R Tutorial 101

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.

R program – 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.

R console – 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
< less than
=< less 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, there are free R tutorials by DataCamp is a great way to get started.

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