R For Dummies Cheat Sheet

• Dos For Dummies Cheat Sheet
• R For Dummies Cheat Sheet

JOIN (TO) LISTS append(x, values, a!er = length(x)) Add to end of list. Append(x, list(d = 1)) prepend(x, values, before = 1) Add to start of list. Prepend(x, list(d. Use the cheatsheets that are built into the RStudio IDE to jog your memory about R Markdown. Go to File Help Cheatsheets R Markdown Cheat Sheet to open the main R Markdown cheatsheet, pictured above. Go to File Help Markdown Quick Reference to open the Markdown Quick Reference in your help pane.

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:

Logical Operators include:

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.

Dos For Dummies 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

R For Dummies 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.

If you spend time with R regularly then you should have the basics of data manipulation & plotting down within a couple of weeks. The more esoteric functions will take time to master. These are PDF cheat sheets that can be printed out. Keep them handy as you work with R.

help.start() - This first link doesn't actually take you to another site because it isn't a resource on the web, it's contained with R. To access it just type help.start() at the prompt and a collection of manuals will appear in your browser window. Pretty handy reference tool, eh?

R Reference Card - This is the most popular reference card. This same document is available from a number of sites and includes info on input & output, getting help, working with variables & arrays, plotting & more. You want this one.

R for Data Mining - This document was written by RDataMining.com. It includes info on classification, clustering, social network analysis, big data and more. This is very handy if you're interested in knowledge discovery & data mining.

R Style Guide - This resource is more than a cheat sheet. Google's internal R user community put together this guide for clean R code that covers syntax & conventions that are unique to R. I include it here because I've refered to it quite a bit in my own work. Your code will be easy to read & maintain if you follow these guidelines.