RStudio Guide

To get started, click on the 'workspace' folder in the bottom right pane of RStudio. Click on the file entitled "Step 1...". You may want to drag the console pane to be smaller so that you have more room to work. You'll complete each task for Step 1 in that R Markdown file. Remember, you must run the cells with the play button at the top right of each cell for a task before moving onto the next task in the R Markdown file. Continue until you have completed all tasks in this step. Then when you are ready to move onto the next step, you'll come back and click on the file for the next step until you have completed all tasks in all steps of the lab.

Creating a Two-way Frequency Table

To review the concepts covered in this step, please refer to the Creating a Two-way and a Three-way Frequency Table in R module of the Building Tables with R course.

Understanding how to create a two-way frequency table is important because it allows us to analyze the relationship between two variables in our dataset. This is a fundamental concept in data analysis and is often the first step in understanding our data.

In this step, we will practice creating a two-way frequency table using R. This will involve using the table function in R to generate a table that shows the frequency of different combinations of two variables. The goal of this step is to familiarize yourself with the table function and understand how to interpret the output of a two-way frequency table.

Task 1.1: Load the Data

Before we can start creating two-way frequency tables, we need some data to analyze. Load the datasets package which contains the data we will be using. Then, use the data() funciton to load the dataset mtcars into the environment.

library('datasets')
data('mtcars')

Task 1.2: View the Structure of the Dataset

To understand the data we are working with, it's always a good idea to view its structure. Use the str function to view the structure of the mtcars dataset.

str(mtcars)

Task 1.3: Create a Two-way Frequency Table

Now that we understand the structure of our data, we can create a two-way frequency table. Use the table function to create a two-way frequency table that shows the relationship between the cyl (number of cylinders) and am (transmission type) variables in the mtcars dataset.

Assign the table to a new object called two_way_table and then print the table.

two_way_table <- table(mtcars$cyl, mtcars$am)
print(two_way_table)

Task 1.4: Count Cars with Specific Characteristics

Write R code to extract from your table the number of cars in the mtcars dataset that have '4' cylinders and an automatic transmission (am equal to '1').

two_way_table['4', '1']

Creating a Three-way Frequency Table

To review the concepts covered in this step, please refer to the Creating a Two-way and a Three-way Frequency Table in R module of the Building Tables with R course.

Creating a three-way frequency table is important because it allows us to analyze the relationship between three variables in our dataset. This can provide more nuanced insights than a two-way table, especially when we are dealing with complex datasets.

In this step, we will practice creating a three-way frequency table using R. This will involve using the ftable function in R to generate a table that shows the frequency of different combinations of three variables. The goal of this step is to familiarize yourself with the ftable function and understand how to interpret the output of a three-way frequency table.

Task 2.1: Load the Data

Once again, load the datasets package which contains the data we will be using. Then, use the data() function to load the dataset mtcars into the environment.

library('datasets')
data('mtcars')

Task 2.2: Inspect the Dataset

Before creating a frequency table, it's good practice to inspect the dataset. We will be using the mtcars dataset which is included in the datasets library.
Instead of using str like before, use the head function to inspect the first 3 rows of the dataset. Then, use the ? operator to call up the help page about mtcars.

head(mtcars, 3)
?mtcars

Task 2.3: Create a Three-way Frequency Table

Now that we have inspected the dataset, we can create a three-way frequency table. We will be looking at the relationship between the cyl, carb, and am variables in the mtcars dataset. Use the ftable function to create the frequency table.
Assign the table to a new variable called my_ftable and print it.

my_ftable <- ftable(mtcars$cyl, mtcars$carb, mtcars$am)

# alternatively, convert a table to an ftable
my_table <- table(mtcars$cyl, mtcars$carb, mtcars$am)
my_ftable <- ftable(my_table)

print(my_ftable)

Task 2.4: Interpret the Frequency Table

Now that we have created the frequency table, we need to interpret it. The ftable function returns a table with the frequency of each combination of the variables. Each cell represents a different combination of the cyl, carb, and am variables.

Print your ftable and visually examine it. How many cars in the dataset have 4 cylinders, 2 carburetors, and a manual transmission (am equal to '1')?

print(my_ftable)
# 4 cars have 4 cylinders, 2 carburetors, and a manual transmission

Creating Proportions for a Table

To review the concepts covered in this step, please refer to the Creating Proportions for a Table in R module of the Building Tables with R course.

Understanding how to create proportions for a table is important because it allows us to analyze our data in relative terms, rather than absolute counts. This can be particularly useful when comparing groups of different sizes.

In this step, we will practice creating a table with proportions using R. This will involve using the prop.table function in R to generate a table that shows the proportions of different combinations of variables. The goal of this step is to familiarize yourself with the prop.table function and understand how to interpret the output of a proportions table.

Task 2.1: Load the Data

Once again, load the datasets package which contains the data we will be using. Then, use the data() funciton to load the dataset mtcars into the environment.

library('datasets')
data('mtcars')

Task 3.2: Create a Table

Now that we have our dataset, we can create a table. Create a table using the table function in R. Use the cyl and am columns from the mtcars dataset. Assign the result to a variable named tbl.

tbl <- table(mtcars$cyl, mtcars$am)

Task 3.3: Create a Proportions Table

With our table created, we can now create a proportions table. Use the prop.table function in R to create a proportions table from the tbl variable. Assign the result to a variable named prop_tbl.

prop_tbl <- prop.table(tbl)

Task 3.4: Identify the Proportion of Cars with Specific Characteristics

Write R code to extract from your table the proportion of cars in the mtcars dataset that have '4' cylinders and an automatic transmission (am equal to '1').

prop_tbl['4', '1']

Developing a Marginal Frequency Table

To review the concepts covered in this step, please refer to the Developing Marginal Frequency Table in R module of the Building Tables with R course.

Developing a marginal frequency table is important because it allows us to summarize our data in a way that highlights the marginal distributions of our variables. This can be particularly useful when we want to understand the distribution of a single variable, regardless of the values of the other variables.

In this step, we will practice developing a marginal frequency table using R. This will involve using the addmargins function in R to generate a table that shows the marginal frequencies of our variables. The goal of this step is to familiarize yourself with the addmargins function and understand how to interpret the output of a marginal frequency table.

Task 4.1: Load the Data

Once again, load the datasets package which contains the data we will be using. Then, use the data() funciton to load the dataset mtcars into the environment.

library('datasets')
data('mtcars')

Task 4.2: Create a Two-way Frequency Table

Use the table function to create a two-way frequency table that shows the relationship between the cyl (number of cylinders) and am (transmission type) variables in the mtcars dataset.

Assign the table to a new object called two_way_table and then print the table.

two_way_table <- table(mtcars$cyl, mtcars$am)
print(two_way_table)

Task 4.3: Add Margins to the Frequency Table

Now, add margins to the frequency table using the addmargins function. This will give us the marginal frequencies. Assign the new table to an object called marginal_table.

marginal_table <- addmargins(two_way_table)

Task 4.4: Extract the Marginal Sum for a Row

Use your marginal table to extract the number of cars in the dataset that have 6 cylinders.

marginal_table['6', 'Sum']