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
## 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(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$lin <- exp(slot(idw, "lin")$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(Not run) # end dontrun