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I had no intention to blog this, but @jayjacobs convinced me otherwise. I was curious about the recent (end of March, 2014) California earthquake “storm” and did a quick plot for “fun” and personal use using `ggmap`/`ggplot`.

I used data from the Southern California Earthquake Center (that I cleaned up a bit and that you can find here) but would have used the USGS quake data if the site hadn’t been down when I tried to get it from there.

The code/process isn’t exactly rocket-science, but if you’re looking for a simple way to layer some data on a “real” map (vs handling shapefiles on your own) then this is a really compact/self-contained tutorial/example.

You can find the code & data over at github as well.

There’s lots of ‘splainin in the comments (which are prbly easier to read on the github site) but drop a note in the comments or on Twitter if it needs any further explanation. The graphic is SVG, so use a proper browser 🙂 or run the code in R if you can’t see it here. (click for larger version)

``` library(ggplot2) library(ggmap) library(plyr) library(grid) library(gridExtra)   # read in cleaned up data dat <- read.table("quakes.dat", header=TRUE, stringsAsFactors=FALSE)   # map decimal magnitudes into an integer range dat\$m <- cut(dat\$MAG, c(0:10))   # convert to dates dat\$DATE <- as.Date(dat\$DATE)   # so we can re-order the data frame dat <- dat[order(dat\$DATE),]   # not 100% necessary, but get just the numeric portion of the cut factor dat\$Magnitude <- factor(as.numeric(dat\$m))   # sum up by date for the barplot dat.sum <- count(dat, .(DATE, Magnitude))   # start the ggmap bit # It's super-handy that it understands things like "Los Angeles" #spoffy # I like the 'toner' version. Would also use a stamen map but I can't get # to it consistently from behind a proxy server la <- get_map(location="Los Angeles", zoom=10, color="bw", maptype="toner")   # get base map layer gg <- ggmap(la)   # add points. Note that the plot will produce warnings for all points not in the # lat/lon range of the base map layer. Also note that i'm encoding magnitude by # size and color and using alpha for depth. because of the way the data is sorted # the most recent quakes in the set should be on top gg <- gg + geom_point(data=dat, mapping=aes(x=LON, y=LAT, size=MAG, fill=m, alpha=DEPTH), shape=21, color="black")   # this takes the magnitude domain and maps it to a better range of values (IMO) gg <- gg + scale_size_continuous(range=c(1,15))   # this bit makes the right size color ramp. i like the reversed view better for this map gg <- gg + scale_fill_manual(values=rev(terrain.colors(length(levels(dat\$Magnitude))))) gg <- gg + ggtitle("Recent Earthquakes in CA & NV")   # no need for a legend as the bars are pretty much the legend gg <- gg + theme(legend.position="none")     # now for the bars. we work with the summarized data frame gg.1 <- ggplot(dat.sum, aes(x=DATE, y=freq, group=Magnitude))   # normally, i dislike stacked bar charts, but this is one time i think they work well gg.1 <- gg.1 + geom_bar(aes(fill=Magnitude), position="stack", stat="identity")   # fancy, schmanzy color mapping again gg.1 <- gg.1 + scale_fill_manual(values=rev(terrain.colors(length(levels(dat\$Magnitude)))))   # show the data source! gg.1 <- gg.1 + labs(x="Data from: http://www.data.scec.org/recent/recenteqs/Maps/Los_Angeles.html", y="Quake Count") gg.1 <- gg.1 + theme_bw() #stopthegray   # use grid.arrange to make the sizes work well grid.arrange(gg, gg.1, nrow=2, ncol=1, heights=c(3,1)) ```