Colored countries on world map based on ISO3 codes in R using ggplot ()
I wonder if I can use colored countries ggplot
? Using rworldmap
this is pretty straightforward and adding points to the lat / long value through is ggplot
also easy.
Is there a way ggplot
to colorize countries based on a simple table using (country names in ISO3 and a number for each country?). The coloring should be based on counting.
p <- ggplot(legend=FALSE) +
geom_polygon(fill = "darkseagreen", data=world, aes(x=long, y=lat,group=group)) +
geom_path(colour = "grey40") +
theme(panel.background = element_rect(fill = "lightsteelblue2", colour = "grey")) +
theme(panel.grid.major = element_line(colour = "grey90")
) +
theme(panel.grid.minor = element_blank()) +
theme(axis.text.x = element_blank(),
axis.text.y = element_blank()) +
theme(axis.ticks = element_blank()) +
xlab("") + ylab("")
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It's pretty simple with ggplot. In what follows, I use the GeoJSON version of Natural Earth's country boundaries (which stores the ISO3 code in iso_a3
), projected onto the Winkel-Tripel. I saved the CSV of the World Bank data in general and read this for a simple spreadsheet. Then I create two layers, one base layer for the geometry of the world and then filled polygons. Since it's predesigned, I use coord_equal
vs coord_map
(which is great for choropleths, but not much if you intend to draw lines as you need to design them first and then too):
library(maptools)
library(mapproj)
library(rgeos)
library(rgdal)
library(ggplot2)
library(jsonlite)
library(RCurl)
# naturalearth world map geojson
URL <- "https://github.com/nvkelso/natural-earth-vector/raw/master/geojson/ne_50m_admin_0_countries.geojson.gz"
fil <- basename(URL)
if (!file.exists(fil)) download.file(URL, fil)
R.utils::gunzip(fil)
world <- readOGR(dsn="ne_50m_admin_0_countries.geojson", layer="OGRGeoJSON")
# remove antarctica
world <- world[!world$iso_a3 %in% c("ATA"),]
world <- spTransform(world, CRS("+proj=wintri"))
dat_url <- getURL("https://gist.githubusercontent.com/hrbrmstr/7a0ddc5c0bb986314af3/raw/6a07913aded24c611a468d951af3ab3488c5b702/pop.csv")
pop <- read.csv(text=dat_url, stringsAsFactors=FALSE, header=TRUE)
map <- fortify(world, region="iso_a3")
# data frame of markers
labs <- data.frame(lat=c(39.5, 35.50),
lon=c(-98.35, 103.27),
title=c("US", "China"))
# pre-project them to winkel-tripel
coordinates(labs) <- ~lon+lat
c_labs <- as.data.frame(SpatialPointsDataFrame(spTransform(
SpatialPoints(labs, CRS("+proj=longlat")), CRS("+proj=wintri")),
labs@data))
gg <- ggplot()
gg <- gg + geom_map(data=map, map=map,
aes(x=long, y=lat, map_id=id, group=group),
fill="#ffffff", color=NA)
gg <- gg + geom_map(data=pop, map=map, color="white", size=0.15,
aes(fill=log(X2013), group=Country.Code, map_id=Country.Code))
gg <- gg + geom_point(data=c_labs, aes(x=lon, y=lat), size=4)
gg <- gg + scale_fill_gradient(low="#f7fcb9", high="#31a354", name="Population by Country\n(2013, log scale)")
gg <- gg + labs(title="2013 Population")
gg <- gg + coord_equal(ratio=1)
gg <- gg + ggthemes::theme_map()
gg <- gg + theme(legend.position="bottom")
gg <- gg + theme(legend.key = element_blank())
gg <- gg + theme(plot.title=element_text(size=16))
gg
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