-## # … with 28 more rows</code></pre>
-<p>There’s quite a lot happening there. I’ll go through it verb-by-verb.</p>
-<p>First, I use <code>mutate</code> to create a <code>diff_cases</code> variable that disaggregates the cumulative values of <code>cases</code> (read the documentation for <code>diff</code> to learn more about this one). Differenced values alone wouldn’t produce the same number of items (try running <code>length(1:10)</code> and compare that with <code>length(diff(1:10, 1))</code> to see what I mean), so I stores the first value of my <code>cases</code> variable and then append the differenced values after that. Within the same call to mutate I also create a new variable <code>weekdate</code> that collapses the dates into weeks (see the documentation for <code>cut.Date</code>) and stores the resulting strings as factors (e.g., a factor where the levels correspond to a series of Mondays: “2020-01-20”, “2020-01-27”…). Hopefully, so far so good?</p>
-<p>Next, I use <code>group_by</code> to aggregate everything by my <code>weekdate</code> factor values.</p>
-<p>Finally I use <code>summarize</code> to reshape my data and collapse everything into weekly counts of new cases (notice that I use <code>sum</code> inside the <code>summarize</code> call to add up the case counts within the grouping variable). Okay, let’s see about plotting this now:</p>
-<p>Hmm. looks like I have a problem with my dates. Let’s troubleshoot this:</p>
+## # … with 29 more rows</code></pre>
+<p>There’s quite a lot happening there so let’s go through it verb-by-verb.</p>
+<p>First, I <code>filter</code> my cases to restrict the set to Illinois data. Then I use <code>mutate</code> to create a <code>diff_cases</code> variable that disaggregates the cumulative values of <code>cases</code> (read the documentation for <code>diff</code> to learn more about this one). Differenced values alone wouldn’t produce the correct number of items (try running <code>length(1:10)</code> and compare that with <code>length(diff(1:10, 1))</code> to see what I mean), so I store the first value of my <code>cases</code> variable and then append the differenced values after that (Note that this assumes and takes advantage of the fact that the data is sorted by date. I could add a call to <code>arrange(-desc())</code> before doing my mutation to ensure the correct ordering, but won’t bother with that for now). Within the same call to mutate I also create a new variable <code>weekdate</code> that collapses the dates into weeks (see the documentation for <code>cut.Date</code>) and stores the resulting strings as factors (e.g., a factor where the levels correspond to a series of Mondays: “2020-01-20”, “2020-01-27”…). Hopefully, so far so good?</p>
+<p>Next, I use <code>group_by</code> to aggregate everything by my <code>weekdate</code> factor values. This is essentially creating conditional groupings of the data that I can then summarize in my next command.</p>
+<p>Finally I use <code>summarize</code> to reshape my data and collapse everything into weekly counts of new cases (notice that I use <code>sum</code> inside the <code>summarize</code> call to add up the case counts within the grouping variable). The result is a brand new two-column tibble consisting of weekdates and weekly counts of new cases. Excellent!</p>
+<p>Okay, let’s see about plotting this now:</p>
+<pre class="r"><code>il_weekly_cases %>%
+ ggplot(aes(weekdate, new_cases)) +
+ geom_line()</code></pre>
+<p><img src="data:image/png;base64,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" width="672" /></p>
+<p>Hmm. looks like I have a problem here. My first guess is that there’s something funny going on with my <code>weekdate</code> variable because it looks very different on the x-axis. Let’s troubleshoot:</p>