# What’s the FREQ?

Directions: Follow along with the slides and answer the questions in red font in your journal.

# Clean it up!

• In Lab 1F, we saw how we could clean data to make it easier to use and analyze.
• Using the data you cleaned, we can start analyzing a small set of variables from the American Time Use (ATU) survey.
• The process of cleaning and then analyzing data is very common in Data Science.
• In this lab, we’ll learn how we can create frequency tables to detect relationships between categorical variables.
• Use the data() function to load the atu_clean data file to use in this lab.

# How do we summarize categorical variables?

• When we’re dealing with categorical variables, we can’t just calculate an average to describe a typical value.
• (Honestly, what’s the average of categories orange, apple and banana, for instance?)
• When trying to describe categorical variables with numbers, we calculate frequency tables

# Frequency tables?

• When it comes to categories, about all you can do is count or tally how often each category comes up in the data.
• Fill in the blanks below to answer the following: How many more females than males are there in our ATU data??
tally(~ ____, data = ____)

# 2-way Frequency Tables

• Counting the categories of a single variable is nice, but often times we want to make comparisons.
• Use a line of code, that’s similar to how we facet plots, to tally the number of people with physical challenges and their genders.
• Does one gender seem to have a higher occurence of physical challenges than the other? If so, which one and explain your reasoning?

# Interpreting 2-way frequency tables

• Recall that there were 1153 more women than men in our data set.
• If there are more women, then we might expect women to have more physical challenges (compared to men).
• Instead of using counts we use percentages.
• Include: format = "percent" as option to the code you used to make your 2-way frequency table. Then answer this question again:
• Does one gender seem to have a higher occurence of physical challenges than the other? If so, which one and explain your reasoning?
• Did your answer change from before? Why?

# One final option

• It’s often helpful to display totals in our 2-way frequency tables.
• To include them, include margins = TRUE as an option in the tally function.

# On your own

• Describe what happens if you create a 2-way frequency table with a numerical variable and a categorical variable.
• How are the types of statistical questions that 2-way frequency tables can answer different than 1-way frequency tables?
• Which gender has a higher rate of part time employment?
• Does one gender socialize more than the other? To answer this question first:
• Create a subset of the ATU data that includes only people who socialized more than 0 minutes.
• Create a histogram and include type = "percent" as an option in the function.