Creating Inset Map with ggplot2

According to wiki.GIS.com one of the reason for using inset map is to provide a reference for an area for unfamiliar readers.  Inset map is also considered a great asset for cartographers.  Most of the GIS software available in the market have a provision for non-cartographer and beginners to create inset map. However, for R users who are into making maps, creating inset map is a bit challenging. Thanks to the post of Pascal Mickelson and Scott Chamberlain which gave users like me a guide on how to create inset map in R using ggplot2. Below is an example of map with inset created using R.

	##################################
	# Creating Inset Maps in ggplot2 #
	##################################
	library(ggplot2)
	library(raster)
	library(gridExtra)
	ph0<-getData("GADM", country="PHL", level=0) # download PHL level 0 map for ucdavis site
	phl<-getData("GADM", country="PHL", level=2) # download PHL level 2 map for ucdavis site
	mrdq<-(phl[phl$NAME_1=="Marinduque",]) # subset province of Marinduque from PHL map
	munnames<-coordinates(mrdq) # get center coordinates of municipalities of Marinduque
	munnames<-data.frame(munnames) # convert matrix format munnames object to data.frame
	munnames$label<-mrdq@data$NAME_2
	# Extent rectangle for inset map
	pol<-data.frame(xmin=121.7,xmax=122.2 ,ymin=13 ,ymax=13.7)
	# Main Map
	p1<-ggplot()+geom_polygon(data=mrdq, aes(long+0.008,lat-0.005, group=group), fill="#9ecae1")+
	geom_polygon(data=mrdq, aes(long,lat, group=group), colour="grey10",fill="#fff7bc")+
	geom_text(data=munnames, aes(x=X1, y=X2,label=label), size=3, colour="grey20")+
	coord_equal()+theme_bw()+xlab("")+ylab("")+
	scale_x_continuous(breaks=seq(121.8,122.2, 0.1), labels=c(paste(seq(121.8,122.2, 0.1),"°E", sep="")))+
	scale_y_continuous(breaks=seq(13.2,13.6, 0.1), labels=c(paste(seq(13.2,13.6, 0.1),"°N", sep="")))+
	theme(axis.text.y =element_text(angle = 90, hjust=0.5))
	#Inset
	p2<-ggplot()+geom_polygon(data=ph0, aes(long,lat,group=group),colour="grey10",fill="#fff7bc")+
	coord_equal()+theme_bw()+labs(x=NULL,y=NULL)+
	scale_x_continuous(breaks=seq(117.5,125, 2.5), labels=c(paste(seq(117.5,125, 2.5),"°E", sep="")))+
	scale_y_continuous(breaks=seq(5,20, 5), labels=c(paste(seq(5,20, 5),"°N", sep="")))+
	geom_rect(data = pol, aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax), alpha=0, colour="red", size = 1, linetype=1)+
	theme(axis.text.x =element_blank(),axis.text.y= element_blank(), axis.ticks=element_blank(),axis.title.x =element_blank(),
	axis.title.y= element_blank())
	png(file="mrdq.png",w=1800,h=1800, res=300)
	grid.newpage()
	v1<-viewport(width = 1, height = 1, x = 0.5, y = 0.5) #plot area for the main map
	v2<-viewport(width = 0.3, height = 0.3, x = 0.86, y = 0.28) #plot area for the inset map
	print(p1,vp=v1)
	print(p2,vp=v2)
	dev.off()

view raw Inset.r hosted with ❤ by GitHub

Social Media Mining with R (Book Review)

The book Social Media Mining with R is a timely text for researchers and practitioners, specially those in social sciences who want to apply the methods of social media mining and learn basic R. The author provided a good balance of theory and step-by-step approach on how to implement different social media mining techniques (e.g. lexicon-based sentiments). However, the book lacks thorough explanation on how to interpret results from the different social media mining techniques.

The book is useful as complementary material. Major books on social media mining and learning R are still necessary.

Malawi Animated Rainfall Map

Couple of months ago, Charles Langton Vanya of Malawi contacted me on how to make animated rainfall map  in R. I was very busy that I was not able to respond. Anyway, to Mr. Vanya, here is the code you can play on to produce a similar map as below.

	# Be sure to install Image Magic (www.imagemagick.org)
	library(raster)
	library(maptools)
	library(rgdal)
	library(animation)
	library(RColorBrewer)
	library (ggplot2)
	library(rgeos)
	malb<-getData("GADM", country="MWI", level=1)
	malp<-getData("worldclim", var="prec", res=0.5, lon=35, lat=-14)
	malpe<-extract(malp,malb,fun=mean, na.rm=T)
	malpe<-data.frame(malpe)
	malb@data<-malpe
	dat<-colnames(malb@data)
	malb@data$id<-row.names(malb@data)
	malbf<-fortify(malb, region="id")
	malb.df<-merge(malbf,malb@data, by="id")
	colnames(malb.df)[8:19]<-c("January","February","March","April","May","June","July","August","September","October","November","December")
	col<-brewer.pal(9,"Spectral")
	for (i in 8:19){
	var<-colnames(malb.df)[i]
	p<-ggplot(malb.df, aes(long,lat, group=group))+geom_polygon(aes_string(fill=var))+coord_equal()
	p<-p+geom_polygon(data=malb, aes(long,lat.group=group),fill=NA, colour="black")
	p<-p+scale_fill_gradientn("Rainfall",limits=c(0,330),guide="legend", breaks=seq(0,330,30), colours=col)
	p<-p+theme_bw()+ggtitle(var)
	print(p)
	ggsave(file=paste("pr",i,".png", sep=""))
	}
	files = sprintf('pr%d.png', 8:19)
	im.convert(files, output = 'Rainfall.gif')

view raw malawi.r hosted with ❤ by GitHub