# R Tutorial

## Obtaining R

R is available for Linux, MacOS, and Windows. Software can be downloaded from The Comprehensive R Archive Network (CRAN).

## Startup

After R is downloaded and installed, simply find and launch R from your Applications folder.

## Entering Commands

R is a command line driven program. The user enters commands at the prompt (**>** by default) and each command is executed one at a time.

## The Workspace

The workspace is your current R working environment and includes any user-defined objects (vectors, matrices, data frames, lists, functions). At the end of an R session, the user can save an image of the current workspace that is automatically reloaded the next time R is started.

## Graphic User Interfaces

Aside from the built in R console, RStudio is the most popular R code editor, and it interfaces with R for Windows, MacOS, and Linux platforms.

## Operators in R

R's binary and logical operators will look very familiar to programmers. Note that binary operators work on vectors and matrices as well as scalars.

Arithmetic Operators include:

Operator |
Description |

+ |
addition |

- |
subtraction |

* |
multiplication |

/ |
division |

^ or ** |
exponentiation |

Logical Operators include:

Operator |
Description |

> |
greater than |

>= |
greater than or equal to |

== |
exactly equal to |

!= |
not equal to |

## Data Types

R has a wide variety of data types including scalars, vectors (numerical, character, logical), matrices, data frames, and lists.

## Creating New Variables

Use the assignment operator **<-** to create new variables.

`# An example of computing the mean with variables`

mydata$sum <- mydata$x1 + mydata$x2

mydata$mean <- (mydata$x1 + mydata$x2)/2

## Functions

Almost everything in R is done through functions. A function is a piece of code written to carry out a specified task; it may accept arguments or parameters (or not) and it may return one or more values (or not!). In R, a function is defined with the construct:

```
```

**function** ( arglist ) {body}

The code in between the curly braces is the body of the function. Note that by using built-in functions, the only thing you need to worry about is how to effectively communicate the correct input arguments (arglist) and manage the return value/s (if any).

## Importing Data

Importing data into R is fairly simple. R offers options to import many file types, from CSVs to databases.

For example, this is how to import a CSV into R.

`# first row contains variable names, comma is separator `

# assign the variable *id* to row names

# note the / instead of \ on mswindows systems

mydata <- read.table("c:/mydata.csv", header=TRUE,

sep=",", row.names="id")

## Descriptive Statistics

R provides a wide range of functions for obtaining summary statistics. One way to get descriptive statistics is to use the **sapply( )** function with a specified summary statistic.

Below is how to get the mean with the **sapply( )** function:

`# get means for variables in data frame mydata`

# excluding missing values

sapply(mydata, mean, na.rm=TRUE)

Possible functions used in **sapply** include ** mean, sd, var, min, max, median, range**, and **quantile**.

## Plotting in R

In R, graphs are typically created interactively. Here is an example:

`# Creating a Graph`

attach(mtcars)

plot(wt, mpg)

abline(lm(mpg~wt))

title("Regression of MPG on Weight")

The **plot( )** function opens a graph window and plots weight vs. miles per gallon. The next line of code adds a regression line to this graph. The final line adds a title.

## Packages

Packages are collections of R functions, data, and compiled code in a well-defined format. The directory where packages are stored is called the library. R comes with a standard set of packages. Others are available for download and installation. Once installed, they have to be loaded into the session to be used.

```
.libPaths() # get library location
```

library() # see all packages installed

search() # see packages currently loaded

## Getting Help

Once R is installed, there is a comprehensive built-in help system. At the program's command prompt you can use any of the following:

`help.start() # general help`

help(*foo*) # help about function *foo*

?*foo* # same thing

apropos("*foo*")
# list all functions containing string foo

example(*foo*) # show an example of function *foo*

## Going Further

If you prefer an online interactive environment to learn R, this free R tutorial by DataCamp is a great way to get started.