Style GeoJSON

Previously on this blog and on my own personal blog, I have discussed how easy it is to create interactive maps on Github using a combination of R, git and Github. This is done using a file format called geojson, a file format based on JSON (JavaScript Object Notation) in which you can specify geographic data along with any other metadata.

In my previous post on this blog about geojson, I described how you could get data from the USGS BISON API using our rbison package, then make a geojson file, then push to Github. Here, I describe briefly how you can style your map. This time, we’ll get data from GBIF using the rgbif package.

Load package

# install_github('rgbif', 'ropensci')
library(rgbif)

Get data from GBIF

splist <- c("Accipiter erythronemius", "Junco hyemalis", "Aix sponsa")
out <- occurrencelist_many(splist, coordinatestatus = TRUE, maxresults = 100)
dat <- gbifdata(out)
head(dat)

                taxonName decimalLatitude decimalLongitude
1 Accipiter erythronemius          -25.91           -54.36
2 Accipiter erythronemius          -25.86           -54.52
3 Accipiter erythronemius          -25.86           -54.52
4 Accipiter erythronemius          -27.35           -65.60
5 Accipiter erythronemius          -27.35           -65.60
6 Accipiter erythronemius          -27.35           -65.60

Style

We first need to convert column names to be latitude and longitude

names(dat)[names(dat) %in% c("decimalLatitude", "decimalLongitude")] <- c("latitude",
    "longitude")

Then use a new function stylegeojson to simply add new columns to the data.frame. With stylegeojson you can add marker colors, symbols, and size. Note that this only works for point/marker maps (not polygon/line) for now. You could also simply edit the geojson file, but that can get very tedious with large files. You can specify color, symbol type, and symbol size all for the same variable, or each of those for different variables - where the ordering of the vector of colors, symbol types and sizes follows the ordering of the unique variable levels. Here we specify a separate color and size for each of the three species within the taxonName variable.

dat <- stylegeojson(input = dat, var = "taxonName", color = c("#976AAE", "#6B944D",
    "#BD5945"), size = "small")
head(dat)

                taxonName latitude longitude marker-color marker-size
1 Accipiter erythronemius   -25.91    -54.36      #976AAE       small
2 Accipiter erythronemius   -25.86    -54.52      #976AAE       small
3 Accipiter erythronemius   -25.86    -54.52      #976AAE       small
4 Accipiter erythronemius   -27.35    -65.60      #976AAE       small
5 Accipiter erythronemius   -27.35    -65.60      #976AAE       small
6 Accipiter erythronemius   -27.35    -65.60      #976AAE       small

Here, specify one color for all three species, and make them all size medium.

dat <- stylegeojson(input = dat, var = "taxonName", color = "#6B944D", size = "medium")

Then write the data.frame to disk, and convert to a geojson file using the function togeojson.

write.csv(dat, "~/github/sac/mygeojson/rgbif_data.csv")
file <- "~/github/sac/mygeojson/rgbif_data.csv"
togeojson(file, method = "web", destpath = "~/github/sac/mygeojson/", outfilename = "rgbif_data")

Git ‘er done

All we need to do now is go to the command line or your git GUI client and push the changes to Github. If you need help with that see Step 3 in the previous geojson post.

Go here to see the map.

That’s it. have a look at your map, have fun, and let us know if you have any feature requests or bug reports at our Github issues tracker for rgbif here.