Deploying to gh-pages from @ be67608 🚀

Author: grantmcdermott

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     "href": "vignettes/introduction.html#footnotes",
     "title": "Introduction to tinyplot",
     "section": "Footnotes",
-    "text": "Footnotes\n\n\nAt this point, experienced base plot users might protest that you can colour by groups using the col argument, e.g. with(aq, plot(Day, Temp, col = Month)). This is true, but there are several limitations. First, you don’t get an automatic legend. Second, the base plot.formula method doesn’t specify the grouping within the formula itself (not a deal-breaker, but not particularly consistent either). Third, and perhaps most importantly, this grouping doesn’t carry over to line plots (i.e., type=“l”). Instead, you have to transpose your data and use matplot. See this old StackOverflow thread for a longer discussion.↩︎\nSee the accompanying help pages of those two functions for more details on the available palettes, or read Zeileis & Murrell (2023, The R Journal, doi:10.32614/RJ-2023-071).↩︎\nFor example, if you have installed the ggsci package (link) then you could use palette = ggsci::pal_npg() to generate a palette consistent with those used by the Nature Publishing Group.↩︎\nThe grouped setting here makes this visualization equivalent to predict(lm(Temp ~ 0 + Month / Day, data = aq), interval = \"confidence\").↩︎\nThe default dimensions are 7x7, with a resolution of 300 DPI. However, these too can be customized via the file.width, file.height, and file.res parameters in tpar().↩︎"
+    "text": "Footnotes\n\n\nAt this point, experienced base plot users might protest that you can colour by groups using the col argument, e.g. with(aq, plot(Day, Temp, col = Month)). This is true, but there are several limitations. First, you don’t get an automatic legend. Second, the base plot.formula method doesn’t specify the grouping within the formula itself (not a deal-breaker, but not particularly consistent either). Third, and perhaps most importantly, this grouping doesn’t carry over to line plots (i.e., type=“l”). Instead, you have to transpose your data and use matplot. See this old StackOverflow thread for a longer discussion.↩︎\nSee the accompanying help pages of those two functions for more details on the available palettes, or read Zeileis & Murrell (2023, The R Journal, doi:10.32614/RJ-2023-071).↩︎\nFor example, if you have installed the ggsci package (link) then you could use palette = pal_npg() to generate a palette consistent with those used by the Nature Publishing Group.↩︎\nThe grouped setting here makes this visualization equivalent to predict(lm(Temp ~ 0 + Month / Day, data = aq), interval = \"confidence\").↩︎\nThe default dimensions are 7x7, with a resolution of 300 DPI. However, these too can be customized via the file.width, file.height, and file.res parameters in tpar().↩︎"
   }
 ]

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@@ -759,7 +759,7 @@ <h2 class="anchored" data-anchor-id="conclusion">Conclusion</h2>
 <ol>
 <li id="fn1"><p>At this point, experienced base plot users might protest that you <em>can</em> colour by groups using the <code>col</code> argument, e.g. <code>with(aq, plot(Day, Temp, col = Month))</code>. This is true, but there are several limitations. First, you don’t get an automatic legend. Second, the base <code>plot.formula</code> method doesn’t specify the grouping within the formula itself (not a deal-breaker, but not particularly consistent either). Third, and perhaps most importantly, this grouping doesn’t carry over to line plots (i.e., type=“l”). Instead, you have to transpose your data and use <code>matplot</code>. See <a href="https://stackoverflow.com/questions/10519873/how-to-create-a-line-plot-with-groups-in-base-r-without-loops">this</a> old StackOverflow thread for a longer discussion.<a href="#fnref1" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
 <li id="fn2"><p>See the accompanying help pages of those two functions for more details on the available palettes, or read Zeileis &amp; Murrell (2023, The R Journal, <a href="https://doi.org/10.32614/RJ-2023-071">doi:10.32614/RJ-2023-071</a>).<a href="#fnref2" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
-<li id="fn3"><p>For example, if you have installed the <strong>ggsci</strong> package (<a href="https://nanx.me/ggsci/index.html">link</a>) then you could use <code>palette = ggsci::pal_npg()</code> to generate a palette consistent with those used by the Nature Publishing Group.<a href="#fnref3" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
+<li id="fn3"><p>For example, if you have installed the <strong>ggsci</strong> package (<a href="https://nanx.me/ggsci/index.html">link</a>) then you could use <code>palette = pal_npg()</code> to generate a palette consistent with those used by the Nature Publishing Group.<a href="#fnref3" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
 <li id="fn4"><p>The grouped setting here makes this visualization equivalent to <code>predict(lm(Temp ~ 0 + Month / Day, data = aq), interval = "confidence")</code>.<a href="#fnref4" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
 <li id="fn5"><p>The default dimensions are 7x7, with a resolution of 300 DPI. However, these too can be customized via the <code>file.width</code>, <code>file.height</code>, and <code>file.res</code> parameters in <a href="https://grantmcdermott.com/tinyplot/man/tpar.html"><code>tpar()</code></a>.<a href="#fnref5" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
 </ol>