ggmap: Plot a ggmap object

In ggmap: Spatial Visualization with ggplot2

Plot a ggmap object

Description

ggmap plots the raster object produced by get_map().

Usage

ggmap(
  ggmap,
  extent = "panel",
  base_layer,
  maprange = FALSE,
  legend = "right",
  padding = 0.02,
  darken = c(0, "black"),
  b,
  fullpage,
  expand,
  ...
)

Arguments

ggmap	an object of class ggmap (from function get_map)
extent	how much of the plot should the map take up? "normal", "device", or "panel" (default)
base_layer	a ggplot(aes(...), ...) call; see examples
maprange	logical for use with base_layer; should the map define the x and y limits?
legend	"left", "right" (default), "bottom", "top", "bottomleft", "bottomright", "topleft", "topright", "none" (used with extent = "device")
padding	distance from legend to corner of the plot (used with legend, formerly b)
darken	vector of the form c(number, color), where number is in (0,1) and color is a character string indicating the color of the darken. 0 indicates no darkening, 1 indicates a black-out.
b	Deprecated, renamed to padding. Overrides any padding argument.
fullpage	Deprecated, equivalent to extent = "device" when TRUE. Overrides any extent argument.
expand	Deprecated, equivalent to extent = "panel" when TRUE and fullpage is FALSE. When fullpage is FALSE and expand is FALSE, equivalent to extent="normal". Overrides any extent argument.
...	...

Value

a ggplot object

Author(s)

David Kahle [e-mail]

See Also

get_map(), qmap()

Examples

## Not run: ## map queries drag R CMD check


## extents and legends
##################################################
hdf <- get_map("houston, texas")
ggmap(hdf, extent = "normal")
ggmap(hdf) # extent = "panel", note qmap defaults to extent = "device"
ggmap(hdf, extent = "device")



# make some fake spatial data
mu <- c(-95.3632715, 29.7632836); nDataSets <- sample(4:10,1)
chkpts <- NULL
for(k in 1:nDataSets){
  a <- rnorm(2); b <- rnorm(2);
  si <- 1/3000 * (outer(a,a) + outer(b,b))
  chkpts <- rbind(
    chkpts,
    cbind(MASS::mvrnorm(rpois(1,50), jitter(mu, .01), si), k)
  )
}
chkpts <- data.frame(chkpts)
names(chkpts) <- c("lon", "lat","class")
chkpts$class <- factor(chkpts$class)
qplot(lon, lat, data = chkpts, colour = class)

# show it on the map
ggmap(hdf, extent = "normal") +
  geom_point(aes(x = lon, y = lat, colour = class), data = chkpts, alpha = .5)

ggmap(hdf) +
  geom_point(aes(x = lon, y = lat, colour = class), data = chkpts, alpha = .5)

ggmap(hdf, extent = "device") +
  geom_point(aes(x = lon, y = lat, colour = class), data = chkpts, alpha = .5)

theme_set(theme_bw())
ggmap(hdf, extent = "device") +
  geom_point(aes(x = lon, y = lat, colour = class), data = chkpts, alpha = .5)

ggmap(hdf, extent = "device", legend = "topleft") +
  geom_point(aes(x = lon, y = lat, colour = class), data = chkpts, alpha = .5)

# qmplot is great for this kind of thing...
qmplot(lon, lat, data = chkpts, color = class, darken = .6)
qmplot(lon, lat, data = chkpts, geom = "density2d", color = class, darken = .6)

## maprange
##################################################

hdf <- get_map()
mu <- c(-95.3632715, 29.7632836)
points <- data.frame(MASS::mvrnorm(1000, mu = mu, diag(c(.1, .1))))
names(points) <- c("lon", "lat")
points$class <- sample(c("a","b"), 1000, replace = TRUE)

ggmap(hdf) + geom_point(data = points) # maprange built into extent = panel, device
ggmap(hdf) + geom_point(aes(colour = class), data = points)

ggmap(hdf, extent = "normal") + geom_point(data = points)
# note that the following is not the same as extent = panel
ggmap(hdf, extent = "normal", maprange = TRUE) + geom_point(data = points)

# and if you need your data to run off on a extent = device (legend included)
ggmap(hdf, extent = "normal", maprange = TRUE) +
  geom_point(aes(colour = class), data = points) +
  theme_nothing(legend = TRUE) + theme(legend.position = "right")

# again, qmplot is probably more useful
qmplot(lon, lat, data = points, color = class, darken = .4, alpha = I(.6))
qmplot(lon, lat, data = points, color = class, maptype = "stamen_toner_lite")

## cool examples
##################################################

# contour overlay
ggmap(get_map(maptype = "satellite"), extent = "device") +
  stat_density2d(aes(x = lon, y = lat, colour = class), data = chkpts, bins = 5)


# adding additional content
library(grid)
baylor <- get_map("one bear place, waco, texas", zoom = 15, maptype = "satellite")
ggmap(baylor)

# use gglocator to find lon/lat"s of interest
(clicks <- gglocator(2) )
ggmap(baylor) +
  geom_point(aes(x = lon, y = lat), data = clicks, colour = "red", alpha = .5)
expand.grid(lon = clicks$lon, lat = clicks$lat)

ggmap(baylor) + theme_bw() +
  annotate("segment", x=-97.110, xend=-97.1188, y=31.5450, yend=31.5485,
    colour=I("red"), arrow = arrow(length=unit(0.3,"cm")), size = 1.5) +
  annotate("label", x=-97.113, y=31.5445, label = "Department of Statistical Science",
    colour = I("red"), size = 3.5) +
  labs(x = "Longitude", y = "Latitude") + ggtitle("Baylor University")


baylor <- get_map("marrs mclean science, waco, texas", zoom = 16, maptype = "satellite")

ggmap(baylor, extent = "panel") +
  annotate("segment", x=-97.1175, xend=-97.1188, y=31.5449, yend=31.5485,
    colour=I("red"), arrow = arrow(length=unit(0.4,"cm")), size = 1.5) +
  annotate("label", x=-97.1175, y=31.5447, label = "Department of Statistical Science",
    colour = I("red"), size = 4)



# a shapefile like layer
data(zips)
ggmap(get_map(maptype = "satellite", zoom = 8), extent = "device") +
  geom_polygon(aes(x = lon, y = lat, group = plotOrder),
    data = zips, colour = NA, fill = "red", alpha = .2) +
  geom_path(aes(x = lon, y = lat, group = plotOrder),
    data = zips, colour = "white", alpha = .4, size = .4)

library(plyr)
zipsLabels <- ddply(zips, .(zip), function(df){
  df[1,c("area", "perimeter", "zip", "lonCent", "latCent")]
})
ggmap(get_map(maptype = "satellite", zoom = 9),
    extent = "device", legend = "none", darken = .5) +
  geom_text(aes(x = lonCent, y = latCent, label = zip, size = area),
    data = zipsLabels, colour = I("red")) +
  scale_size(range = c(1.5,6))

qmplot(lonCent, latCent, data = zipsLabels, geom = "text",
  label = zip, size = area, maptype = "stamen_toner_lite", color = I("red")
)


## crime data example
##################################################

# only violent crimes
violent_crimes <- subset(crime,
  offense != "auto theft" &
  offense != "theft" &
  offense != "burglary"
)

# rank violent crimes
violent_crimes$offense <-
  factor(violent_crimes$offense,
    levels = c("robbery", "aggravated assault",
      "rape", "murder")
  )

# restrict to downtown
violent_crimes <- subset(violent_crimes,
  -95.39681 <= lon & lon <= -95.34188 &
   29.73631 <= lat & lat <=  29.78400
)


# get map and bounding box
theme_set(theme_bw(16))
HoustonMap <- qmap("houston", zoom = 14, color = "bw",
  extent = "device", legend = "topleft")
HoustonMap <- ggmap(
  get_map("houston", zoom = 14, color = "bw"),
  extent = "device", legend = "topleft"
)

# the bubble chart
HoustonMap +
   geom_point(aes(x = lon, y = lat, colour = offense, size = offense), data = violent_crimes) +
   scale_colour_discrete("Offense", labels = c("Robbery","Aggravated Assault","Rape","Murder")) +
   scale_size_discrete("Offense", labels = c("Robbery","Aggravated Assault","Rape","Murder"),
     range = c(1.75,6)) +
   guides(size = guide_legend(override.aes = list(size = 6))) +
   theme(
     legend.key.size = grid::unit(1.8,"lines"),
     legend.title = element_text(size = 16, face = "bold"),
     legend.text = element_text(size = 14)
   ) +
   labs(colour = "Offense", size = "Offense")


# doing it with qmplot is even easier
qmplot(lon, lat, data = violent_crimes, maptype = "stamen_toner_lite",
  color = offense, size = offense, legend = "topleft"
)

# or, with styling:
qmplot(lon, lat, data = violent_crimes, maptype = "stamen_toner_lite",
  color = offense, size = offense, legend = "topleft"
) +
  scale_colour_discrete("Offense", labels = c("Robbery","Aggravated Assault","Rape","Murder")) +
  scale_size_discrete("Offense", labels = c("Robbery","Aggravated Assault","Rape","Murder"),
    range = c(1.75,6)) +
  guides(size = guide_legend(override.aes = list(size = 6))) +
  theme(
    legend.key.size = grid::unit(1.8,"lines"),
    legend.title = element_text(size = 16, face = "bold"),
    legend.text = element_text(size = 14)
  ) +
  labs(colour = "Offense", size = "Offense")






# a contour plot
HoustonMap +
  stat_density2d(aes(x = lon, y = lat, colour = offense),
    size = 3, bins = 2, alpha = 3/4, data = violent_crimes) +
   scale_colour_discrete("Offense", labels = c("Robbery","Aggravated Assault","Rape","Murder")) +
   theme(
     legend.text = element_text(size = 15, vjust = .5),
     legend.title = element_text(size = 15,face="bold"),
     legend.key.size = grid::unit(1.8,"lines")
   )



# 2d histogram...
HoustonMap +
  stat_bin_2d(aes(x = lon, y = lat, colour = offense, fill = offense),
    size = .5, bins = 30, alpha = 2/4, data = violent_crimes) +
   scale_colour_discrete("Offense",
     labels = c("Robbery","Aggravated Assault","Rape","Murder"),
     guide = FALSE) +
   scale_fill_discrete("Offense", labels = c("Robbery","Aggravated Assault","Rape","Murder")) +
   theme(
     legend.text = element_text(size = 15, vjust = .5),
     legend.title = element_text(size = 15,face="bold"),
     legend.key.size = grid::unit(1.8,"lines")
   )





# changing gears (get a color map)
houston <- get_map("houston", zoom = 14)
HoustonMap <- ggmap(houston, extent = "device", legend = "topleft")

# a filled contour plot...
HoustonMap +
  stat_density2d(aes(x = lon, y = lat, fill = ..level.., alpha = ..level..),
    size = 2, bins = 4, data = violent_crimes, geom = "polygon") +
  scale_fill_gradient("Violent\nCrime\nDensity") +
  scale_alpha(range = c(.4, .75), guide = FALSE) +
  guides(fill = guide_colorbar(barwidth = 1.5, barheight = 10))

# ... with an insert

overlay <- stat_density2d(aes(x = lon, y = lat, fill = ..level.., alpha = ..level..),
    bins = 4, geom = "polygon", data = violent_crimes)

attr(houston,"bb") # to help finding (x/y)(min/max) vals below

HoustonMap +
  stat_density2d(aes(x = lon, y = lat, fill = ..level.., alpha = ..level..),
    bins = 4, geom = "polygon", data = violent_crimes) +
  scale_fill_gradient("Violent\nCrime\nDensity") +
  scale_alpha(range = c(.4, .75), guide = FALSE) +
  guides(fill = guide_colorbar(barwidth = 1.5, barheight = 10)) +
  inset(
    grob = ggplotGrob(ggplot() + overlay +
      scale_fill_gradient("Violent\nCrime\nDensity") +
      scale_alpha(range = c(.4, .75), guide = FALSE) +
      theme_inset()
    ),
    xmin = -95.35877, xmax = -95.34229,
    ymin = 29.73754, ymax = 29.75185
  )









## more examples
##################################################

# you can layer anything on top of the maps (even meaningless stuff)
df <- data.frame(
  lon = rep(seq(-95.39, -95.35, length.out = 8), each = 20),
  lat = sapply(
    rep(seq(29.74, 29.78, length.out = 8), each = 20),
    function(x) rnorm(1, x, .002)
  ),
  class = rep(letters[1:8], each = 20)
)

qplot(lon, lat, data = df, geom = "boxplot", fill = class)

HoustonMap +
  geom_boxplot(aes(x = lon, y = lat, fill = class), data = df)




## the base_layer argument - faceting
##################################################

df <- data.frame(
  x = rnorm(1000, -95.36258, .2),
  y = rnorm(1000,  29.76196, .2)
)

# no apparent change because ggmap sets maprange = TRUE with extent = "panel"
ggmap(get_map(), base_layer = ggplot(aes(x = x, y = y), data = df)) +
  geom_point(colour = "red")

# ... but there is a difference
ggmap(get_map(), base_layer = ggplot(aes(x = x, y = y), data = df), extent = "normal") +
  geom_point(colour = "red")

# maprange can fix it (so can extent = "panel")
ggmap(get_map(), maprange = TRUE, extent = "normal",
  base_layer = ggplot(aes(x = x, y = y), data = df)) +
  geom_point(colour = "red")

# base_layer makes faceting possible
df <- data.frame(
  x = rnorm(10*100, -95.36258, .075),
  y = rnorm(10*100,  29.76196, .075),
  year = rep(paste("year",format(1:10)), each = 100)
)
ggmap(get_map(), base_layer = ggplot(aes(x = x, y = y), data = df)) +
  geom_point() +  facet_wrap(~ year)

ggmap(get_map(), base_layer = ggplot(aes(x = x, y = y), data = df), extent = "device") +
  geom_point() +  facet_wrap(~ year)

qmplot(x, y, data = df)
qmplot(x, y, data = df, facets = ~ year)


## neat faceting examples
##################################################

# simulated example
df <- data.frame(
  x = rnorm(10*100, -95.36258, .05),
  y = rnorm(10*100,  29.76196, .05),
  year = rep(paste("year",format(1:10)), each = 100)
)
for(k in 0:9){
  df$x[1:100 + 100*k] <- df$x[1:100 + 100*k] + sqrt(.05)*cos(2*pi*k/10)
  df$y[1:100 + 100*k] <- df$y[1:100 + 100*k] + sqrt(.05)*sin(2*pi*k/10)
}

ggmap(get_map(),
  base_layer = ggplot(aes(x = x, y = y), data = df)) +
  stat_density2d(aes(fill = ..level.., alpha = ..level..),
    bins = 4, geom = "polygon") +
  scale_fill_gradient2(low = "white", mid = "orange", high = "red", midpoint = 10) +
  scale_alpha(range = c(.2, .75), guide = FALSE) +
  facet_wrap(~ year)



# crime example by month
levels(violent_crimes$month) <- paste(
  toupper(substr(levels(violent_crimes$month),1,1)),
  substr(levels(violent_crimes$month),2,20), sep = ""
)
houston <- get_map(location = "houston", zoom = 14, source = "osm", color = "bw")
HoustonMap <- ggmap(houston,
  base_layer = ggplot(aes(x = lon, y = lat), data = violent_crimes)
  )

HoustonMap +
  stat_density2d(aes(x = lon, y = lat, fill = ..level.., alpha = ..level..),
    bins = I(5), geom = "polygon", data = violent_crimes) +
  scale_fill_gradient2("Violent\nCrime\nDensity",
    low = "white", mid = "orange", high = "red", midpoint = 500) +
  labs(x = "Longitude", y = "Latitude") + facet_wrap(~ month) +
  scale_alpha(range = c(.2, .55), guide = FALSE) +
  ggtitle("Violent Crime Contour Map of Downtown Houston by Month") +
  guides(fill = guide_colorbar(barwidth = 1.5, barheight = 10))








## darken argument
##################################################
ggmap(get_map())
ggmap(get_map(), darken = .5)
ggmap(get_map(), darken = c(.5,"white"))
ggmap(get_map(), darken = c(.5,"red")) # silly, but possible



## End(Not run)

ggmap documentation built on Nov. 19, 2023, 9:06 a.m.