Editing Statistics and Statistical Programming (Winter 2021)/Problem set 7
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=== PC2. Learn about the Open Policing data === | === PC2. Learn about the Open Policing data === | ||
Review the project overview on the [https://openpolicing.stanford.edu/ SOPP homepage], the [https://openpolicing.stanford.edu/data/ overview of the data], the [https://github.com/stanford-policylab/opp/blob/master/data_readme.md#description-of-standardized-data description of the standardized data], the [https://github.com/stanford-policylab/opp/blob/master/data_readme.md#statewide- | Review the project overview on the [https://openpolicing.stanford.edu/ SOPP homepage], the [https://openpolicing.stanford.edu/data/ overview of the data], the [https://github.com/stanford-policylab/opp/blob/master/data_readme.md#description-of-standardized-data description of the standardized data], the [https://github.com/stanford-policylab/opp/blob/master/data_readme.md#statewide-il codebook/notes for the Washington data] from the [https://github.com/stanford-policylab/opp/blob/master/data_readme.md data_readme.md], as well as any other ancillary materials that you can find that seem likely to help you get oriented with the data. | ||
For the questions below we'll focus on the following measures recorded for each traffic stop in Washington 2009-2018: <code>date</code>, <code>subject_age</code>, <code>subject_race</code>, <code>subject_sex</code>, and <code>search_conducted</code>. | For the questions below we'll focus on the following measures recorded for each traffic stop in Washington 2009-2018: <code>date</code>, <code>subject_age</code>, <code>subject_race</code>, <code>subject_sex</code>, and <code>search_conducted</code>. | ||
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Calculate and report appropriate summary statistics for the outcome (<code>search_conducted</code>) and each of the predictor variables we care about (<code>date</code>, <code>subject_age</code>, <code>subject_race</code>, and <code>subject_sex</code>). Include visual and/or tabular summaries where appropriate. Attempt, when possible, to write efficient/elegant code that avoids unnecessary repetition while also retaining clarity. | Calculate and report appropriate summary statistics for the outcome (<code>search_conducted</code>) and each of the predictor variables we care about (<code>date</code>, <code>subject_age</code>, <code>subject_race</code>, and <code>subject_sex</code>). Include visual and/or tabular summaries where appropriate. Attempt, when possible, to write efficient/elegant code that avoids unnecessary repetition while also retaining clarity. | ||
=== PC4. Summarize | === PC4. Summarize aggregate relationships between outcome and predictor variables === | ||
The outcome variable we care about here is a dichotomous indicator for whether each traffic stop resulted in a police search (of either the driver or the vehicle) being conducted. Summarize the relationship between each of the predictor variables (<code>date</code>, <code>subject_age</code>, <code>subject_race</code>, and <code>subject_sex</code>) and the outcome variable (<code>search_conducted</code>). For continuous predictors, be sure to include visual summaries. For categorical predictors, focus on providing cross-tabulations that report conditional summary statistics within groups (for example, compare the numbers of searches conducted across the two categories of <code>subject_sex</code>). | The outcome variable we care about here is a dichotomous indicator for whether each traffic stop resulted in a police search (of either the driver or the vehicle) being conducted. Summarize the relationship between each of the predictor variables (<code>date</code>, <code>subject_age</code>, <code>subject_race</code>, and <code>subject_sex</code>) and the outcome variable (<code>search_conducted</code>). For continuous predictors, be sure to include visual summaries. For categorical predictors, focus on providing cross-tabulations that report conditional summary statistics within groups (for example, compare the numbers of searches conducted across the two categories of <code>subject_sex</code>). | ||
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=== PC5. Analyze relationships between driver race/ethnicity and vehicle searches over time === | === 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 | 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 at minimum, you should produce the following: | ||
# A plot of the number of stops and searches across the entire sample within each month. | |||
# A plot of the number of stops | |||
# A plot of the number of stops within each <code>subject_race</code> category 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 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. | # A plot of the proportion all searches accounted for within each <code>subject_race</code> category each month. | ||
Here's a suggestion for how you might approach this: | |||
Here's | |||
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: | 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 | * date as a month/year (should be a date or date-time object) | ||
* race/ethnicity (from the <code>subject_race</code> variable) | * 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 stops (within the <code>subject_race</code> group identified for the row) | ||
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* total number of searches that month/year | * total number of searches that month/year | ||
* proportion of total searches (within the <code>subject_race</code> group identified for the row). | * 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).'' | ''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).'' | ||
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>. | 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>. |