```
makeGrid =
#
# This takes a collection of population densities
# and a box/rectangle (or a grid of x's and y's)
# and creates a matrix with the population densities
# spread appropriately through the box.
# It uses the maps package to do this to determine the
# polygons/boundaries for each
#
# For each (x, y) point on the "grid", it figures out
# which polygon the point is in and then determine the
# population density.
#
function(x, y, densities, dim = c(100, 100), land.mean = mean(densities))
{
library(maps)
# if x has 4 elements and we have no y,
# break x up into x and y
if(missing(y) && length(x) == 4) {
y = x[3:4]
x = x[1:2]
}
# If x has two elements, then compute the
# grid of x's, equally spaced
# So we end up with a vector of x values and a vector
# of y's and this gives us the (x, y) points for our grid.
if(length(x) == 2)
x = seq(min(x), max(x), length = dim[1])
if(length(y) == 2)
y = seq(min(y), max(y), length = dim[2])
# Compute all the (x,y) points in our matrix.
grid = expand.grid(x, y)
# now figure out the name of the state in which
# each of the x,y values is located.
ids = map.where('state', grid)
ids = gsub(":.*", "", ids)
names(densities) = tolower(names(densities))
# Construct our matrix by getting the population densities
# at each (x,y) pair based on what state it was in and looking
# this up in
values = matrix(densities[ids], length(x), length(y), byrow = TRUE)
# Where there are missing values, use the mean value.
values[is.na(values)] = land.mean
structure(list(grid = t(values), x = range(x), y = range(y),
mean = mean(densities)),
class = "CartogramGrid")
# t(values[nrow(values):1, ])
}
plot.CartogramGrid =
#
# Should we transpose the grid or not.
#
function(x, ...)
image(seq(x$x[1], x$x[2], length = ncol(x$grid)),
seq(x$y[1], x$y[2], length = nrow(x$grid)),
t(x$grid), xlab = "latitude", ylab = "longitude")
statePopulations =
#
# Get the US state populations by reading them from Wikipedia.
#
#
function()
{
pop = htmlParse("http://en.wikipedia.org/wiki/List_of_U.S._states_by_population", error = function(...){})
els = pop["//table//tr/child::*[3] | //table//tr/child::*[4]" ]
vals = sapply(els[ - (1:2) ], xmlValue)
i = seq(1, by = 2, length = length(vals)/2)
structure(as.numeric(gsub("^&0*([0-9]+)\\..*", "\\1", vals[i+1])), names = vals[i])
}
if(exists("run") && run) {
# Read the state populations.
gridSize = 400
pop = statePopulations()
library(maps)
mm = map('usa', plot = FALSE)
g = makeGrid(mm$range, , pop, c(gridSize, gridSize))
library(Rcartogram)
big = addBoundary(g$grid, 2, g$mean)
image(1:nrow(big), 1:ncol(big), big)
cart = cartogram(t(big))
load("states.rda")
new.polys = lapply(countyBoundaries$us,
function(pol) {
z = Rcartogram:::mapToGrid(pol[,1], pol[,2], g, c(800, 800))
z.new = predict(cart, z)
})
tmp = do.call("rbind", lapply(new.polys, function(x) cbind(x$x, x$y)))
# quartz()
plot(0, type = "n", xlim = range(tmp[,1], na.rm = TRUE), ylim = range(tmp[,2], na.rm = TRUE))
invisible(lapply(new.polys, polygon, border = "blue"))
plot(0, type = "n", xlim = range(tmp[,1], na.rm = TRUE), ylim = range(tmp[,2], na.rm = TRUE))
state.win = sapply(states, function(x) diff(colSums(x[,1:2])) < 0)
names(state.win) = gsub("\\..*$", "", names(state.win))
names(state.win) = gsub("-", " ", names(state.win))
i = match(tolower(names(new.polys)), names(state.win), 0)
invisible(mapply(polygon, new.polys, ifelse(state.win[tolower(names(new.polys[i]))], "blue", "red")))
invisible(polygon(do.call("rbind", lapply(new.polys[i], function(x) cbind(x$x, x$y))),
border = "blue",
))
}
```

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