-<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>
+<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>