Google has recently changed its API requirements, and ggmap users are now required to register with Google. From a user’s perspective, there are essentially three ramifications of this:
Users must register with Google. You can do this at https://cloud.google.com/maps-platform/. While it will require a valid credit card (sorry!), there seems to be a fair bit of free use before you incur charges, and even then the charges are modest for light use.
Users must enable the APIs they intend to use. What may appear to ggmap users as one overarching “Google Maps” product, Google in fact has several services that it provides as geo-related solutions. For example, the Maps Static API provides map images, while the Geocoding API provides geocoding and reverse geocoding services. Apart from the relevant Terms of Service, generally ggmap users don’t need to think about the different services. For example, you just need to remember that get_googlemap() gets maps, geocode() geocodes (with Google, DSK is done), etc., and ggmap handles the queries for you. However, you do need to enable the APIs before you use them. You’ll only need to do that once, and then they’ll be ready for you to use. Enabling the APIs just means clicking a few radio buttons on the Google Maps Platform web interface listed above, so it’s easy.
Inside R, after loading the new version of ggmap, you’ll need provide ggmap with your API key, a hash value (think string of jibberish) that authenticates you to Google’s servers. This can be done on a temporary basis with register_google(key = "[your key]") or permanently using register_google(key = "[your key]", write = TRUE). If you use the former, know that you’ll need to re-do it every time you reset R.
Your API key is private and unique to you, so be careful not to share it online, for example in a GitHub issue or saving it in a shared R script file. If you share it inadvertantly, just get on Google’s website and regenerate your key - this will retire the old one. Keeping your key private is made a bit easier by ggmap scrubbing the key out of queries by default, so when URLs are shown in your console, they’ll look something like key=xxx. (Read the details section of the register_google() documentation for a bit more info on this point.)
We hope the new version of ggmap will be on CRAN soon, but until then you can install the version here with:
if(!requireNamespace("devtools")) install.packages("devtools")
devtools::install_github("dkahle/ggmap")The details of the readme below will be changed shortly to reflect these changes. Thanks for your patience!
ggmap is an R package that makes it easy to retrieve raster map tiles from popular online mapping services like Google Maps and Stamen Maps and plot them using the ggplot2 framework:
library("ggmap")
us <- c(left = -125, bottom = 25.75, right = -67, top = 49)
get_stamenmap(us, zoom = 5, maptype = "toner-lite") %>% ggmap()
Use qmplot() in the same way you’d use qplot(), but with a map automatically added in the background:
library("dplyr")
library("forcats")
# define helper
`%notin%` <- function(lhs, rhs) !(lhs %in% rhs)
# reduce crime to violent crimes in downtown houston
violent_crimes <- crime %>%
filter(
offense %notin% c("auto theft", "theft", "burglary"),
-95.39681 <= lon & lon <= -95.34188,
29.73631 <= lat & lat <= 29.78400
) %>%
mutate(
offense = fct_drop(offense),
offense = fct_relevel(offense, c("robbery", "aggravated assault", "rape", "murder"))
)
# use qmplot to make a scatterplot on a map
qmplot(lon, lat, data = violent_crimes, maptype = "toner-lite", color = I("red"))
All the ggplot2 geom’s are available. For example, you can make a contour plot with geom = "density2d":
In fact, since ggmap’s built on top of ggplot2, all your usual ggplot2 stuff (geoms, polishing, etc.) will work, and there are some unique graphing perks ggmap brings to the table, too.
robberies <- violent_crimes %>% filter(offense == "robbery")
qmplot(lon, lat, data = violent_crimes, geom = "blank",
zoom = 14, maptype = "toner-background", darken = .7, legend = "topleft"
) +
stat_density_2d(aes(fill = ..level..), geom = "polygon", alpha = .3, color = NA) +
scale_fill_gradient2("Robbery\nPropensity", low = "white", mid = "yellow", high = "red", midpoint = 650)
Faceting works, too:
qmplot(lon, lat, data = violent_crimes, maptype = "toner-background", color = offense) +
facet_wrap(~ offense)
Google Maps can be used just as easily. However, since Google Maps use a center/zoom specification, their input is a bit different:
get_googlemap("waco texas", zoom = 12) %>% ggmap()
# Source : https://maps.googleapis.com/maps/api/staticmap?center=waco%20texas&zoom=12&size=640x640&scale=2&maptype=terrain&key=xxx
# Source : https://maps.googleapis.com/maps/api/geocode/json?address=waco+texas&key=xxx
Moreover, you can get various different styles of Google Maps with ggmap (just like Stamen Maps):
get_googlemap("waco texas", zoom = 12, maptype = "satellite") %>% ggmap()
get_googlemap("waco texas", zoom = 12, maptype = "hybrid") %>% ggmap()
get_googlemap("waco texas", zoom = 12, maptype = "roadmap") %>% ggmap()Google’s geocoding and reverse geocoding API’s are available through geocode() and revgeocode(), respectively:
geocode("1301 S University Parks Dr, Waco, TX 76798")
# Source : https://maps.googleapis.com/maps/api/geocode/json?address=1301+S+University+Parks+Dr,+Waco,+TX+76798&key=xxx
# # A tibble: 1 x 2
# lon lat
# <dbl> <dbl>
# 1 -97.1 31.6
revgeocode(c(lon = -97.1161, lat = 31.55098))
# Source : https://maps.googleapis.com/maps/api/geocode/json?latlng=31.55098,-97.1161&key=xxx
# Multiple addresses found, the first will be returned:
# 1301 S University Parks Dr, Waco, TX 76706, USA
# 55 Baylor Ave, Waco, TX 76706, USA
# 1437 FM434, Waco, TX 76706, USA
# Bear Trail, Waco, TX 76706, USA
# Robinson, TX 76706, USA
# Waco, TX, USA
# McLennan County, TX, USA
# United States
# [1] "1301 S University Parks Dr, Waco, TX 76706, USA"There is also a mutate_geocode() that works similarly to dplyr’s mutate() function:
tibble(address = c("white house", "", "waco texas")) %>%
mutate_geocode(address)
# Source : https://maps.googleapis.com/maps/api/geocode/json?address=white+house&key=xxx
# Source : https://maps.googleapis.com/maps/api/geocode/json?address=waco+texas&key=xxx
# # A tibble: 3 x 3
# address lon lat
# <chr> <dbl> <dbl>
# 1 white house -77.0 38.9
# 2 "" NA NA
# 3 waco texas -97.1 31.5Treks use Google’s routing API to give you routes (route() and trek() give slightly different results; the latter hugs roads):
trek_df <- trek("houson, texas", "waco, texas", structure = "route")
# Source : https://maps.googleapis.com/maps/api/directions/json?origin=houson,+texas&destination=waco,+texas&key=xxx&mode=driving&alternatives=false&units=metric
qmap("college station, texas", zoom = 8) +
geom_path(
aes(x = lon, y = lat), colour = "blue",
size = 1.5, alpha = .5,
data = trek_df, lineend = "round"
)
# Source : https://maps.googleapis.com/maps/api/staticmap?center=college%20station,%20texas&zoom=8&size=640x640&scale=2&maptype=terrain&language=en-EN&key=xxx
# Source : https://maps.googleapis.com/maps/api/geocode/json?address=college+station,+texas&key=xxx
(They also provide information on how long it takes to get from point A to point B.)
Map distances, in both length and anticipated time, can be computed with mapdist()). Moreover the function is vectorized:
mapdist(c("houston, texas", "dallas"), "waco, texas")
# Source : https://maps.googleapis.com/maps/api/distancematrix/json?origins=dallas&destinations=waco,+texas&key=xxx&mode=driving
# Source : https://maps.googleapis.com/maps/api/distancematrix/json?origins=houston,+texas&destinations=waco,+texas&key=xxx&mode=driving
# # A tibble: 2 x 9
# from to m km miles seconds minutes hours mode
# <chr> <chr> <int> <dbl> <dbl> <int> <dbl> <dbl> <chr>
# 1 houston, tex… waco, texas 298570 299. 186. 10297 172. 2.86 drivi…
# 2 dallas waco, texas 152822 153. 95.0 5394 89.9 1.50 drivi…From CRAN: install.packages("ggmap")
From Github: devtools::install_github("dkahle/ggmap")