Our time.

Lab 1H

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

Space, Click, Right Arrow or swipe left to move to the next slide.

We’ve come a long way

The labs until now have covered a huge range of topics:
- We’ve learned how to make plots for different types of variables.
- We know how to subset our data to get a more refined view of our data.
- We’ve covered cleaning data and making two-way frequency tables.
In this lab, we’re going to combine all of these ideas and topics together to find out how we spend our time.

First steps first.

Export, Upload, Import the data from your class’s Time Use campaign.
The data, as-is, is very messy and hard to interpret/analyze.
- Fill in the blank with the name of your imported data to format it:

timeuse <- timeuse_format( _____ )

This function formats/cleans the data so that each row represents a typical day for each student in the class.
Hint: Search your History tab for the code to save your formatted timeuse data as an R data file (.Rda).

timeuse_format specifics

In case you’re wondering, the timeuse_format function:
- Takes each student’s daily data and adds up all of the time spent doing each activity for each day.
- The time spent on each activity for each day is then averaged together to create a typical day in the life of each student.

Exploring your data

Start by getting familiar with your timeuse data:
- How many observations and variables are there?
- What are the names of the variables?
- Which row represents YOUR typical day?

How do we spend our time?

We would like to investigate the research question: “How did our class spend our time?”
- To do this, we’ll perform a statistical investigation.
State and answer two statistical investigative questions based on our research question.
- Also, state one way in which your personal data is typical and one way that it differs from the rest of the class.
Justify your answers by using appropriate statistical graphics and summary tables.
- If you subset your data, explain why and how it benefited your analysis.