How to get good maps in R and avoid the expensive softwares

How to convey as much information as possible in a clear and simple way? Producing maps for social sciences is not difficult, there are a plethora of softwares that can help us. But there are a few issues to consider when choosing your to go program:
(1) Do I want to do all my analysis in one (or more) program(s) and then switch to another one to make those maps?
(2) Are those programs freely accessible to me?
1. Using more than one softwares usually implies spending time to learn different syntax:  why do your analysis in (insert name here ____) and then plot in R when you can do everything in R?
2. The availability of mapping softwares is no trivial issue. Not all researchers have powerful computers, not all institutes have bottomless funds to buy licences, and sometimes having the possibility to map on your laptop while bingeing on Netflix is way nicer than waiting for the one computer with the one licence.

Probably the best and most elegant mapping tool available to Geographers is ArcGIS (to my knowledge, but again, I use R and own a Mac), however it does not come for free. What to do? Well, R is a very good alternative, you can produce elegant maps, customizable to the very last detail. The only drawback I have encountered is the time you would spend to get the first map, but then you would have the syntax and any other map would be pretty quick to plot, and you can always for loop all graphics (although I do not recommend it). Moreover, R runs on your Mac (and Linux), it allows for way more control over features, and has great color palettes (see here and here).

Here are some useful libraries:
library(maps) #for creating geographical maps
library(RColorBrewer) #contains color palettes
library(classInt) #defines the class intervals for the color palettes
library(maptools) #tools for handling spatial objects
library(raster) #tools to deal with raster maps
library(ggplot2) #to create maps, quick and painless

Some stuff to keep in mind:
(1) add a scale with (or a nice  scalebar);
(2) it is sometimes required to add a north arrow, you can find many versions for that (see this document on page 4 for  examples, I use the same with no labels);
(3) locator() is a very useful tool to get the coordinates when adding labels, arrows, scales and so on.

Part 1: get a plain map.

Below is a very simple example produced using EUROSTAT shape files for world countries (world) and DIVA-GIS for Spain at NUTS3 level (spain). In this map I have removed the Canary Islands, but you can always cut it and paste it in the map using either par(fig=c(…)) or par(fin(…)), inset, or something more elaborated with layout, and framing it using box() or rectangle.

world is the shapefile for the whole world, where I select the neighboring countries I want to appear in the map, in this case Spain, France, Portugal, France, Morocco, and Algeria.
spain is the Spain NUTS3 shapefile where I remove the Canary Islands (45)

plot(spain[-c(45),], border=F) #this first line does not plot anything, it just centers my graph on Spain, the -c(45) removes the Canary Islands
plot(world[c(6, 67,74, 132, 177),], border="lightblue",add=T, col="beige") #plotting the countries appearing in the map
plot(spain[-c(45),], border="brown", lwd=0.2, add=T, col="lightblue") #plot spain, removing the Canary Islands
map.scale(3,35.81324, ratio=F, cex=0.7, relwidth=0.1) # scale map
northarrow(c(4.8,42.9),0.7, cex=0.8)


Part 2: Add labels

Using the function shadow text to avoid labels overlapping.

coords<- coordinates(spain) # get goordinates of the centroids, it's where you center your labels
# p.names is a data frame containing the coordinates and all the names of the provinces (remember to get rid of those you don't want to use if using only a selection). Usually you can find the names in the shapefile, but I didn't have them.
shadowtext(p.names[,1],p.names[,2], label=paste(p.names[,3]), cex=0.7,col="black", bg="white",r=0.1)




Author: acarioli

is a PostDoc at the Geography and Environment department of the University of Southampton, WorldPop project team. She is also affiliated researcher at CED, UAB and Dondena Centre. Her interests include spatial econometrics and modeling, bayesian methods, machine learning processes, forecasting, micro-data simulation, and data visualization. Demo-traveler, Mac enthusiast, R zealot and Rladies member.