Working with shapefiles, projections and world maps in ggplot
In this post I will show some different examples of how to work with map
projections and how to plot the maps using ggplot
. Many maps that are
shown using their default projection are in the longlat-format, which is
far from optimal. For plotting world maps I prefer to use either
Robinson or Winkel Tripel projection—but many more are available—and I
will show how to use both these projections.
Before we get started you need to download a couple of shapefiles that we will use. You can find them here:
- http://www.naturalearthdata.com/downloads/110m-physical-vectors/110m-land/
- http://www.naturalearthdata.com/downloads/110m-cultural-vectors/110m-admin-0-countries/
- http://www.naturalearthdata.com/downloads/110m-cultural-vectors/110m-populated-places/
- http://www.naturalearthdata.com/downloads/110m-physical-vectors/110m-graticules/
Put them directly inside your working directory. We will use functions
from the rgdal
-package to read the shapefiles into R, so if you do not
have it, you need to install it before you continue.
This will create a longlat-projected world map.
Here the world map is shown using the Robinson
projection.
However, the Caspian sea is missing. This is because of how ggplot handles polygon holes. Ggplot will plot polygon holes as a separate polygon, thus we need to make it pseudo-transparent by changing its fill color.
![World map in ggplot2 polygon hole example][./img/map3.png]
Now the Caspian sea is visible.
If we want we can also add a graticule and a bounding box. The bounding box is useful if we want to make the sea blue—especially when using some form of curved projection. Here I have added a graticule and bounding box to the longlat-map.
Robinson projection with added graticule and bounding
box.
Here I have added country borders to the previous map
plot.
Bubble plots are a popular way of displaying information on
maps. Here I used project() to reproject the bubbles’ coordinates into
the Robinson projection.
Lastly, here is an example of the Winkel tripel projection. This
projection became popular after 1998 when the National Geographic
Society choose to use it for their world maps—using it to replace the
Robinson projection, which they previously used.
Written by Kristoffer Magnusson, a researcher in clinical psychology. You should follow him on Twitter and come hang out on the open science discord Git Gud Science.
Published May 23, 2013 (View on GitHub)
Buy Me A Coffee
A huge thanks to the 64 supporters who've bought me a 143 coffees!
SCCT/Psychology bought ☕☕☕ (3) coffees
Keep the visualizations coming!
@noelnguyen16 bought ☕ (1) coffee
Hi Kristoffer, many thanks for making all this great stuff available to the community!
Eran Barzilai bought ☕ (1) coffee
These visualizations are awesome! thank you for creating it
Someone bought ☕ (1) coffee
@elena_bolt bought ☕☕☕ (3) coffees
Thank you so much for your work, Kristoffer. I use your visualizations to explain concepts to my tutoring students and they are a huge help.
A random user bought ☕☕☕ (3) coffees
Thank you for making such useful and pretty tools. It not only helped me understand more about power, effect size, etc, but also made my quanti-method class more engaging and interesting. Thank you and wish you a great 2021!
@hertzpodcast bought ☕☕☕ (3) coffees
We've mentioned your work a few times on our podcast and we recently sent a poster to a listener as prize so we wanted to buy you a few coffees. Thanks for the great work that you do!Dan Quintana and James Heathers - Co-hosts of Everything Hertz
Chris SG bought ☕ (1) coffee
Very nice.
@whlevine bought ☕☕ (2) coffees
Thank you so much for these amazing visualizations. They're a great teaching tool and the allow me to show students things that it would take me weeks or months to program myself.
Someone bought ☕☕ (2) coffees
Cameron Proctor bought ☕☕☕ (3) coffees
Used your vizualization in class today. Thanks!
@Daniel_Brad4d bought ☕☕☕☕☕ (5) coffees
Wonderful work!
Gray Church bought ☕ (1) coffee
Thank you for the visualizations. They are fun and informative.
eshulman@brocku.ca bought ☕☕☕ (3) coffees
My students love these visualizations and so do I! Thanks for helping me make stats more intuitive.
Qamar bought ☕ (1) coffee
Someone bought ☕☕☕ (3) coffees
Adrian Helgå Vestøl bought ☕☕☕ (3) coffees
David Loschelder bought ☕☕☕☕☕ (5) coffees
Terrific work. So very helpful. Thank you very much.
Tanya McGhee bought ☕ (1) coffee
@neilmeigh bought ☕☕☕☕☕ (5) coffees
I am so grateful for your page and can't thank you enough!
@schultemi bought ☕ (1) coffee
@giladfeldman bought ☕☕☕☕☕ (5) coffees
Wonderful work, I use it every semester and it really helps the students (and me) understand things better. Keep going strong.
Dean Norris bought ☕☕☕☕☕ (5) coffees
@misteryosupjoo bought ☕☕☕ (3) coffees
For a high school teacher of psychology, I would be lost without your visualizations. The ability to interact and manipulate allows students to get it in a very sticky manner. Thank you!!!
Sal bought ☕☕☕☕☕ (5) coffees
Really super useful, especially for teaching. Thanks for this!
Neilo bought ☕ (1) coffee
Really helpful visualisations, thanks!
Chi bought ☕☕☕ (3) coffees
You Cohen's d post really helped me explaining the interpretation to people who don't know stats! Thank you!
Someone bought ☕☕☕ (3) coffees
You doing useful work !! thanks !!
@ArtisanalANN bought ☕☕☕ (3) coffees
Enjoy.
Someone bought ☕ (1) coffee
This is amazing stuff. Very slick.
Someone bought ☕ (1) coffee
Sarko bought ☕ (1) coffee
Thanks so much for creating this! Really helpful for being able to explain effect size to a clinician I'm doing an analysis for.
@jsholtes bought ☕☕☕ (3) coffees
Teaching stats to civil engineer undergrads (first time teaching for me, first time for most of them too) and grasping for some good explanations of hypothesis testing, power, and CI's. Love these interactive graphics!
@DominikaSlus bought ☕ (1) coffee
Thank you! This page is super useful. I'll spread the word.
Someone bought ☕ (1) coffee
dde@paxis.org bought ☕☕☕☕☕ (5) coffees
Very helpful to helping teach teachers about the effects of the Good Behavior Game
@notawful bought ☕☕☕ (3) coffees
Thank you for using your stats and programming gifts in such a useful, generous manner. -Jess
Mateu Servera bought ☕☕☕ (3) coffees
A job that must have cost far more coffees than we can afford you ;-). Thank you.
Melinda Rice bought ☕ (1) coffee
Thank you so much for creating these tools! As we face the challenge of teaching statistical concepts online, this is an invaluable resource.
@tmoldwin bought ☕ (1) coffee
Fantastic resource. I think you would be well served to have one page indexing all your visualizations, that would make it more accessible for sharing as a common resource.
@cdrawn bought ☕☕☕ (3) coffees
Thank you! Such a great resource for teaching these concepts, especially CI, Power, correlation.
Someone bought ☕ (1) coffee
Fantastic Visualizations! Amazing way to to demonstrate how n/power/beta/alpha/effect size are all interrelated - especially for visual learners! Thank you for creating this?
@akreutzer82 bought ☕☕☕☕☕ (5) coffees
Amazing visualizations! Thank you!
@rdh_CLE bought ☕☕☕☕☕ (5) coffees
So good!
@jackferd bought ☕ (1) coffee
Incredible visualizations and the best power analysis software on R.
Julia bought ☕☕☕ (3) coffees
Fantastic work with the visualizations!
@felixthoemmes bought ☕☕☕ (3) coffees
@dalejbarr bought ☕☕☕ (3) coffees
Your work is amazing! I use your visualizations often in my teaching. Thank you.
@notawful bought ☕☕ (2) coffees
Thank you for sharing your visualization skills with the rest of us! I use them frequently when teaching intro stats.
Cameron Proctor bought ☕ (1) coffee
Great website!
Someone bought ☕ (1) coffee
Hanah Chapman bought ☕ (1) coffee
Thank you for this work!!
Someone bought ☕ (1) coffee
Jayme bought ☕ (1) coffee
Nice explanation and visual guide of Cohen's d
Bart Comly Boyce bought ☕ (1) coffee
thank you
Dr. Mitchell Earleywine bought ☕ (1) coffee
This site is superb!
Florent bought ☕ (1) coffee
Zampeta bought ☕ (1) coffee
thank you for sharing your work.
Mila bought ☕ (1) coffee
Thank you for the website, made me smile AND smarter :O enjoy your coffee! :)
Deb bought ☕ (1) coffee
Struggling with statistics and your interactive diagram made me smile to see that someone cares enough about us strugglers to make a visual to help us out!😍
Someone bought ☕ (1) coffee
@exerpsysing bought ☕ (1) coffee
Much thanks! Visualizations are key to my learning style!
@PsychoMouse bought ☕☕☕ (3) coffees
Excellent! Well done! SOOOO Useful!😊 🐭
Someone bought ☕ (1) coffee
Sponsors
You can sponsor my open source work using GitHub Sponsors and have your name shown here.
Backers ✨❤️
Questions & Comments
Please use GitHub Discussions for any questions related to this post, or open an issue on GitHub if you've found a bug or wan't to make a feature request.
Webmentions
0 0
There are no webmentions for this page
(Webmentions sent before 2021 will unfortunately not show up here.)
Archived Comments (13)
Tack, det här är jättebra. Jag måste dock tweaka lite pga vill inte ha default centrering, så det blir lite krångliare. Jag har hellre en brytning i atlanten än i Stilla Havet, så dom flesta lösningar jag hittar för bra projektioner måste jag modda mer.. men.. jaja. Tack i alla fall ^^!
Great Tutorial. Thanks Kristoffer. I do have a problem with the projection when I get the map data from maps library:
map <- map_data("world")
map <- subset(map, region!="Antarctica")
map <- spTransform(map, CRS("+proj=wintri"))
Gives me this error:
Error in (function (classes, fdef, mtable) :
unable to find an inherited method for function ‘spTransform’ for signature ‘"data.frame", "CRS"’
Any clue of why this might be?
Thanks.
could you guys please help me out with my query.............I would like to create a calender using (R- studio) and i want to show the description pop-up on the date field when i moves my courser on a
specified date.
wanting to learn how to use shape files within R, tried replicating your tutorial. Using latest ver of required libraries and shape files. Now getting err: - Error in ogrInfo(dsn = dsn, layer = layer, encoding = encoding, use_iconv = use_iconv, : Cannot open file. Online references point to 'possible errs in topography'. I notice that your tutorial on R-bloggers now displays the map graphics as broken links. Any thoughts?
I tried but didn't succeed (sorry, just started with R), how may I change the colours of the countries, e.g. fill the United States with blue and Japan with yellow? Is there an easy way to do that?
I think readOGR/rgdal changed somehow (and I have the latest version), it only worked when I read it in as wmap <- readOGR(dsn="path to the folder which contains the shapefiles", layer="ne_110m_land").
Amazing tutorial!!
Short update: I managed to project my own data onto the map, however I failed in converting my long-lat data to robinson projection. Have to use the normal projection instead.
Hi, great post!! Is there a simple way to create a custom file with places instead of the one that you used. I have data from different places with long-and latt data, which I would love to project on that map.
Greetings
Friso
Thank you for the great and elegant post! Is there a simple way to add a specific latitude to the map? grat gives only a choice of major latitudes. What if I only want to draw a single latitude (and no longitudes) on the map?
Thanks for this. Nice post. Pretty new to the idea of mapping in R, surprised that there were so few tutorials around on the web. I was working on pulling the country names in too so you could tag a lists of countries for coding appropriately etc. Will look to post that back here once I have it refined. Cheers
This is great. Very easy to follow and expand upon. Actually, I was trying to grab the ocean shapefiles as well (I know it seems strange to overlay the ocean shapes onto the land shapefiles, but for I am doing it makes sense) and the website says that the "polygons are split into contiguous pieces." When I plot this downloaded files, it seems that it only shows the oceans around Asia and Africa. Not sure if that is supposed to be that way or I have something wrong. In the latter instance, using the routine described above does anyone know of a way to join these pieces together?
Nice post. I know the Robinson and Winkel projections work better overall, but being a Kiwi I prefer the old fashioned Mercator projection if only because the others tend to turn New Zealand into a disortorted smear at the edge of the world...
Well, the Mercator projection will make Sweden look bigger than it is, so I'm not wholly against it. Luckily Sweden is close to the prime median, else it would probably look like New Zealand...