Lab 2G - Getting It Together

Directions: Follow along with the slides and answer the questions in bold font in your IDS Journal.

So far in the labs you've only looked at individual data files.
However, you can gain additional insights by including information from a separate data set.
In this lab you will learn how to merge information from your personality color data with your stress/chill data.
Export, upload, and import your Personality Color data set and name it colors.
Then, export, upload, and import your Stress/Chill data set and name it stress.

Now you'll analyze the research question:

How do people's personality colors and/or sports participation affect their stress levels?
You already have data about personality color and a seperate data set about stress. What you don't have is a single data set with information from both...yet. You'll start by strategizing on how to merge your data together.

Before you merge data, you'll need to decide how you plan to merge it.
You can stack your data sets, which means you can take the rows from one data set and add them to the bottom of the other data set.
You can also join your data sets horizontally. You do this by taking one data set's columns and adding them to the end of the other data set's columns, based on matching an ID variable. The ID variable will have entries that you use to match observations in both data sets.
To answer the statistical question of interest, would it make more sense to stack or join your colors and stress data?

Look at the names of the variables in each data set. To merge different data sets together, you need to find variables they share in common.
Which variables do the data sets have in common?
If you merge data sets based on a shared variable, which variable would you choose? Why not the others?

Whether stacking or joining, you need to be careful when you merge data.
When stacking data, you must be absolutely certain that the variables you're stacking represent the exact same measurements For example, you wouldn't want to stack height in meters and height in inches without converting one to the other.
When joining data, you must make sure that the id variable in your primary data set matches to one and only one observation in the joining data. Otherwise, R won't know which observation to match to.

The 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 the IDS Response Manager, then re-export, upload, and import your colors data.
After you add the data from colors to stress, how many rows should your merged data have? Write this number down.

You can use the merge function to join your 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 data.
```
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 obviously wrong with it.
Why didn't you 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 your stress_color data for later use.
```
save(stress_colors, file = "stress_colors.rda")
```
Be sure to look in the Files tab to make sure your data was saved.