ggmap (version 2.5.2)

qmplot: Quick map plot

Description

qmplot is the ggmap equivalent to the ggplot2 function qplot and allows for the quick plotting of maps with data/models/etc.

Usage

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 = deparse(substitute(x)),
  ylab = deparse(substitute(y)))

Arguments

x

longitude values

y

latitude values

...

other aesthetics passed for each layer

data

data frame to use (optional). If not specified, will create one, extracting vectors from the current environment.

zoom

map zoom, see get_map

source

map source, see get_map

maptype

map type, see get_map

extent

how much of the plot should the map take up? "normal", "panel", or "device" (default)

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 extent = "device")

force

force new map (don't use archived version)

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.

mapcolor

color ("color") or black-and-white ("bw")

facets

faceting formula to use. Picks facet_wrap or facet_grid depending on whether the formula is one sided or two-sided

margins

whether or not margins will be displayed

geom

character vector specifying geom to use. defaults to "point"

stat

character vector specifying statistics to use

position

character vector giving position adjustment to use

xlim

limits for x axis

ylim

limits for y axis

main

character vector or expression for plot title

f

number specifying the fraction by which the range should be extended

xlab

character vector or expression for x axis label

ylab

character vector or expression for y axis label

Examples

# 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)


library(scales)
library(grid)
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 = 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$lin <- exp(slot(idw, "lin")$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 dontrun