Questions for you to answer are as quoted blocks of text. Put your code used to address these questions and interpretation below each block. Make sure your answers are NOT in block quotes like these. Load all libraries you want to use in the setup chunk; when you discover you want to use a library later, add it to the setup chunk at the top. You will turn in Part 1 for the coming week and Part 2 for the following week. You will upload the entire template each time, with whatever progress you have made.

Part 1

Getting the data in

Download the data from It is a plain text file of data, about 60 MB in size. Values are separated with commas (you can see this by opening it with a good text editor, e.g. Atom or Sublime but not Notepad). Save it in the same folder as this template and read the file into R. You will want to use the cache=TRUE chunk option for this (and potentially other chunks). cache=TRUE will allow R to read the file only once to save time.


Inspecting the data

Use glimpse() to look at the data. Describe the data in their current state. How many rows are there? What variables are there? What kinds of values do they take (don’t list them all if there are many)? Are the column types sensible?



In addition to looking generally, look at each variable individually… except consider LEG, CC and CG at the same time (I will tell you now these three aren’t likely to be useful to you, but maybe guess what they are!). Remember these are real administrative data so they may be really strangely structured and some variables are indecipherable; in real world data work, you often have to get by with intuition or poking around online with regard to the nature of your data. Here useful way to look at 10 unique values of individual columns, given some data and a variable of interest:

data %>%
    distinct(variable) %>%

Another thing you may want to do is get a frequency (count) of distinct values:

data %>% count(variable)













Notice something odd about CounterType in particular? It tells you what a given row of votes was for… but it also has Registered Voters and Times Counted. What is Times Counted?



The quantities of interest

We will focus on only the three major executive races in Washington in 2016:

  • President (and Vice-President)
  • Governor
  • Lieutenant Governor

With these races, we are interested in: 1. Turnout rates for each of these races in each precinct. We will measure turnout as times votes were counted (including for a candidate, blank, write-in, or “over vote”) divided by the number of registered voters. 2. Differences between precincts in Seattle and precincts elsewhere in King County. Again, these data are not documented, so you will have to figure out how to do this. 3. Precinct-level support for the Democratic candidates in King County in 2012 for each contest. We will measure support as the percentage of votes in a precinct for the Democratic candidate out of all votes for candidates or write-ins. Do not include blank votes or “over votes” (where the voter indicated multiple choices) in the overall vote count for the denominator.

You will perform most of the data management for #1 and #2 in Part 1. Part 2 will contain most of the work for #3 and also covers visualizing results.

The goal to accomplish over Parts 1 and 2 will be to get the data to one row per precinct with the following 7 columns:

  • Precinct identifier
  • Indicator for whether the precinct is in Seattle or not
  • Precinct size in terms of registered voters
  • Turnout rate
  • Percentage Democratic support for President
  • Percentage Democratic support for Governor
  • Percentage Democratic support for Lieutenant Governor

The sections below describe steps you may want to do to get your data organized, and provide some hints and suggestions for methods, in particular using dplyr and tidyr.

Filtering down the data

For what we want to do, there are a lot of rows that are not useful. We only want ones pertaining to races for President, Governor, and Lieutenant Governor. So let’s trim everything down. You will want to see how these things show up in the data. The easiest way may be to (1) display every unique value of Race and find which ones match our races of interest, then (2) filter the data to those races.


Seattle precincts

We want to determine which precincts are in Seattle and which are not. You should look at values of the Precinct variable and see if you can figure out what uniquely identifies Seattle precincts. Hint: All Seattle tracts have the same naming scheme… but some non-Seattle tracts are similar so be careful!

You will then want to create a binary variable that identifies Seattle tracts (for instance, with values "Seattle" and "Not Seattle"). One approach: You can use stringr::str_sub() or base R’s substr() to grab a number of characters—a sub-string—from text (say, to test if they equal something); if you use this with ifelse() inside mutate() you can make a new variable based on whether the sub-string of Precinct equals a value.


Registered voters and turnout rates

We want to calculate turnout rates as total votes (including normal votes, blank votes, over votes, write-ins) for the Presidential race divided by registered voters. \(Turnout = \frac{Total Votes}{Registered Voters}\). Hint: You will want to look at CounterType and SumOfCount at the same time, within each Precinct and Race. Examine how the SumOfCount values for CounterType value "Times Counted" relate to all the other CounterType values.


That’s it for Part 1!

Part 2

Democratic support rates

We want to get measures of democratic support in each Precinct for each of our three races. You are asked to measure support as the percentage of votes in a precinct for the Democratic candidate out of all votes for candidates or write-ins, but this time do not to include blank votes or “over votes” (where the voter indicated multiple choices) in the overall vote count for the denominator. Hint: A good approach here is to compute the total votes (denominator) for each precinct, and then merge (e.g. left_join()) on the Democratic vote count for each race (numerator) and divide by the total votes. That is, \(Dem Support = \frac{Dem Count}{Total Votes}\).

Computing candidate votes

You will probably want to follow a process like this:

  1. Make a new dataframe with the total number of votes cast for any actual candidates (including "Write-In") in each precinct and race. Hint: You will likely want to use filter() followed by group_by() and summarize() using the SumOfCount variable.
  1. Make another dataframe with the total number of votes for democratic candidates in each precinct and race. You will want to check the Party of candidates and work only with the democratic observations to get these vote counts. Hint: There are different democratic parties for different races (e.g. "Dem" and "DPN").
  1. Merge the total votes data with the democratic votes data, then calculate a percent democratic votes variable for each race.

Combining it all

Once you’ve calculated democratic voting percentages for each race you’ll want to put them back together with the precinct turnout rate data using a join. Then you will want to make sure your data are shaped as I recommend above: One row per precincts, with columns for each of the relevant measures. If your data are in a format where you have a row for each race within each precinct (“long format”), you may find the pivot_wider() command useful for turning multiple rows for each precinct into single precinct rows with different columns for each race.


Graphing the results


Make a scatterplot where the horizontal axis (x=) is number of registered voters in the precinct, and the vertical axis (y=) is turnout rate. Color (color=) the precincts in Seattle one color, and use a different color for other precincts. Do you observe anything?


Democratic support

Now let’s visualize the Democratic support rates for the three races within each precinct for sufficently large precincts. Limit the data to precincts with at least 500 registered voters (use filter()). Make a line plot where the horizontal axis (x=) indicates precincts, and the vertical axis (y=) shows the Democratic support rates. There should be three lines in different colors (one for each race of interest).

Do not label the precincts on the horizontal axis: scale_x_discrete(breaks=NULL) is one method for doing this. You should, however, arrange them on the axis in order from smallest to largest in terms of support for the Democratic candidate for president—that is, the line plotting percentage support for Obama should be smoothly increasing from left to right. The order of the lines in the legend should follow the order of the lines at the right edge of the plot.

To do this, we need to use the “wide” version of the data (one row per precinct), and order Precinct based on Democratic support for the Presidential race (Hint: You will probably want to use forcats::fct_reorder() on Precinct). Then we can reshape back from “wide” to “tidy” form using pivot_longer() so that we have one variable giving the race—and another giving vote percentage—and can plot a separate line for each race.