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 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
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: gender
Multiple variable plots are tools for finding relationships between data.
Let's take our
fooddata and make some new multiple variable plots you haven't created before!
Scatterplots are useful for viewing how one numerical variable relates to another numerical variable.
Fill in the blanks to create a scatterplot with
sodiumon the y-axis and
sugaron the x-axis.
xyplot(____ ~ ____, data = food)
Scatterplots in action
Use a scatterplot to answer the following questions:
– Do snacks that have more
proteinalso have more
calories? Why do you think that?
– What happens if you swap the
caloriesvariables in your code? Does the relationship between the variables change?
– Does the relationship between
calorieschange when the snack is either
Sweet? Write down the code you used to answer this question.
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
groupsargument 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:
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.
histogramof the calories variable, but facet the data using:
healthy_level + salty_sweet
How does the
Sweetsnack impact the number of
caloriesin the snack?
Although we are treating healthy_level as a categorical variable, R recongizes it as a numerical variable.
– Use the str command to confirm
– 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
ingredientsthan less healthy snacks?
– What other variables seem to be related to the number of
ingredientsof a snack? Describe their relationships.