Editing Statistics and Statistical Programming (Winter 2021)/Problem set 7 (section)

=== PC5. Analyze relationships between driver race/ethnicity and vehicle searches over time ===

Summarize the relationship between the recorded <code>subject_race</code> for each traffic stop and the <code>search_conducted</code> outcome over the time period covered by the dataset. You may do this in a variety of ways, but a good goal should be to should be to produce the following:

# A plot of the number of stops across the entire sample within each month
# A plot of the number of searches across the entire sample within each month.
# A plot of the number of stops within each <code>subject_race</code> category each month.
# A plot of the number of searches within each <code>subject_race</code> category each month.
# A plot of the proportion all searches accounted for within each <code>subject_race</code> category each month.

You'll need to build a dataset. My suggestion is draw out the dataset you want to build. What are the rows? What are the columns?

Here's one suggestion for how you might approach this:

1. Create a new data frame that aggregates stop and search data across sub-groups of <code>subject_race</code> per month. This object could include the following columns: 
* date as a month/year (should be a date or date-time object which will require a day. I set all of them just to "YYYY-MM-01" by rounding them down)
* race/ethnicity (from the <code>subject_race</code> variable)
* number of stops (within the <code>subject_race</code> group identified for the row)
* number of searches (within the <code>subject_race</code> group identified for the row)
* total number of searches that month/year
* proportion of total searches (within the <code>subject_race</code> group identified for the row). 

''Note that this will result in a data frame with multiple rows per month/year (as many as one row for each <code>subject_race</code> category).''

If your dataset is wide, you will need to turn it into a long dataset for graphing.

2. Use <code>ggplot2</code> and the [https://ggplot2.tidyverse.org/reference/geom_path.html <code>geom_line</code>] layer to generate each of the plots. Note that you'll want to assign <code>subject_race</code> as an aesthetic element (<code>aes</code>) for some of the plots so that ggplot2 represents each category as a separate line (maybe distinguished by color?). Make sure to incorporate useful titles, axis labels, and legends for each plot you produce. Recall that the R tutorials include examples of using <code>aes</code> with <code>ggplot2</code>.