This week, I mentioned a series of maps, on Twitter,
some minimalist maps https://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.
10 thoughts on “Minimalist Maps”
Yes, you’re right. However, for my personal taste, I usually prefer to have a complete match as I refer to data variables, since the code seems neater to me. In any case, well done to point it out.
Hello and thank you for a very nice post and some very interesting maps
I am learning R and love cartography so i thought i would play a little with this, but i have run into a wall and hope you can help
I am trying to get a visualization limited by country
> world.cities2 = world.cities[
I believe the selection work but the plot gives an error
Error in plot.window(…) : need finite ‘xlim’ values
In addition: Warning messages:
1: In min(x) : no non-missing arguments to min; returning Inf
2: In max(x) : no non-missing arguments to max; returning -Inf
3: In min(x) : no non-missing arguments to min; returning Inf
4: In max(x) : no non-missing arguments to max; returning -Inf
I have searched for a fix but can’t get through it, any help would be highly appreciated
I think it is only because a comma is missing (you select some rows from the original dataset) and the capital S
> world.cities2 = world.cities[world.cities$country==”Sweden”, ]
I tried with and without the comma but get the same error
the problems are in the variable name you want to filter – which is called country.etc – and in the attribute value you are looking for – Sweden. So, a possible solution could be:
world.cities2 = world.cities[ world.cities$country.etc ==’Sweden’,]
Please, note the comma before the ‘]’ as highlighted by Arthur.
Hope it will be useful.
Thanks a lot, that works.
And i do feel pretty stupid, i saw the “.etc” part while checking the source but managed to overlook it…
but sometimes R does not care..
“world.cities$country” works, you don’t need the “etc” part. What was wrong in the original question was the small “s” in “sweden”.