<p><div><div><div><div data-show-faces="false" data-height="21" data-width="100" data-layout="button_count" data-send="true" data-href="https://www.r-bloggers.com/recreating-and-updating-minard-with-ggplot2/"></div></div><div></div></div><div></div></div>
<div>
(This article was first published on <strong><a href="http://blog.revolutionanalytics.com/2017/08/recreating-minard.html"> Revolutions</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<div>
<p>Minard's <a rel="nofollow" href="http://www.edwardtufte.com/tufte/minard" target="_blank">chart depicting Napoleon's 1812 march on Russia</a> is a classic of data visualization that has <a rel="nofollow" href="http://blog.revolutionanalytics.com/2009/11/because-its-friday-minard-goes-mainstream.html" target="_blank">inspired many homages</a> using different time-and-place data. If you'd like to recreate the original chart, or create one of your own, Andrew Heiss has created a tutorial on using the <a rel="nofollow" href="https://www.andrewheiss.com/blog/2017/08/10/exploring-minards-1812-plot-with-ggplot2/" target="_blank">ggplot2 package to re-envision the chart in R</a>:</p>
<p><a rel="nofollow" href="https://www.andrewheiss.com/blog/2017/08/10/exploring-minards-1812-plot-with-ggplot2/" target="_blank"><img border="0" alt="Minard" title="Minard" src="http://revolution-computing.typepad.com/.a/6a010534b1db25970b01b8d2a315bc970c-800wi"></a></p>
<p>The R script provided in the tutorial is driven by historical data on the location and size of Napoleon's armies during the 1812 campaign, but you could adapt the script to use new data as well. Andrew also shows how to combine the chart with a geographical or satellite map, which is how the cities appear in the version above (unlike in Minard's original). </p>
<p><a rel="nofollow" href="http://a5.typepad.com/6a0105360ba1c6970c01b8d2a3166d970c-pi" target="_blank"><img border="0" alt="Minard-image" title="Minard-image" src="http://a5.typepad.com/6a0105360ba1c6970c01b8d2a3166d970c-800wi"></a></p>
<p>The data behind the Minard chart is <a rel="nofollow" href="http://www.datavis.ca/gallery/re-minard.php" target="_blank">available from Michael Friendly</a> and you can find the <a rel="nofollow" href="https://github.com/andrewheiss/fancy-minard" target="_blank">R scripts at this Github repository</a>. For the complete tutorial, follow the link below.</p>
<p>Andrew Heiss: <a rel="nofollow" href="https://github.com/andrewheiss/fancy-minard%C2%A0" target="_blank">Exploring Minard’s 1812 plot with ggplot2</a> (<a rel="nofollow" href="https://twitter.com/JennyBryan/status/895891297763971076" target="_blank">via</a> Jenny Bryan)</p>
<p> </p>
</div>
	<p>
<div>
<div>To <strong>leave a comment</strong> for the author, please follow the link and comment on their blog: <strong><a href="http://blog.revolutionanalytics.com/2017/08/recreating-minard.html"> Revolutions</a></strong>.</div>
<hr>
<a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers.com</a> offers <strong><a rel="nofollow" href="https://feedburner.google.com/fb/a/mailverify?uri=RBloggers">daily e-mail updates</a></strong> about <a rel="nofollow" href="https://www.r-project.org/" title="The R Project for Statistical Computing">R</a> news and <a rel="nofollow" href="https://www.r-bloggers.com/search/tutorial" title="R tutorials">tutorials</a> on topics such as: <a rel="nofollow" href="https://www.r-bloggers.com/search/data%20science" title="Data science">Data science</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/Big%20Data" title="Big Data">Big Data, <a rel="nofollow" href="https://www.r-users.com/" title="R jobs">R jobs</a>, visualization (<a rel="nofollow" href="https://www.r-bloggers.com/search/ggplot2" title="ggplot and ggplot2 tutorials">ggplot2</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/boxplot" title="Boxplots using lattice and ggplot2 tutorials">Boxplots</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/map" title="Maps and gis">maps</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/animation" title="Animation in R">animation</a>), programming (<a rel="nofollow" href="https://www.r-bloggers.com/search/RStudio" title="RStudio IDE for R">RStudio</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/sweave" title="Sweave and literate programming">Sweave</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/LaTeX" title="LaTeX in R">LaTeX</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/SQL" title="SQL and databases">SQL</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/eclipse" title="Eclipse IDE for R">Eclipse</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/git" title="git and github, Version Control System">git</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/hadoop" title="Large data in R using Hadoop">hadoop</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/Web+Scraping" title="Web Scraping of google, facebook, yahoo, twitter and more using R">Web Scraping</a>) statistics (<a rel="nofollow" href="https://www.r-bloggers.com/search/regression" title="Regressions and ANOVA analysis tutorials">regression</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/PCA" title="principal component analysis tutorial">PCA</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/time+series" title="Time series">time series</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/trading" title="finance trading">trading</a>) and more...
<img width="1" alt="" src="http://feeds.feedburner.com/~r/RBloggers/~4/IP3fOTDCGWc" height="1"></a></div>