers generated after setting the seed will be the same every time you run the code, making your results reproducible.

### Clearing and Removing Variables in R

There might be instances where you want to remove specific variables from your environment. To do this, you can use the `rm()`

function:

`rm(variable_name)`

Alternatively, you can use the “broom” icon in the Environment pane to clear all variables simultaneously.