Unit 1. Getting Started: Labs

These labs test and build on the material presented in the SWIRL lessons.

Scroll down or click here to check how to submit them.

Lab 1

1. Read the New York Times and Quartz articles on R.

2. Install R and RStudio.

Lab 2

Simple Operations

1. Perform the following basic operations in the R console:

a. Add 3 and 5.

b. Add 1, 3, 5, 7 and 11.

c. Subtract 675 from 3.

d. Divide 1 by 1.4.

e. Multiply 2 by 2 by 2.

f. Find 2 to the 3rd power.

Manipulate Variables

2. Assign variables and perform operations

a. Assign 2 to the variable x.

b. Add 4 and x.

c. Assign 10 to the variable myVar.

d. Divide myVar by x.

External data and variable manipulation

3. Use the Harry Potter movie data (Table 1) to answer each question.

Table 1. Box office history for all Harry Potter movies¹.

a. Create three separate variables for each of the first three movies, giving them the value of their worldwide box office take.

b. Check if the Sorcerer’s Stone made more money than the Prisoner of Azkaban.

c. How much money did all three movies make together?

Lab 3

Generate Vectors

Table 2. Ten random responses from the class survey. You will use this table to answer a few of the questions.

1. Create the following vectors and save them as variables of your choosing:

a. A vector from 1 to 1000, using :.

b. The same vector, using seq().

c. A vector from 0 to 200 in increments of 5, using seq().

d. A vector of 10 1s, using rep().

e. Using c(), create a vector of the sizes of your shoe size and 4 random classmates.

f. Using c(), create a combined vector of your variables from c, d, and e, and the number 10.5.

Manipulate vectors

Table 3. Word counts and book size data from The Harry Potter book series.

Sources: 1, 2, 3

2. Using data about the Harry Potter series of books (Table 2), create the following vectors and manipulate them:

a. Create a vector of the word count from each book.

b. Create a vector of the number of chapters in each book.

c. Divide the word count of each book by the number of chapters to find the average number of words per chapter.

d. Create a basic plot of the number of words per book.

e. Create the same plot as (d) but add a title, using an additional argument in your plot() function. Note that any time you would like to do something extra with a function but don’t know how, you can always use the query command ? before the function name (but without the parentheses) to view a help file, which will explain the various arguments that can be passed to a function. For example, ?plot.

Further vector practice

3. Create the following vectors from the class survey data:

a. Create a vector of your classmate’s eye colors.

b. Check what kind of vector this is?

c. Create a vector of the number of harry potter books your classmates have read.

d. Plot this vector.

e. Add a title to the above plot.

Unit 1 Recap

1. Create the following vectors:

a. Generate a vector of 10 shoe sizes, three people with size 10, two of size 11, and five of size 9, using c().

b. Create a vector of the leap years from 1900 to 2018, using seq().

c. Count to 10 in 2s, 5 times.

d. Make a vector of your favorite color repeated 10 times.

e. Five of your classmates like pineapple on their pizza, and five do not. Use rep() to create a vector.

2. Examine the qualities of each vector with the given quantities

a. What is the structure of your shoe size vector?

b. Summarize the shoe size data. What is the maximum shoe size in the class?

c. How is the structure of shoe size and favorite color different?

3. Perform the following on each vector

a. What is the average class height?

b. Create a boxplot of class height.

c. What is the tallest height in the class?

d. What is the average number of harry potter books read?

e. Create a barplot of HP house. Make your plot more informative by adding a title and y-axis label. Check ?barplot if needed.

f. Create a barplot of yes vs nos to pineapple on pizza. Make your plot more informative by adding a title and y-axis label.

g. What is the mean shoe size? (Note: you will likely have to use the help file for mean() here).

4. Is there anything that has been exceptionally confusing so far?

Best Practice Lab

Correct these R code snippets following the best practice code style.

1. average<-mean(feet/12+inches,na.rm=TRUE)

2. x<-1 : 10

3. x [1,]

4. mean(x <- 1:10, FALSE)

5. doSomethingVeryComplicated(data = datafil1,position="here",baseline = na,title="A very long title")

6. x=5

7. simonSays <- 'It's time's to gets going, y'all!'

How to submit your labs

How to write up your lab answers

You will need to write R code to answer each of the questions.

Please format your answers as follows:

• Copy and paste each question, commented out. This ensures that we know which answer corresponds to which question.

• Write your R code answer below each question.

It should look something like this:

# LAB: Unit 1. Lab 1
# Your Name #  Put your name here

# 1. Add 7 and 3,456.
7 + 3456

# 2. Assign this value to an object
x <- 7 + 3456

How to submit your lab answers on Canvas

1. Log into Canvas.

2. Go to the Assignments page.

3. Under ‘Labs’, you should find the correct assignment.

4. Copy and paste your R code into the text box.

5. Click ‘Submit Assignment’.

You are permitted to submit your answers as many times as you like within each Unit.

Answers will be graded two or three times a week and re-opened if you submit early.

Each lab will close at its respective deadline (see Canvas).

Final grades for each lab will be computed and entered into the Canvas gradebook at the end of each Unit.

Updated: 2018-09-29