qmplot(x, y, ..., data, zoom, source = "stamen",
  maptype = "toner-lite", extent = "device", legend = "right",
  padding = 0.02, force = FALSE, darken = c(0, "black"),
  mapcolor = "color", facets = NULL, margins = FALSE,
  geom = "auto", stat = list(NULL), position = list(NULL),
  xlim = c(NA, NA), ylim = c(NA, NA), main = NULL, f = 0.05,
  xlab = "Longitude", ylab = "Latitude")

## Not run:  # these are skipped to conserve R check time

qmplot(lon, lat, data = crime)


# 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
)

theme_set(theme_bw())

qmplot(lon, lat, data = violent_crimes, colour = offense,
  size = I(3.5), alpha = I(.6), legend = "topleft")

qmplot(lon, lat, data = violent_crimes, geom = c("point","density2d"))
qmplot(lon, lat, data = violent_crimes) + facet_wrap(~ offense)
qmplot(lon, lat, data = violent_crimes, extent = "panel") + facet_wrap(~ offense)
qmplot(lon, lat, data = violent_crimes, extent = "panel", colour = offense, darken = .4) +
  facet_wrap(~ month)




qmplot(long, lat, xend = long + delta_long,
  color = I("red"), yend = lat + delta_lat, data = seals,
  geom = "segment", zoom = 5)

qmplot(long, lat, xend = long + delta_long, maptype = "watercolor",
  yend = lat + delta_lat, data = seals,
  geom = "segment", zoom = 6)

qmplot(long, lat, xend = long + delta_long, maptype = "terrain",
  yend = lat + delta_lat, data = seals,
  geom = "segment", zoom = 6)


qmplot(lon, lat, data = wind, size = I(.5), alpha = I(.5)) +
  ggtitle("NOAA Wind Report Sites")

# thin down data set...
s <- seq(1, 227, 8)
thinwind <- subset(wind,
  lon %in% unique(wind$lon)[s] &
  lat %in% unique(wind$lat)[s]
)

# for some reason adding arrows to the following plot bugs
theme_set(theme_bw(18))

qmplot(lon, lat, data = thinwind, geom = "tile", fill = spd, alpha = spd,
    legend = "bottomleft") +
  geom_leg(aes(xend = lon + delta_lon, yend = lat + delta_lat)) +
  scale_fill_gradient2("Wind Speed\nand\nDirection",
    low = "green", mid = scales::muted("green"), high = "red") +
  scale_alpha("Wind Speed\nand\nDirection", range = c(.1, .75)) +
  guides(fill = guide_legend(), alpha = guide_legend())




## kriging
############################################################
# the below examples show kriging based on undeclared packages
# to better comply with CRAN's standards, we remove it from
# executing, but leave the code as a kind of case-study
# they also require the rgdal library


library(lattice)
library(sp)
library(rgdal)

# load in and format the meuse dataset (see bivand, pebesma, and gomez-rubio)
data(meuse)
coordinates(meuse) <- c("x", "y")
proj4string(meuse) <- CRS("+init=epsg:28992")
meuse <- spTransform(meuse, CRS("+proj=longlat +datum=WGS84"))

# plot
plot(meuse)

m <- data.frame(slot(meuse, "coords"), slot(meuse, "data"))
names(m)[1:2] <- c("lon", "lat")

qmplot(lon, lat, data = m)
qmplot(lon, lat, data = m, zoom = 14)


qmplot(lon, lat, data = m, size = zinc,
  zoom = 14, source = "google", maptype = "satellite",
  alpha = I(.75), color = I("green"),
  legend = "topleft", darken = .2
) + scale_size("Zinc (ppm)")








# load in the meuse.grid dataset (looking toward kriging)
library(gstat)
data(meuse.grid)
coordinates(meuse.grid) <- c("x", "y")
proj4string(meuse.grid) <- CRS("+init=epsg:28992")
meuse.grid <- spTransform(meuse.grid, CRS("+proj=longlat +datum=WGS84"))

# plot it
plot(meuse.grid)

mg <- data.frame(slot(meuse.grid, "coords"), slot(meuse.grid, "data"))
names(mg)[1:2] <- c("lon", "lat")

qmplot(lon, lat, data = mg, shape = I(15), zoom = 14, legend = "topleft") +
  geom_point(aes(size = zinc), data = m, color = "green") +
  scale_size("Zinc (ppm)")



# interpolate at unobserved locations (i.e. at meuse.grid points)
# pre-define scale for consistency
scale <- scale_color_gradient("Predicted\nZinc (ppm)",
  low = "green", high = "red", lim = c(100, 1850)
)



# inverse distance weighting
idw <- idw(log(zinc) ~ 1, meuse, meuse.grid, idp = 2.5)
mg$idw <- exp(slot(idw, "data")$var1.pred)

qmplot(lon, lat, data = mg, shape = I(15), color = idw,
  zoom = 14, legend = "topleft", alpha = I(.75), darken = .4
) + scale



# linear regression
lin <- krige(log(zinc) ~ 1, meuse, meuse.grid, degree = 1)
mg$lin <- exp(slot(lin, "data")$var1.pred)

qmplot(lon, lat, data = mg, shape = I(15), color = lin,
  zoom = 14, legend = "topleft", alpha = I(.75), darken = .4
) + scale



# trend surface analysis
tsa <- krige(log(zinc) ~ 1, meuse, meuse.grid, degree = 2)
mg$tsa <- exp(slot(tsa, "data")$var1.pred)

qmplot(lon, lat, data = mg, shape = I(15), color = tsa,
  zoom = 14, legend = "topleft", alpha = I(.75), darken = .4
) + scale



# ordinary kriging
vgram <- variogram(log(zinc) ~ 1, meuse)   # plot(vgram)
vgramFit <- fit.variogram(vgram, vgm(1, "Exp", .2, .1))
ordKrige <- krige(log(zinc) ~ 1, meuse, meuse.grid, vgramFit)
mg$ordKrige <- exp(slot(ordKrige, "data")$var1.pred)

qmplot(lon, lat, data = mg, shape = I(15), color = ordKrige,
  zoom = 14, legend = "topleft", alpha = I(.75), darken = .4
) + scale



# universal kriging
vgram <- variogram(log(zinc) ~ 1, meuse) # plot(vgram)
vgramFit <- fit.variogram(vgram, vgm(1, "Exp", .2, .1))
univKrige <- krige(log(zinc) ~ sqrt(dist), meuse, meuse.grid, vgramFit)
mg$univKrige <- exp(slot(univKrige, "data")$var1.pred)

qmplot(lon, lat, data = mg, shape = I(15), color = univKrige,
  zoom = 14, legend = "topleft", alpha = I(.75), darken = .4
) + scale



# adding observed data layer
qmplot(lon, lat, data = mg, shape = I(15), color = univKrige,
  zoom = 14, legend = "topleft", alpha = I(.75), darken = .4
) +
  geom_point(
    aes(x = lon, y = lat, size = zinc),
    data = m, shape = 1, color = "black"
  ) +
  scale +
  scale_size("Observed\nLog Zinc")







## End(Not run) # end dontrun