# R Tutorial

## Obtaining R

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

## Startup ## 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.