Putting data together

In the labs so far, we’ve only ever looked at individual data files.
- But often times, we gain additional insights by including additional information from a separate data set.
In this lab, we will learn how to merge information from our personality color data with our stress/chill data.
Export, upload, import your Personality Color data set and name it colors.
Then, export, upload, import your Stress/Chill data set and name it stress.

Looking at Stress/Chill

How do people’s personality colors and/or sports participation affect their stress levels?

We already have data about personality color and a seperate data set about stress.
- What we don’t have is a single data set with information from both … yet.
We’ll start then by strategizing how to merge our data together.

Before we merge data, we need to decide how we plan to merge it:
We can stack our data sets, that is, take one data set’s rows and add them to the bottom of the other data set.
We can also join our data sets horizontally. This is where we take one data set’s columns and add them to the end of the other data set’s columns based on matching an ID variable.
- The ID variable will have entries that we use to match observations in both data sets.
To answer the statistical question of interest, would it make more sense to stack or join our colors and stress data?

Look at the names of the variables in each data set.
- To merge different data sets together, we need to find variables they have in common.
Which variables do the data sets have in common?
Which variable would make sense to merge the data sets together with? Why not the others?

Whether stacking or joining, we need to be careful when we merge data:
When stacking data, we need to be absolutely certain that the variables we’re stacking represent the exact same measurements.
- We wouldn’t want to stack height in meters and height in inches, for instance (without converting one to the other).
When joining data, we need to make sure that the id variable in our primary data set matches to one and only one observation in the joining data.
- Otherwise, R won’t know which observation to match to.

Our goal is to add the variables from the colors data onto the stress data.
Start by ensuring that every user.id in the colors data is unique.
- If there’s a duplicate, have your teacher remove the duplicate from your class’ Web Response Manager and then re-export, upload, import your colors data.
After we add the data from colors to stress, how many rows should our merged data have? Write this number down.

We can use the merge function to join our data sets together using the variables that appear in both sets.
Fill in the blanks below to join the information from the colors data onto the stress.

merge(____, ____, by = "____")

Assign this merged data set the name stress_colors.
- Make sure your data has the same number of observations that you wrote down on the previous slide.

View your merged data and make sure nothing appears to be blatantly wrong with it.
Why didn’t we stack the rows of data instead?
What happens if you swap the order of the data sets in the merge function?
Fill in the blank below to save our stress_color data for later use.

save(stress_colors, file = "stress_colors.rda")

In the next lab, we’ll begin analyzing our merged data. In the meantime:
Make a few plots using variables from the stress data and facet or group the plots based on variables from the colors data.
- Write down the most interesting discovery you make by just exploring your data. Write out how you found your discovery and interpret what it means for the people in your class.
With our colors data, we could answer questions about the typical color scores in your class. Why can we no longer answer this question in our stress_color data?