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Lab 1E: What’s the Relationship?

Lab 1E - What's the Relationship?

Directions: Follow along with the slides, completing the questions in blue on your computer, and answering the questions in red 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?

  • 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: sex

  • 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

  • Scatterplots are useful for viewing how one numerical variable relates to another numerical variable.

Creating scatterplots

  • 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 protein also have more calories? Why do you think that?

    – What happens if you swap the protein and calories variables in your code? Does the relationship between the variables change?

    – Does the relationship between protein and calories 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, cost, salty_sweet.

Multiple facets

  • It can sometimes be helpful to facet on more than 1 variable.

    – Splitting 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?

  • Although we are treating healthy_level as a categorical variable, R recongizes it as a numerical variable.

    – Verify this using the str function.

    – Notice that the faceted histograms or dotPlots do not have labels but rather tick-marks.

    – You will have the opportunity to convert the healthy_level variable into a factor later on.

  • Faceting your data on a numerical variable is NOT recommended.

    – Numerical variables often have so many different values that they overwhelm the plot and make it hard to read.

On your own

  • Answer the following questions by creating an appropriate graph or graphs:

    – Do healthier snacks have more or less ingredients than less healthy snacks?

    – What other variables seem to be related to the number of ingredients of a snack? Describe their relationships.