What's the Relationship?

Lab 1E

Directions: Follow along with the slides and answer the questions in red font in your journal.

Finding patterns in data.

  • To discover (really) interesting observations or relationships in data, we need to find them!
    • Which is difficult if we only look at the raw data.
  • The best tool for finding patterns is often … your own eyes.
    • Plots are an excellent way to help your eye search for patterns.
  • In this lab, we'll learn how to include more variables in our plots to make them more informative.
  • Import the data from your class' Food Habits campaign and name it food.

Where's the variables?

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  • How many variables were used to create this plot? Which variables were used and how were they used?

Multiple variable plots

  • The previous graph is an example of a multiple variable plot, which means that more than a single variable was used. In this case:
    • Variable 1: height
    • Variable 2: gender
  • Multiple variable plots are tools for finding relationships between data.
  • Let's take our food data and make some new multiple variable plots you haven't created before!


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Creating scatterplots

  • Scatterplots are useful for viewing how one numerical variable relates to another numerical variable.
  • Fill in the blanks to create a scatterplot with sodium on the y-axis and sugar on the x-axis.
xyplot(____ ~ ____, data = food)

Scatterplots in action

  • Use a scatterplot to answer the following questions:
    • Do snacks that have more calories also have more total_fat? Why do you think that?
    • What happens if you swap the calories and total_fat variables in your code? Does the relationship between the variables change?
    • Does the relationship between calories and total_fat change when the snack is either Salty or Sweet? Write down the code you used to answer this question.

4-variable scatterplots

  • When we make scatterplots, we can include:
    • 1 numerical variable on the x-axis
    • 1 numerical variable on the y-axis
    • Use 1 categorical variable to facet our scatterplot
    • Change the color of the points based on another categorical variable
  • To change the color of our points, we can include the groups argument much like we did for bargraphs (use the search feature in the History pane if you need help).
  • Create a scatterplot that uses these 4 variables: sodium, sugar, healthy_level, salty_sweet.

Multiple facets

  • It can sometimes be helpful to facet on more than 1 variable.
    • Splitting the the data using 2 facets can give us additional insights that might otherwise be hidden.
  • Create a dotPlot or histogram of the calories variable, but facet the data using:
healthy_level + salty_sweet 
  • How does the healthy_level of a Salty or Sweet snack impact the number of calories in the snack?.

On your own

  • Answer the following questions by creating an appropriate graph or graphs.
    • Do healthier snacks cost more or less than less healthy snacks?
    • What other variables seem to be related to the cost of a snack? Describe their relationships.