R is an open source language for statistics and visualization. R is free and has a large (and growing) community, making it a popular choice for data analysis.
Our real goal isn't to teach you R, but to teach you the basic concepts that all programming depends on. We use R in our lessons because:
RStudio is an IDE, or "integrated development environment" for R. You don't need to use RStudio to use R, but it comes with a lot of handy features.
Let's start by creating a new RStudio project on our desktop. RStudio helps you organize your work into discrete projects, each with their own files and history. You can use RStudio projects to easily pick up where you left off with a project or analysis.
Now that we're in our project, let's get familiar with RStudio's layout. First there is the R console, which will evaluate the commands we give it. You can type directly into the console. For example, R can do arithmetic:
2 + 2
5 * 6
10 / 4
The Files tab shows files that are in the directory we are currently in, e.g.
Home/Desktop/swc-wsu -- since we are using an RStudio project, that directory should be the same directory that the project is in.
While you can type commands directly in the console, usually you'll want to create objects, using "assignment". You assign values, data, etc. to objects using the
<- ooperator. For example:
x <- 8 x
Here we have created an object called
x and assigned it the value 8. We can then use
x in other calculations or other assignments.
x * 3
y <- x / 2 y
You can overwrite an object's value at any time.
x <- 15 x
y was defined as
x / 2, has it changed now that we have changed
x? The answer is no. We assigned the value of
x / 2 to
x was 8, so the value that actually got assigned to
y was 4. This will not change unless we reassign
To view the objects that are in your current environment, use
 "x" "y"
RStudio also shows thee objects in the Environment panel.
One of the major reasons to write code is to keep a record of what you have done so that you can repeat it. This way, if a reviewer asks you to slightly modify a figure in a manuscript or to clarify how you found a particular result, you don't need to remember and recreate every button click, you can just re-run the script or modify it if necessary. So far we have been typing directly into the console, but to save our work for future use we should write a script. Let's create a new script and copy the commands we have already executed from the History tab in RStudio.
To execute code in the console from a script you use Ctrl-Enter (or Cmd-Enter on a Mac). If code is highlighted it will execute the entire selection; if nothing is highlighted it will execute the current line.
From now on we'll be working in a script and executing the code from there. This way you will have a record of everything we did in this lesson.
Adapted from Christie Bahlai's materials for the 2015-01-05 UMich workshop
A vector is a group of values, mainly either numbers or characters. You can assign this set of values to a variable, just like you would for one item. For example we can create a vector of animal weights:
weights <- c(50, 60, 65, 82) weights
 50 60 65 82
c() stands for "combine". A vector can also contain characters:
animals <- c("mouse", "rat", "dog") animals
 "mouse" "rat" "dog"
The contents of a vector must all be the same type (i.e. all character or all numeric).
Functions in R let you perform various operations, such as finding the mean of a set of numbers, for example. Functions have a name followed by a set of parentheses that contain arguments to the function. In many cases the first argument to a function will be an object that you are working with.
If you want to learn more about a function you can look up the help file for it by typing
There are many functions that allow you to inspect the content of an object.
length() tells you how many elements are in a particular vector:
class() indicates the class (the type of element) of an object:
str() provides an overview of the object and the elements it contains. It is a really useful function when working with large and complex objects:
num [1:4] 50 60 65 82
chr [1:3] "mouse" "rat" "dog"
You can add elements to your vector simply by using the
weights <- c(weights, 90) # adding at the end weights <- c(30, weights) # adding at the beginning weights
 30 50 60 65 82 90
What happens here is that we take the original vector
weights, and we are adding another item first to the end of the other ones, and then another item at the beginning. We can do this over and over again to build a vector or a dataset. As we program, this may be useful to auto-update results that we are collecting or calculating.
We just saw 2 of the 6 data types that R uses:
"numeric". The other 4 are:
FALSE(the boolean data type)
"integer"for integer numbers (e.g.,
Lindicates to R that it's an integer)
"complex"to represent complex numbers with real and imaginary parts (e.g.,
1+4i) and that's all we're going to say about them
"raw"that we won't discuss further
Vectors are one of the many data structures that R uses. Other important ones are lists (
list), matrices (
matrix), data frames (
data.frame) and factors (
weightsinto a vector called