Over a million developers have joined DZone.

Minimalist Density Plotting in R

DZone's Guide to

Minimalist Density Plotting in R

Make minimalist maps using R for data visualization based on locations such as cities by population and airports.

· Big Data Zone ·
Free Resource

Access NoSQL and Big Data through SQL using standard drivers (ODBC, JDBC, ADO.NET). Free Download 

This week, I mentioned a series of maps, on Twitter,

some minimalist maps http://t.co/YCNPf3AR9n (poke @visionscarto) pic.twitter.com/Ip9Tylsbkv

— Arthur Charpentier (@freakonometrics) 2 Septembre 2015

Friday evening, just before leaving the office to pick-up the kids after their first week back in class, Matthew Champion (aka @matthewchampion) sent me an email, asking for more details. He wanted to know if I did produce those graphs, and if he could mention then, in a post. The truth is, I have no idea who produced those graphs, but I told him one can easily reproduce them. For instance, for the cities, in R, use

> library(maps)
> data("world.cities")
> plot(world.cities$lon,world.cities$lat,
+ pch=19,cex=.7,axes=FALSE,xlab="",ylab="")

It is possible to get a more minimalist one by plotting only cities with more than 100,000 unhabitants, e.g.,

> world.cities2 = world.cities[
+ world.cities$pop>100000,]
> plot(world.cities2$lon,world.cities2$lat,
+ pch=19,cex=.7,axes=FALSE,xlab="",ylab="")

For the airports, it was slightly more complex since on http://openflights.org/data.html#airport, 6,977 airports  are mentioned. But on http://www.naturalearthdata.com/, I found another dataset with only 891 airports.

> library(maptools)
> shape <- readShapePoints(
+ "~/data/airport/ne_10m_airports.shp")
> plot(shape,pch=19,cex=.7)

On the same website, one can find a dataset for ports,

> shape <- readShapePoints(
+ "~/data/airport/ne_10m_ports.shp")
> plot(shape,pch=19,cex=.7)

This is for graphs based on points. For those based on lines, for instance rivers, shapefiles can be downloaded from https://github.com/jjrom/hydre/tree/, and then, use

> require(maptools)
> shape <- readShapeLines(
+ "./data/river/GRDC_687_rivers.shp")
> plot(shape,col="blue")

For roads, the shapefile can be downloaded from http://www.naturalearthdata.com/

> shape <- readShapeLines(
+ "./data/roads/ne_10m_roads.shp")
> plot(shape,lwd=.5)

Last, but not least, for lakes, we need the polygons,

> shape <- readShapePoly(
+ "./data/lake/ne_10m_lakes.shp")
> plot(shape,col="blue",border="blue",lwd=2)

Nice, isn’t it? See See the world differently with these minimalist maps for Matthew Champion‘s post.

The fastest databases need the fastest drivers - learn how you can leverage CData Drivers for high performance NoSQL & Big Data Access.

big data ,data visualization ,r language ,density plots

Published at DZone with permission of

Opinions expressed by DZone contributors are their own.

{{ parent.title || parent.header.title}}

{{ parent.tldr }}

{{ parent.urlSource.name }}