Rebekah Robinson, Georgetown College

In This Chapter

R and RStudio

  • R is a statistical software program.
  • RStudio is an integrated development environment (IDE) that facilitates the use of R.
  • RStudio makes R easier and more fun to use!

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Panels and Tabs: Bottom Left Panel

The bottom left panel

  • is called the Console.
  • is the “brain” of R.

Anything entered in the Console will be executed by R.

Give it a try! Place your cursor in the Console panel, type the following, and then hit return.


Panels and Tabs: Top Right Panel

The top right panel has two tabs:

  • History - stores all commands entered in the Console.

Take a look at your History tab. You should see 4+9.

  • Environment - stores all objects (datasets, functions, etc.) that have been entered in the Console.

There isn't anything in your Environment tab because we have not created any objects.

Panels and Tabs: Top Right Panel

Let's create an expression object! Type the following:

mysum <- 4+9 
  • You have stored the sum 4+9 in the object named mysum.
  • There is nothing special about the name mysum. You could have named it anything!
  • Look in the Environment tab. You should see this object, along with it's value.
  • Go back to the Console and type the following to return the value stored in mysum.

Panels and Tabs: Bottom Right Panel

The bottom right panel has four tabs:

  • Files
    • lists available files
    • allows you to upload new files
  • Plots
    • displays plots that you create in the Console
  • Packages
    • allows you to install and load necessary packages
  • Help
    • view help files

Panels and Tabs: Bottom Right Panel

There are 3 important packages to load when you work in R

  • tigerstats
  • mosaic
  • manipulate

You can load them one of two ways:

  • Checking the appropriate box in the Packages tab.
  • Typing the following into your Console:

Panels and Tabs: Top Left Panel

The top left panel

  • is called the Source.
  • is where you will do most of your typing.
  • is where you will open, edit, and save files.

Let's open an R Script file. Select

  • File
  • New File
  • R Script

Panels and Tabs: Top Left Panel

An R Script is a file to store code. This allows you to

  • easily modify and edit long lines of code
  • save your work
  • share your work with others

Once a command is typed into an R script file, it should be run through the Console.

  • Select the entire command. Then copy and paste into the Console.
  • With the cursor on the line you want to run, press the Run button at the top right of the Source window.

Basic R

R as Calculator

The most basic way to use R is as a calculator. Type the following expressions into an RScript and run them through the Console.

  • use + for addition
  • use - for subtraction
  • use * for multiplication
  • use / for division

R as Calculator

When using R as a calculator, use parentheses to preserve the order of operations. The expression

[1] 17

is different than

[1] 24

R as Calculator

R has some built in mathematical functions that should be familiar to you. For example:

sqrt(81)  #square root function
[1] 9
cos(pi)  #cosine of pi
[1] -1

Use the # sign to add a comment next to a line of code. This helps you remember what a particular line of code does!

Getting Help

What if you couldn't remember what the square root function was? You could access the help file on this function using either of the following methods.

  • Type the following into the Console


  • Type the first few letters of the function into the Console and press Tab. Select the appropriate function from the list. Press F1 to open the help file in the Help tab.

Important R Functions

There are several built in functions to become familiar with:

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Concatenate Function

To combine values into a list, type

c(1, 3, 5)

It is useful to store a list in an object. Name it whatever you like!

mylist <- c(1, 3, 5) #creates the object
mylist #calls the object
[1] 1 3 5

Concatenate Function

To combine words or letters into a list, type

mywordlist <- c("A", "B", "Cat")

To see the list, type:

[1] "A"   "B"   "Cat"

Letters and words must be put in quotations.

Replicate Function

To create a list of numbers that are all the same, you can use the concatenate function.

c(2, 2, 2, 2, 2)

It is easier to use the rep function:

myreps <- rep(x=2,times=5)
[1] 2 2 2 2 2

Replicate Function


The rep function requires two inputs:

  • x is the value that you want to replicate
  • times is the number of times you want to replicate x

Replicate Function

You do not have to enter the names of the inputs

[1] 2 2 2 2 2

as long as you enter them in the correct order.

[1] 5 5

Replicate Function

You can also replicate letters or words.

rep("apple", 3)
[1] "apple" "apple" "apple"

Sequence Function

The seq function comes in handy for making seqences of values.

To create the sequence 1, 2, 3, 4, 5, type

[1] 1 2 3 4 5

Sequence Function


This functions requires three inputs:

  • from is the starting point of the sequence,
  • to is the ending point of the sequence,
  • by is the increment.

Sequence Function

This function is useful for other increments.

[1] 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0
 [1] 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Let's Play Cards

The Game

  • A volunteer draws 10 cards from our classroom deck of cards, with replacement.
  • The cards will be shuffled between each draw.
  • The class wins a point for each red card drawn and the instructor wins a point for each black card drawn.

With replacement means that the volunteer will

  • draw a card
  • record the color
  • put it back in the deck before drawing another card.

The Game

Questions to consider before we play:

  • What is the probability that the volunteer will pull out a red card?

  • Of the 10 cards drawn, how many do you expect to be red?

  • Do we think that the volunteer will draw exactly the hypothesized number of red cards?

Let's play!

The Results

Questions to consider about our results:

  • Do these results seem consistent with how many we expected to be red? Or do they seem strange?

  • Do you still believe your hypothesized probability of drawing a red card? In other words, do you still believe that we are playing with a standard deck?

We will investigate these questions by playing more games. Let's simulate 1000 games in R.

The Results

Let's start by making a deck of cards in R. Since we only care about the color, we can do this by:

mycards <- c(rep("Red",26),rep("Black",26))

Let's look at the deck:


You should see 26 Reds followed by 26 Blacks.

The Results

Now, we will randomly draw 10 cards from our virtual deck.

sample(mycards, size=10, replace=TRUE)

The Results

You can take a sample of ten cards and then count up the results:


Black   Red 
    6     4 

The Results

Let's play 3 games:

                   size=10, replace=TRUE))
  Black Red
1     7   3
2     4   6
3     2   8
  • Game One: 7 black and 3 red cards
  • Game Two: 4 black and 6 red cards
  • Game Three: 2 black and 8 red cards

The Results

  Black Red
1     7   3
2     4   6
3     2   8

Let's display these results in terms of how many games resulted in a given number of red cards.

 0  1  2  3  4  5  6  7  8  9 10 
 0  0  0  1  0  0  1  0  1  0  0 

The Results

Now that we know how to read the results, let's simulate 1000 games.

  0   1   2   3   4   5   6   7   8   9  10 
  0   5  41 119 205 249 209 121  42   8   0 

For example,

  • Five red cards were drawn in 249 of the 1000 games.
  • Eight red cards were drawn in 42 of the 1000 games.
  • Nine red cards were drawn in 8 of the 1000 games.
  • Ten red cards were drawn in 0 of the 1000 games.

The Results

Another way to think about these numbers is:

  • Five red cards were drawn in 25.9% of the games.
  • Eight red cards were drawn in 4.2% of the games.
  • Nine red cards were drawn in 0.8% of the games
  • Ten red cards were drawn in 0% of the games.

How likely is it that our volunteer drew their original hand, based on our simulations?

The Results

A graphical representation of these percents is useful.

plot of chunk unnamed-chunk-38

The Results

  • Horizontal axis - the number of red cards in a hand of 10 cards from a standard deck.
  • Vertical axis - the percentage of times (out of the 1000 simulated games) that a particular number of red cards was drawn.

plot of chunk unnamed-chunk-39

The Results

Let's shade a bar in the histogram to mark the number of red cards our volunteer drew in the class game.

For example, suppose our volunteer drew 9 red cards.

The Results

This shaded region in the histogram represents the estimated chance of drawing 9 red cards from a standard deck.

plot of chunk unnamed-chunk-40

The Results

The likelihood that our class game resulted in such a high number of red cards (or higher) if we were really drawing from a standard deck of playing cards is called a p-value. (This P-value is about 0.008)

plot of chunk unnamed-chunk-41

Our Process

  1. We wanted to test the hypothesis that we were playing with a standard deck.

  2. Data was gathered from a real-world experiment to test our hypothesis.

  3. We asked, “How likely was it to draw the hand that we did if we drew from a standard deck?” “

  4. We simulated 1000 games using R, and counted the # of games that gave us at least the result we got in the experiment.

  5. We calculated a P-value, the probability of getting results as extreme as ours (or more so!) from a standard deck.

The Conclusion

Finally, draw a conclusion.

If we assume that our volunteer drew 10 cards from a standard deck of cards, there is about a 0.8% chance of drawing 9 red cards.

  • What does this suggest about our originial hypothesis?
  • Do you believe we were really playing with a standard deck?
  • How convinced are you?


Goal: Translate data into knowledge and understanding of the world around us. Statistics is the art and science of learning from data!

The card game we played above is a perfect example of the three aspects of statistics.

  • Design - asking the right questions and collecting useful data.
  • Description - summarizing and analyzing data.
  • Inference - making decisions, generalizations, and turning data into new knowledge.