r - Is there a way to add a scale bar (for linear distances) to ggmap?

Question

Not that it's critical to my question, but here is my plot example, on top of which I'd like to add a scale bar.

ggmap(get_map(location = "Kinston, NC", zoom = 12, maptype = 'hybrid')) +
geom_point(x = -77.61198, y = 35.227792, colour = "red", size = 5) +
geom_point(x = -77.57306, y = 35.30288, colour = "blue", size = 3) +
geom_point(x = -77.543, y = 35.196, colour = "blue", size = 3) +
geom_text(x = -77.575, y = 35.297, label = "CRONOS Data") +
geom_text(x = -77.54, y = 35.19, label = "NOAA") +
geom_text(x = -77.61, y = 35.22, label = "PP Site")

Answer 1

There are a few things you need to do to make this happen.

First is to put your data into a data.frame():

sites.data = data.frame(lon = c(-77.61198, -77.57306, -77.543),
                        lat = c(35.227792, 35.30288, 35.196),
                        label = c("PP Site","NOAA", "CRONOS Data"),
                        colour = c("red","blue","blue"))

Now we can get the map for this region using the gg_map package:

require(gg_map)
map.base <- get_map(location = c(lon = mean(sites.data$lon),
                                 lat = mean(sites.data$lat)),
                    zoom = 10) # could also use zoom = "auto"

We'll need the extents of that image:

bb <- attr(map.base,"bb")

Now we start figuring out the scale. First, we need a function give us the distance between two points, based on lat/long. For that, we use the Haversine formula, described by Floris at Calculate distance in (x, y) between two GPS-Points:

distHaversine <- function(long, lat){

  long <- long*pi/180
  lat <- lat*pi/180  
  dlong = (long[2] - long[1])
  dlat  = (lat[2] - lat[1])

  # Haversine formula:
  R = 6371;
  a = sin(dlat/2)*sin(dlat/2) + cos(lat[1])*cos(lat[2])*sin(dlong/2)*sin(dlong/2)
  c = 2 * atan2( sqrt(a), sqrt(1-a) )
  d = R * c
  return(d) # in km
}

The next step is to work out the points that will define our scale bar. For this example, I put something in the lower left of the plot, using the bounding box that we've already figured out:

sbar <- data.frame(lon.start = c(bb$ll.lon + 0.1*(bb$ur.lon - bb$ll.lon)),
                   lon.end = c(bb$ll.lon + 0.25*(bb$ur.lon - bb$ll.lon)),
                   lat.start = c(bb$ll.lat + 0.1*(bb$ur.lat - bb$ll.lat)),
                   lat.end = c(bb$ll.lat + 0.1*(bb$ur.lat - bb$ll.lat)))

sbar$distance = distHaversine(long = c(sbar$lon.start,sbar$lon.end),
                              lat = c(sbar$lat.start,sbar$lat.end))

Finally, we can draw the map with the scale.

ptspermm <- 2.83464567  # need this because geom_text uses mm, and themes use pts. Urgh.

map.scale <- ggmap(map.base,
                   extent = "normal", 
                   maprange = FALSE) %+% sites.data +
  geom_point(aes(x = lon,
                 y = lat,
                 colour = colour)) +
  geom_text(aes(x = lon,
                y = lat,
                label = label),
            hjust = 0,
            vjust = 0.5,
            size = 8/ptspermm) +    
  geom_segment(data = sbar,
               aes(x = lon.start,
                   xend = lon.end,
                   y = lat.start,
                   yend = lat.end)) +
  geom_text(data = sbar,
            aes(x = (lon.start + lon.end)/2,
           y = lat.start + 0.025*(bb$ur.lat - bb$ll.lat),
           label = paste(format(distance, 
                                digits = 4,
                                nsmall = 2),
                         'km')),
           hjust = 0.5,
           vjust = 0,
           size = 8/ptspermm)  +
  coord_map(projection="mercator",
            xlim=c(bb$ll.lon, bb$ur.lon),
            ylim=c(bb$ll.lat, bb$ur.lat))  

Then we save it...

# Fix presentation ----
map.out <- map.scale +  
  theme_bw(base_size = 8) +
  theme(legend.justification=c(1,1), 
        legend.position = c(1,1)) 

ggsave(filename ="map.png", 
       plot = map.out,
       dpi = 300,
       width = 4, 
       height = 3,
       units = c("in"))

Which gives you something like this:

The nice thing is that all of the plotting uses ggplot2(), so you can use the documentation at http://ggplot2.org to make this look how you need.