Contour and Density Layers with ggmap

December 13, 2013
By

[This article was first published on Exegetic Analytics » R, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

I am busy working on a project which uses data from the World Wide Lightning Location Network (WWLLN). Specifically, I am trying to reproduce some of the results from

Orville, Richard E, Gary R. Huffines, John Nielsen-Gammon, Renyi Zhang, Brandon Ely, Scott Steiger, Stephen Phillips, Steve Allen, and William Read. 2001. “Enhancement of Cloud-to-Ground Lightning over Houston, Texas.” Geophysical Research Letters 28 (13): 2597–2600.

This is what the data look like:

> head(W)
     lat     lon    dist
1 29.775 -94.649  68.706
2 30.240 -94.270 117.872
3 29.803 -94.418  91.166
4 29.886 -94.342  99.316
5 29.892 -94.085 123.992
6 29.898 -94.071 125.458
> attributes(W)$ndays
[1] 1096

I have already pre-processed the data quite extensively and use the geosphere package to add a column giving the distances from the centre of Houston to each lightning discharge. The ndays attribute indicates the number of days included in the data.

I want to plot the density of lightning on a map of the area around Houston. The first step is to get the map data.

> library(ggmap)
> 
> houston = c(lon = -95.36, lat =  29.76)
> 
> houston.map = get_map(location = houston, zoom = 8, color = "bw")

My initial attempt at creating the map used the following:

> ggmap(houston.map, extent = "panel", maprange=FALSE) +
+   geom_density2d(data = W, aes(x = lon, y = lat)) +
+   stat_density2d(data = W, aes(x = lon, y = lat,  fill = ..level.., alpha = ..level..),
+                  size = 0.01, bins = 16, geom = 'polygon') +
+   scale_fill_gradient(low = "green", high = "red") +
+   scale_alpha(range = c(0.00, 0.25), guide = FALSE) +
+   theme(legend.position = "none", axis.title = element_blank(), text = element_text(size = 12))

And this gave a rather pleasing result. But I was a little uneasy about those contours near the edges: there was no physical reason why they should be running more or less parallel to the boundaries of the plot.

plot-1

It turns out that my suspicions were well founded. After some fiddling around I found that if I changed the extent argument then I got to see the bigger picture.

> ggmap(houston.map, extent = "normal", maprange=FALSE) %+% W + aes(x = lon, y = lat) +
+   geom_density2d() +
+   stat_density2d(aes(fill = ..level.., alpha = ..level..),
+                  size = 0.01, bins = 16, geom = 'polygon') +
+   scale_fill_gradient(low = "green", high = "red") +
+   scale_alpha(range = c(0.00, 0.25), guide = FALSE) +
+   theme(legend.position = "none", axis.title = element_blank(), text = element_text(size = 12))

You will also note here a different syntax for feeding the data into ggplot. The resulting plot shows that in my initial plot the data were being truncated at the boundaries of the plot.

plot-2

Now at least I have more realistic densities and contours. But, of course, I didn’t want all of that extra space around the map. Not a problem because we can crop the map once the contour and density layers have been added.

> ggmap(houston.map, extent = "normal", maprange=FALSE) %+% W + aes(x = lon, y = lat) +
+     geom_density2d() +
+     stat_density2d(aes(fill = ..level.., alpha = ..level..),
+                    size = 0.01, bins = 16, geom = 'polygon') +
+     scale_fill_gradient(low = "green", high = "red") +
+     scale_alpha(range = c(0.00, 0.25), guide = FALSE) +
+     coord_map(projection="mercator", 
+               xlim=c(attr(houston.map, "bb")$ll.lon, attr(houston.map, "bb")$ur.lon),
+               ylim=c(attr(houston.map, "bb")$ll.lat, attr(houston.map, "bb")$ur.lat)) +
+     theme(legend.position = "none", axis.title = element_blank(), text = element_text(size = 12))

And this gives the final plot, which I think is very pleasing indeed!

plot-3

To leave a comment for the author, please follow the link and comment on their blog: Exegetic Analytics » R.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.



If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Search R-bloggers

Sponsors

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)