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copied!<p>Plots using polar coordinates are certainly underused--some would say with good reason. I think the situations which justify their use are not common; I also think that when those situations arise, polar plots can reveal patterns in data that linear plots cannot. </p> <p>I think that's because sometimes your data is <em>inherently</em> polar rather than linear--eg, it is cyclical (x-coordinates representing times during 24-hour day over multiple days), or the data were previously mapped onto a polar feature space.</p> <p>Here's an example. This plot shows a Website's mean traffic volume by hour. Notice the two spikes at 10 pm and at 1 am. For the Site's network engineers, those are significant; it's also significant that they occur near each other other (just <strong>two</strong> hours apart). But if you plot the same data on a traditional coordinate system, this pattern would be completely concealed--plotted linearly, these two spikes would be <strong>20</strong> hours apart, which they are, though they are also just two hours apart on consecutive days. The polar chart above shows this in a parsimonious and intuitive way (a legend isn't necessary).</p> <p><img src="https://i.stack.imgur.com/FS6Pi.png" alt="Polar chart showing site traffic, with peaks at hours 1 and 22"></p> <p>There are two ways (that I'm aware of) to create plots like this using R (I created the plot above w/ R). One is to code your own function in either the base or grid graphic systems. They other way, which is easier, is to use the <strong>circular package</strong>. The function you would use is '<strong>rose.diag</strong>':</p> <pre><code>data = c(35, 78, 34, 25, 21, 17, 22, 19, 25, 18, 25, 21, 16, 20, 26, 19, 24, 18, 23, 25, 24, 25, 71, 27) three_palettes = c(brewer.pal(12, "Set3"), brewer.pal(8, "Accent"), brewer.pal(9, "Set1")) rose.diag(data, bins=24, main="Daily Site Traffic by Hour", col=three_palettes) </code></pre>

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