from R-help digest, Vol 51, Issue 5 (5/5/2007)


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Message: 74
Date: Sat, 5 May 2007 01:00:16 +0000
From: Alberto Vieira Ferreira Monteiro <albmont@centroin.com.br>
Subject: Re: [R] Help with map
To: Roger.Bivand@nhh.no
Cc: R-help@stat.math.ethz.ch
Message-ID: <200705050100.18512.albmont@centroin.com.br>
Content-Type: text/plain;  charset="iso-8859-1"

[for those that worry about these things, this _is_ a homework
assignment. However, it's not an R homework, it's a Geography
and History homework... and I want to use R to create a pretty
map]

Roger Bivand wrote:

Is there any way to associate one color to each country?

Try:

map_poly_obj <- map("worldHires", c("Argentina", "Brazil"), plot=FALSE,
  fill=TRUE)
str(map_poly_obj)

and you'll see that the component of interest is the named polygons, of
which there are 28, namely

Ok, I guess I can see what you mean. It worked, but I don't think
this is a practical way to draw things.

For example, suppose [this would help homework mentioned above] I
want to draw a series of maps showing the evolution of Communism
in the XX century. I would like to start with a 1917 map, showing most
countries as in...

map("worldHires")

.... but with the Soviet Union in red. I don't see how I could mix the two 
maps (BTW, there's no Russia in worldHires, but there is a USSR...)

map("worldHires"); map("worldHires", "USSR", col="red", fill=T)


map_poly_obj$names

So you can build a matching colours vector, or:

library(sp)
library(maptools)
IDs <- sapply(strsplit(map_poly_obj$names, ":"), function(x) x[1])
SP_AB <- map2SpatialPolygons(map_poly_obj, IDs=IDs,
  proj4string=CRS("+proj=longlat +datum=wgs84"))

but

plot(SP_AB, col=c("cyan", "green"))

still misses, because some polygons have their rings of coordinates in
counter-clockwise order, so:

pl_new <- lapply(slot(SP_AB, "polygons"), checkPolygonsHoles)
slot(SP_AB, "polygons") <- pl_new
# please forget the assignment to the slot and do not do it unless you can
# replace what was there before

plot(SP_AB, col=c("cyan", "green"), axes=TRUE)

now works. Moreover, SP_AB is a SpatialPolygons object, which can be
promoted to a SpatialPolygonsDataFrame object, for a data slot holding a
data.frame with row names matching the Polygons ID values:

sapply(slot(SP_AB, "polygons"), function(x) slot(x, "ID"))

So adding a suitable data frame gets you to the lattice graphics method

spplot(SP_AB, "my_var")

Hope this helps,

So, in the above mentioned case, I could do something like:

library(mapdata)
commies <- c("USSR", "Mongolia") 
# Mongolia was the 2nd communist country, in 1925
map_poly_obj <- map("worldHires", plot=FALSE)
map_poly_commies <- map("worldHires", commies,
  plot=FALSE, fill=TRUE)
plot(map_poly_obj, type="l")
polygon(map_poly_commies, col="red", border="black")

I guess I can keep going, unless there is a simpler solution.

Alberto Monteiro

------------------------------------------------------------------------------

Here's the sloppy code I used to put together the map in the persp3d 
example in rgl 0.71.  I've just made a few edits; I hope it still runs.
The idea is to do a lot of calculations on a vector of colours, then
plot them all at once.

library(mapdata)

names <- map("worldHires", plot=FALSE, namesonly=TRUE)

countries <- gsub(":.*", "", names)  # the first part of the name
lakes <- grep("Lake", names)
lakes <- lakes[-15]
lakes <- unique(c(lakes, grep("Ozero", names), 
                         grep("Vodokh", names),
			   grep("Nuur", names),
			   grep("Ostrov Ol'khon", names),
			   grep("Hayk", names),
			   grep("Lago", names)))  # The map doesn't distinguish these

seas <- grep("Sea", names) 
nfld <- grep("^Newfoundland$", names) # nfld should be coloured as Canada
canada <- grep("^Canada$", names)

svalbard <- grep("Svalbard", names)   # Svalbard coloured as Norway
norway <- grep("^Norway$", names)

nums <- as.numeric(factor(countries))
N <- max(nums)
set.seed(3)
col <-  hcl(h=sample((1:N)*360/N),
            c=sample(seq(35,45,len=N)),
            l=sample(seq(75,85,len=N)))[nums]  # random colours by country
col[c(lakes,seas)] <- "white" # with lakes and seas white
col[nfld] <- col[canada]
col[svalbard] <- col[norway]

 png(width=2200, height=2200, file='world.png')
 map('worldHires', fill = TRUE, col = col, ylim=c(-90,90))
 abline(h=c(-90, 90))
 abline(v=c(-180.05, 180.05))
 dev.off()

I hope this gives you some ideas.

Duncan Murdoch

------------------------------------------------------------------------------

[Please note that the worldHires database refers to a particular time
cross section, probably late 1980's. The territorial extents of the former
USSR in 1919, 1920, 1939, 1940, 1941, 1944, 1945, etc., etc. are not the
same;  the same consideration would apply to PRC's actual control over
Tibet. 

So to do this, you need a sequence of maps showing the marginal
increments, with 1917 actually only colouring Petrograd/St Petersburg and
perhaps some other cities. I'm not aware of any publically available
sequence of boundary files adequately representing the situation of say
the Baltic states or Finland for the 1917-2007 period, if anyone has a
suitable link, please say so.

Geographical data are vintaged, not just "where", but "where when". Was
for example Estonia occupied by the USSR 1940-1941, 1944-1991, or was it
part of the USSR for the purposes of this exercise? Using disputed 
boundaries implies a choice of point of view, one that may not be 
intended.]

Roger