hdf <- ggmap()
(HoustonMap <- ggmapplot(hdf)
require(MASS)
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(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)
HoustonMap +
geom_point(aes(x = lon, y = lat, colour = class), data = chkpts, alpha = .5)
HoustonMap +
stat_density2d(aes(x = lon, y = lat, size = ..density.., colour = class),
geom = 'point', alpha = I(1/2), data = chkpts, contour = FALSE) +
scale_size(range = c(.1, .75), guide = 'none')
HoustonMap <- ggmapplot(ggmap(maptype = 'satellite'), fullpage = TRUE)
HoustonMap +
stat_density2d(aes(x = lon, y = lat, colour = class), data = chkpts, bins = 5)
ggmapplot(ggmap('Paris', verbose = TRUE), fullpage = TRUE)
baylor <- ggmap('baylor university', zoom = 15,
maptype = 'satellite', regularize = FALSE, verbose = TRUE)
ggmapplot(baylor) + theme_bw() +
annotate('rect', xmin=-97.11920, ymin=31.5439, xmax=-97.101, ymax=31.5452,
fill = I('black'), alpha = I(3/4)) +
annotate('segment', x=-97.110, xend=-97.11920, y=31.5450, yend=31.5482,
colour=I('red'), arrow = arrow(length=unit(0.3,"cm")), size = 1.5) +
annotate('text', x=-97.110, y=31.5445, label = 'Department of Statistical Science',
colour = I('red'), size = 6) +
labs(x = 'Longitude', y = 'Latitude') + opts(title = 'Baylor University')
# the following is helpful to place the annotation boxes
clicks <- gglocator(2)
expand.grid(lon = clicks$lon, lat = clicks$lat)
baylor <- ggmap('baylor university', zoom = 16,
maptype = 'satellite', regularize = FALSE, verbose = TRUE)
ggmapplot(baylor, fullpage = TRUE) +
annotate('rect', xmin=-97.1164, ymin=31.5441, xmax=-97.1087, ymax=31.5449,
fill = I('black'), alpha = I(3/4)) +
annotate('segment', x=-97.1125, xend=-97.11920, y=31.5449, yend=31.5482,
colour=I('red'), arrow = arrow(length=unit(0.4,"cm")), size = 1.5) +
annotate('text', x=-97.1125, y=31.5445, label = 'Department of Statistical Science',
colour = I('red'), size = 6)
baylor <- ggmap(center = c(lat = 31.54838, lon = -97.11922), zoom = 19,
maptype = 'satellite', regularize = FALSE, verbose = TRUE)
ggmapplot(baylor, fullpage = TRUE)
# some playing around with the scale parameter is typically needed here. see ?GetMap.OSM
baylorosm <- ggmap(center = c(lat = 31.54838, lon = -97.11922), source = 'osm',
verbose = TRUE, zoom = 16, scale = 10000)
ggmapplot(baylorosm, fullpage = TRUE)
data(zips)
ggmapplot(ggmap(maptype = 'satellite', zoom = 9), fullpage = TRUE) +
geom_path(aes(x = lon, y = lat, group = plotOrder), data = zips, colour = I('red'), size = I(.4))
# adjust device size to get rid of horizontal lines
library(plyr)
zipsLabels <- ddply(zips, .(zip), function(df){
df[1,c("area", "perimeter", "zip", "lonCent", "latCent")]
})
options('device')$device(width = 7.7, height = 6.7)
ggmapplot(ggmap(maptype = 'satellite', zoom = 9), fullpage = TRUE) +
geom_text(aes(x = lonCent, y = latCent, label = zip, size = area),
data = zipsLabels, colour = I('red')) +
scale_size(range = c(1.5,6))
# Crime data example
# format data
violent_crimes <- subset(crime,
offense != 'auto theft' & offense != 'theft' & offense != 'burglary'
)
violent_crimes$offense <- factor(violent_crimes$offense,
levels = c('robbery', 'aggravated assault', 'rape', 'murder')
)
levels(violent_crimes$offense) <- c('robbery', 'aggravated assault', 'rape', 'murder')
# get map and bounding box
houston <- ggmap(location = 'houston', zoom = 14)
lat_range <- as.numeric(attr(houston, 'bb')[c('ll.lat','ur.lat')])
lon_range <- as.numeric(attr(houston, 'bb')[c('ll.lon','ur.lon')])
HoustonMap <- ggmapplot(houston)
theme_set(theme_bw())
# make bubble chart
options('device')$device(width = 9.25, height = 7.25)
HoustonMap +
geom_point(aes(x = lon, y = lat, colour = offense, size = offense), data = violent_crimes) +
scale_x_continuous('Longitude', limits = lon_range) +
scale_y_continuous('Latitude', limits = lat_range) +
opts(title = 'Violent Crime Bubble Map of Downtown Houston') +
scale_colour_discrete('Offense', labels = c('Robery','Aggravated\nAssault','Rape','Murder')) +
scale_size_discrete('Offense', labels = c('Robery','Aggravated\nAssault','Rape','Murder'))
# make contour plot
violent_crimes <- subset(violent_crimes,
lat_range[1] <= lat & lat <= lat_range[2] &
lon_range[1] <= lon & lon <= lon_range[2]
)
HoustonMap +
stat_density2d(aes(x = lon, y = lat, colour = ..level..),
bins = I(20), fill = NA, alpha = I(1/2), size = I(.75), data = violent_crimes) +
scale_colour_gradient2('Violent\nCrime\nDensity',
low = 'darkblue', mid = 'orange', high = 'red', midpoint = 35) +
scale_x_continuous('Longitude', limits = lon_range) +
scale_y_continuous('Latitude', limits = lat_range) +
opts(title = 'Violent Crime Contour Map of Downtown Houston')
# the fill aesthetic is now available -
HoustonMap +
stat_density2d(aes(x = lon, y = lat, fill = ..level..),
bins = I(6), alpha = I(1/5), geom = 'polygon', data = violent_crimes, ) +
scale_fill_gradient('Violent\nCrime\nDensity') +
scale_x_continuous('Longitude', limits = lon_range) +
scale_y_continuous('Latitude', limits = lat_range) +
opts(title = 'Violent Crime Contour Map of Downtown Houston') +
guides(fill = guide_colorbar(override.aes = list(alpha = 1)))
options('device')$device(width = 9.25, height = 7.25)
HoustonMap +
stat_density2d(aes(x = lon, y = lat, fill = ..level.., alpha = ..level..),
bins = I(6), geom = 'polygon', data = violent_crimes) +
scale_fill_gradient2('Violent\nCrime\nDensity',
low = 'white', mid = 'orange', high = 'red', midpoint = 500) +
scale_x_continuous('Longitude', limits = lon_range) +
scale_y_continuous('Latitude', limits = lat_range) +
scale_alpha(range = c(.1, .45), guide = FALSE) +
opts(title = 'Violent Crime Contour Map of Downtown Houston') +
guides(fill = guide_colorbar(barwidth = 1.5, barheight = 10))
# we can also add insets
HoustonMap +
stat_density2d(aes(x = lon, y = lat, fill = ..level.., alpha = ..level..),
bins = I(6), geom = 'polygon', data = violent_crimes) +
scale_fill_gradient2('Violent\nCrime\nDensity',
low = 'white', mid = 'orange', high = 'red', midpoint = 500) +
scale_x_continuous('Longitude', limits = lon_range) +
scale_y_continuous('Latitude', limits = lat_range) +
scale_alpha(range = c(.1, .45), guide = FALSE) +
opts(title = 'Violent Crime Contour Map of Downtown Houston') +
guides(fill = guide_colorbar(barwidth = 1.5, barheight = 10)) +
annotation_custom(
grob = ggplotGrob(ggplot() +
stat_density2d(aes(x = lon, y = lat, fill = ..level.., alpha = ..level..),
bins = I(6), geom = 'polygon', data = violent_crimes) +
scale_fill_gradient2('Violent\nCrime\nDensity',
low = 'white', mid = 'orange', high = 'red', midpoint = 500, guide = FALSE) +
scale_x_continuous('Longitude', limits = lon_range) +
scale_y_continuous('Latitude', limits = lat_range) +
scale_alpha(range = c(.1, .45), guide = FALSE) +
theme_inset()
),
xmin = attr(houston,'bb')$ll.lon +
(7/10) * (attr(houston,'bb')$ur.lon - attr(houston,'bb')$ll.lon),
xmax = Inf,
ymin = -Inf,
ymax = attr(houston,'bb')$ll.lat +
(3/10) * (attr(houston,'bb')$ur.lat - attr(houston,'bb')$ll.lat)
)
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)
# using the base_layer argument
df <- data.frame(
x = rnorm(100, -95.36258, .05),
y = rnorm(100, 29.76196, .05)
)
ggmapplot(ggmap(), base_layer = ggplot(aes(x = x, y = y), data = df)) +
geom_point(colour = 'red')
# using the maprange argument
ggmapplot(ggmap(), maprange = TRUE,
base_layer = ggplot(aes(x = x, y = y), data = df)) +
geom_point(colour = 'red')
# faceting is available via the base_layer argument
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)
)
ggmapplot(ggmap(), base_layer = ggplot(aes(x = x, y = y), data = df)) +
geom_point() + facet_wrap(~ year)
# a neat 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)
}
options('device')$device(width = 10.93, height = 7.47)
ggmapplot(ggmap(), maprange = TRUE, expand = TRUE,
base_layer = ggplot(aes(x = x, y = y), data = df)) +
stat_density2d(aes(fill = ..level.., alpha = ..level..),
bins = 4, geom = 'polygon') +
scale_fill_gradient2(low = 'white', mid = 'orange', high = 'red', midpoint = 10) +
scale_alpha(range = c(.2, .75), guide = FALSE) +
facet_wrap(~ year)
ggmapplot(ggmap(), maprange = TRUE, expand = TRUE,
base_layer = ggplot(aes(x = x, y = y), data = df)) +
stat_density2d(aes(fill = ..level.., alpha = ..level..),
bins = 20, geom = 'polygon') +
scale_fill_gradient2(low = 'white', mid = 'orange', high = 'red', midpoint = 10) +
scale_alpha(range = c(.2, .75), guide = FALSE) +
facet_wrap(~ year)
# crime by month
levels(violent_crimes$month) <- paste(
toupper(substr(levels(violent_crimes$month),1,1)),
substr(levels(violent_crimes$month),2,20), sep = ''
)
houston <- ggmap(location = 'houston', zoom = 14, source = 'osm', type = 'bw')
HoustonMap <- ggmapplot(houston,
base_layer = ggplot(aes(x = lon, y = lat), data = violent_crimes)
)
options('device')$device(width = 8.62, height = 7.48)
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) +
scale_x_continuous('Longitude', limits = lon_range) +
scale_y_continuous('Latitude', limits = lat_range) +
facet_wrap(~ month) +
scale_alpha(range = c(.2, .55), guide = FALSE) +
opts(title = 'Violent Crime Contour Map of Downtown Houston by Month') +
guides(fill = guide_colorbar(barwidth = 1.5, barheight = 10))Run the code above in your browser using DataLab