Answer to http://stackoverflow.com/questions/22855197/how-do-you-get-geom-map-to-show-all-parts-of-a-map/22875922 since it's long-ish and easier to cut/paste from here than on SO

22855197.R

library(ggplot2)
library(maps)
library(plyr)
library(gridExtra)

ARCTP53_SOExample <- read.csv("dat.csv")

# reduce all the distinct exon/introns to just exon or intron
ARCTP53_SOExample$EorI <- factor(ifelse(grepl("exon", 
                                              ARCTP53_SOExample$ExonIntron, 
                                              ignore.case = TRUE), 
                                        "exon", "intron"))

# extract summary data for the two variables we care about for the map
arc.combined <- count(ARCTP53_SOExample, .(Country, EorI))
colnames(arc.combined) <- c("region", "EorI", "ei.ct")

# get total for country (region) and add to the summary info
arc.combined <- merge(arc.combined, count(arc.combined, .(region), wt_var=.(ei.ct)))
colnames(arc.combined) <- c("region", "EorI", "ei.ct", "region.total")

# it wasn't specified if the "EorI" is going to be used on the map so 
# we won't use it below (but we could, now)

# get map and intercourse Antarctica

world_map <- map_data("world")
world_map <- subset(world_map, region!="Antarctica")

# DEAL WITH MISSING REGIONS

# get our regions and the regions named in the "world"
wm.reg <- unique(as.character(world_map$region))
arc.reg <- unique(as.character(ARCTP53_SOExample$Country))

# this shows the missing ones
sort(arc.reg[!arc.reg %in% wm.reg])

# this shows all valid ones
sort(wm.reg)

# INSERT YOUR #SPIFFY CODE TO CLEANUP THE REGIONS HERE :-)

# this will show the counts by country with all of the "chart junk" removed
# and the "counts" scaled as a gradient, and with the legend at the top

gg <- ggplot(arc.combined)
gg <- gg + geom_map(dat=world_map, map = world_map, aes(map_id=region), 
                    fill="white", color="#7f7f7f", size=0.25)
gg <- gg + geom_map(map = world_map, aes(map_id = region, fill = region.total), size=0.25)
gg <- gg + scale_fill_gradient(low="#fff7bc", high="#cc4c02", name="Tumor counts")
gg <- gg + expand_limits(x = world_map$long, y = world_map$lat)
gg <- gg + labs(x="", y="", title="Tumor contribution by country")
gg <- gg + theme(panel.grid=element_blank(), panel.border=element_blank())
gg <- gg + theme(axis.ticks=element_blank(), axis.text=element_blank())
gg <- gg + theme(legend.position="top")
gg

# BUT you might want to show the counts by intron/exon by country

gg <- ggplot(arc.combined[arc.combined$EorI=="exon",])
gg <- gg + geom_map(dat=world_map, map = world_map, aes(map_id=region), 
                    fill="white", color="#7f7f7f", size=0.25)
gg <- gg + geom_map(map = world_map, aes(map_id = region, fill = ei.ct), size=0.25)
gg <- gg + scale_fill_gradient(low="#f7fcb9", high="#238443", name="Tumor counts")
gg <- gg + expand_limits(x = world_map$long, y = world_map$lat)
gg <- gg + labs(x="", y="", title="Tumor contribution by 'exon' & country")
gg <- gg + theme(panel.grid=element_blank(), panel.border=element_blank())
gg <- gg + theme(axis.ticks=element_blank(), axis.text=element_blank())
gg <- gg + theme(legend.position="top")
gg.exon <- gg

gg <- ggplot(arc.combined[arc.combined$EorI=="intron",])
gg <- gg + geom_map(dat=world_map, map = world_map, aes(map_id=region), 
                    fill="white", color="#7f7f7f", size=0.25)
gg <- gg + geom_map(map = world_map, aes(map_id = region, fill = ei.ct), 
                    colour = "#7f7f7f", size=0.25)
gg <- gg + scale_fill_gradient(low="#ece7f2", high="#0570b0", name="Tumor counts")
gg <- gg + expand_limits(x = world_map$long, y = world_map$lat)
gg <- gg + labs(x="", y="", title="Tumor contribution by 'intron' & country")
gg <- gg + theme(panel.grid=element_blank(), panel.border=element_blank())
gg <- gg + theme(axis.ticks=element_blank(), axis.text=element_blank())
gg <- gg + theme(legend.position="top")
gg.intron <- gg

grid.arrange(gg.exon, gg.intron, ncol=1)

better-maps.R

library(ggplot2)
library(maps)
library(plyr)
library(gridExtra)
library(maptools)

ARCTP53_SOExample <- read.csv("dat.csv")

# reduce all the distinct exon/introns to just exon or intron
ARCTP53_SOExample$EorI <- factor(ifelse(grepl("exon", 
                                              ARCTP53_SOExample$ExonIntron, 
                                              ignore.case = TRUE), 
                                        "exon", "intron"))

# extract summary data for the two variables we care about for the map
arc.combined <- count(ARCTP53_SOExample, .(Country, EorI))
colnames(arc.combined) <- c("id", "EorI", "ei.ct")

# get total for country (region) and add to the summary info
arc.combined <- merge(arc.combined, count(arc.combined, .(id), wt_var=.(ei.ct)))
colnames(arc.combined) <- c("id", "EorI", "ei.ct", "region.total")

# it wasn't specified if the "EorI" is going to be used on the map so 
# we won't use it below (but we could, now)

# get map and intercourse Antarctica

data(wrld_simpl)

world_map <- fortify(wrld_simpl, region="NAME")
world_map <- subset(world_map, id!="Antarctica")
world_map <- world[order(world_map$order), ] 

# DEAL WITH MISSING REGIONS

# get our regions and the regions named in the "world"

# clean up our data first
arc.combined$id <- gsub("USA", "United States", arc.combined$id)
arc.combined$id <- gsub("UK", "United Kingdom", arc.combined$id)
arc.combined$id <- gsub("The Netherlands", "Netherlands", arc.combined$id)
arc.combined$id <- gsub("Iran", "Iran (Islamic Republic of)", arc.combined$id)
arc.combined$id <- gsub("China.*", "China", arc.combined$id)
arc.combined$id <- gsub("Chinese.*", "Taiwan", arc.combined$id)

wm.reg <- unique(as.character(world_map$id))
arc.reg <- unique(as.character(arc.combined$id))

# this shows the missing ones
sort(arc.reg[!arc.reg %in% wm.reg])

# this shows all valid ones

# INSERT YOUR #SPIFFY CODE TO CLEANUP THE REGIONS HERE :-)

# this will show the counts by country with all of the "chart junk" removed
# and the "counts" scaled as a gradient, and with the legend at the top

gg <- ggplot(arc.combined)
gg <- gg + geom_map(dat=world_map, map = world_map, aes(map_id=id), 
                    fill="white", color="#7f7f7f", size=0.25)
gg <- gg + geom_map(map = world_map, aes(map_id = id, fill = region.total), size=0.25)
gg <- gg + scale_fill_gradient(low="#fff7bc", high="#cc4c02", name="Tumor counts")
gg <- gg + expand_limits(x = world_map$long, y = world_map$lat)
gg <- gg + labs(x="", y="", title="Tumor contribution by country")
gg <- gg + theme(panel.grid=element_blank(), panel.border=element_blank())
gg <- gg + theme(axis.ticks=element_blank(), axis.text=element_blank())
gg <- gg + theme(legend.position="top")
gg

# BUT you might want to show the counts by intron/exon by country

gg <- ggplot(arc.combined[arc.combined$EorI=="exon",])
gg <- gg + geom_map(dat=world_map, map = world_map, aes(map_id=id), 
                    fill="white", color="#7f7f7f", size=0.25)
gg <- gg + geom_map(map = world_map, aes(map_id = id, fill = ei.ct), size=0.25)
gg <- gg + scale_fill_gradient(low="#f7fcb9", high="#238443", name="Tumor counts")
gg <- gg + expand_limits(x = world_map$long, y = world_map$lat)
gg <- gg + labs(x="", y="", title="Tumor contribution by 'exon' & country")
gg <- gg + theme(panel.grid=element_blank(), panel.border=element_blank())
gg <- gg + theme(axis.ticks=element_blank(), axis.text=element_blank())
gg <- gg + theme(legend.position="top")
gg.exon <- gg

gg <- ggplot(arc.combined[arc.combined$EorI=="intron",])
gg <- gg + geom_map(dat=world_map, map = world_map, aes(map_id=id), 
                    fill="white", color="#7f7f7f", size=0.25)
gg <- gg + geom_map(map = world_map, aes(map_id = id, fill = ei.ct), 
                    colour = "#7f7f7f", size=0.25)
gg <- gg + scale_fill_gradient(low="#ece7f2", high="#0570b0", name="Tumor counts")
gg <- gg + expand_limits(x = world_map$long, y = world_map$lat)
gg <- gg + labs(x="", y="", title="Tumor contribution by 'intron' & country")
gg <- gg + theme(panel.grid=element_blank(), panel.border=element_blank())
gg <- gg + theme(axis.ticks=element_blank(), axis.text=element_blank())
gg <- gg + theme(legend.position="top")
gg.intron <- gg

grid.arrange(gg.exon, gg.intron, ncol=1)

Hi there. Took some time but worked my may around the issues with maptoolsand gpclibPermit(). On line 32 I believe there's a small mistake in your code:

world_map <- world[order(world_map$order), ] ## should be
world_map <- world_map[order(world_map$order), ]

If you change all of that I get the respective plots. Thanks for that!
Sent you an msge as well, which is not at all of priority. But thanks so much for everything until now. !
Cheers,

O.