Skip to content

Lab 1E: What’s the Relationship?

Lab 1E - What's the Relationship?

Directions: Follow along with the slides and answer the questions in bold font in your IDS 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 are the variables?

  • 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!

Scatterplots

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.

Four-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.