1. Quick maps in R Melanie Frazier, NCEAS Presentation materials here: 1

2. And, you want to see where they are
Have Lat/Long data?
And, you want to see where they are
Data from the EPA’s WestuRe project:

3. Many options: But we’ll stick with 2
library(plotKML)
library(ggmap)

4. ggmap Part 1 Download the map raster Part 2 Overlay data onto raster
Refer to Quickstart guide:

5. ggmap: Part 1 (getting the map)
Specify coordinates
- geocode
myLocation <- “University of Washington”
- Lat/Long
myLocation <- c(lon=-95.36, lat=29.76)
- Bounding box
(lowerleftlon, lowerleftlat,
upperrightlon, upperrightlat)
myLocation <- c(-130, 30, -105, 50)
[NOTE: glitchy for google maps]

6. can do any of the maps in “bw”
ggmap: Part 1 (getting the map)
B. Define map source, maptype, and color
maptype =
watercolor
toner
terrain
source
stamen
terrain
satellite
roadmap
hybrid
google
color
can do any of the maps in “bw”
osm
(and, cloudmade)

7. Scale matters in regard to map source/type
ggmap: Part 1 (getting the map)
B. Define map source, maptype, and color
Scale matters in regard to map source/type

8. ggmap: Part 1 (getting the map)
B. get_map function provides a general approach for quickly getting maps
myMap <- get_map(location = myLocation,
source = “stamen”,
maptype = “watercolor”,
color = “bw”)
Additional options such as “zoom” and “crop”
?ggmap

9. ggmap: Part 1 (getting the map)
Sometimes get_map doesn’t provide the control needed to get the map you want.
In this case, use the specific functions designed for the different map sources:
get_googlemap
get_openstreetmap
get_stamenmap
get_cloudmademap

10. ggmap: Part 2 (overlaying your data)
A. Plot the raster
ggmap(myMap)
B. Get your lat/long point data:
myData <- read.csv(“
C. Add points (ggplot2 syntax)
ggmap(myMap)+
geom_point(aes(x=estLongitude, y=estLatitude),
data=myData, alpha=0.5,
color=“darkred”, size=3)

11. ggmaps: Part 2

12. ggmaps: Part 2

13. ggmap: Additional options

14. plotKML

15. plotKML Load libraries: Tutorial: library(plotKML) library(sp)
Load libraries:
library(plotKML)
library(sp)
Convert to spatial dataframe object:
coordinates(myData) <- ~estLongitude+estLatitude
Provide the projection (just copy this):
proj4string(myData) <- CRS("+proj=longlat +datum=WGS84")
Make the plot:
plotKML(myData, colour="lnEstArea", balloon=TRUE)

16. Questions Presentation materials here: 16