Introducing R

Introducing Markdown

The file you are now reading is created using the Markdown language. Think of Markdown like Microsoft Word on steroids. The difference is, instead of clicking through drop-down menus to change font, line spacing, etc. you need to type in the instructions inside the document.

This file has the extension .qmd which stands for Quarto Markdown. All assignments and activities will be given as .qmd files.

The upside is that you can easily write nice looking reports and directly incorporate code output including summary tables and graphs.

The YAML

At the very top of the document, you will see some blue and green text between two sets of ---. This gives instructions to the computer about what kind of document to make. It is called the YAML. What YAML stands for is up for debate. But the instructions are necessary for building the document and making it look nice.

The YAML above is very basic. We may expand on it a little as the semester progresses. But for now, this document can be made into an HTML file by “Rendering” it.

Rendering the Document

Above, you should see a “Render” button. Click on it and see what happens.

We can make section headers, type text, include code chunks and stitch it all together into a decent-looking report with only a few lines of text.

This is a Section Header

This is a section subheader

sub-subheader

You get the point.

Making R Work for Us

The best thing about Markdown files is that we can include code and use output directly within the document.

Next, we’ll see how to program in R within a text document! Hooray!

Coding inside this word document can be done by inserting a “code chunk” using the tick mark symbol (not an apostrophe) and curly brackets. The shortkey command is:

  1. ctrl+alt+i on a PC
  2. cmd+alt+i on a Mac

The above combination of keys will create a new code chunk wherever your cursor is located.

Basic R Operators

We introduce a few basic and frequently used R commands.

At the end of this chapter you should be able to:

  1. Understand the combine function, c()
  2. Understand the assignment operator, <-
  3. The read.csv() function
  4. How to view a dataset
  5. Understand the selection operator, $

The Combine Function, c()

Sometimes I want to type in data manually. The simplest way to accomplish this is to use the combine function, c(). Think of c() as a way to combine a list of things that are alike. For example, I can create a new dataset using the combine function:

c(21, 22, 18, 19, 19, 20, 22)
[1] 21 22 18 19 19 20 22

I can create a list of names as well, but I have to put characters in quotes:

c("Billy", "Joel", "Bobby", "Dylan", "Carly", "Rae", "Jeppson")
[1] "Billy"   "Joel"    "Bobby"   "Dylan"   "Carly"   "Rae"     "Jeppson"

Running the above code isn’t very helpful because I won’t be able to do anything with those lists. Computers are so literal. All the code did was print out a list that I made. If I want to be able to use those lists, I need to give them a name.

The Assignment Operator

In R, we like to name things. You can name pretty much anything including datasets, graphs, output, etc. When we want to assign a name to something we use <-, which is made up of a “less than” sign and a dash. We put the name on the left side.

Let’s demonstrate by naming our lists for ages and names above.

ages <- c(21, 22, 18, 19, 19, 20, 22)

students <- c("Billy", "Joel", "Bobby", "Dylan", "Carly", "Rae", "Jeppson")

Now if I want to refer to those data, I can just refer to the name:

ages
[1] 21 22 18 19 19 20 22
students
[1] "Billy"   "Joel"    "Bobby"   "Dylan"   "Carly"   "Rae"     "Jeppson"

SHORT KEY: You can quickly create the assignment operator by hitting “Alt” plus “-” on a PC, or “Option” plus “-” on a Mac.

Importing Raw Data

In this course, we will read raw data into R and use it for analysis and visualizations. Say there is a dataset stored online that we would like to bring into R to anlayze. We can use the import() function from the rio library to load the data.

Running the following code instructs R to import that data…and nothing else.

read.csv('https://raw.githubusercontent.com/byuistats/Math221D_Cannon/master/Data/All_class_combined_personality_data.csv')

Again, I can’t do anything with it because it hasn’t been stored anywhere. If I want to be able to do anything with the data, I must somehow store it in R. I do this by assigning a name to the data. I can make up any name I like as long as it begins with a letter and has no spaces. Let’s call it “steve”.

steve <- read.csv('https://raw.githubusercontent.com/byuistats/Math221D_Cannon/master/Data/All_class_combined_personality_data.csv')

Notice how nothing printed out. This is because computers, being literal, assigned a dataset to something I named “steve”. I haven’t told the computer to do anything with it yet. But now I have an R “object” that I can use.

Viewing Raw Data

There are several ways to view raw data in R:

  1. Run “steve” by itself
  2. use the View() function

Run each line individually by putting the curso on the line you want to run and hitting:

  1. PC: ctrl+Enter
  2. Mac: CMD+Enter
steve

View(steve)

BEST PRACTICE: It is best to name objects in R with descriptive names relating to what the object is. big5_data would be a better name than steve, for example, because this is data about the big 5 personality traits.

Selecting a Column From a Dataset

Datasets usually have many columns. When we want to select one column from a dataset to summarize, we can use the $ operator. For example, if we want to look at only the scores for Extroversion, we can run the following code:

steve$Extroversion

This prints out only the values in the Extroversion column.

WATCHOUT: The column name has to be typed EXACTLY as it is in the dataset.

R makes it a little easier because after you type the $ it will bring up a dropdown menu and you can select the column you’re interested in.

SPACES: If there are spaces in a column name you have to wrap the whole column name using backticks `. On most keyboards, this is on the same key as the ~ next to the 1 key. It’s always easier to use the dropdown menu for complicated column names.

NOTE: The drop down only works when you’re typing forward. If you delete text back to the dollar sign it won’t show up. To get it back, delete the dollar sign and retype it.

We will use the selection $ all very frequently to specify which columns we want to use for data summaries, visualizations, and analyses.

For example, I can easily get the mean Openness score by putting steve$Openness into the mean() function:

mean(steve$Openness)
[1] 71.81111