# R Basics Cheat Sheet

Posted : admin On 1/25/2022## Obtaining R

R For Dummies Cheat Sheet By Andrie de Vries, Joris Meys R is more than just a statistical programming language. It’s also a powerful tool for all kinds of data processing and manipulation, used by a community of programmers and users, academics, and practitioners. R has awesome libraries to create basic and more evolved visualizations like Bar Chart, Histogram, Scatter Plot, Map visualization, Mosaic Plot and various others. Here is the cheat sheet of popular visualization for representing data. Find out about the wide array of functions R provides to help you with statistical analysis with R—from simple statistics to complex analyses. Converting between common data types in R. Can always go from a higher value in the table to a lower value. a a 1 'apple' The Environment Variable Assignment ls List all variables in the environment. Rm(x) Remove x from the environment. Rm(list = ls) Remove all variables.

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.

## R Basic Cheat Sheet

`# 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*

## Base R Cheat Sheet

## 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.