James Austin (@awhstin) made some #spiffy 4-panel maps with base R graphics but also posited he didn’t use ggplot2
because:
…
ggplot2
and maps currently do not support world maps at this point, which does not give us a great overall view.
That is certainly a box I would not put ggplot2
into, especially with the newly updated R maps
(et al) packages, ggplot2
2.0 and my (still in development) ggalt
package (though this was all possible before ggplot2
2.0 and ggalt
). NOTE: I have no idea why I get so defensive about ggplot2
besides the fact that it’s one the best visualization tools ever created.
Here’s all you need to use the built-in facet options of ggplot2
to make the 4-panel plot (as James points out, you can get the data file from here: (CLIWOC15.csv)[http://www.austinwehrwein.com/wp-content/uploads/2015/12/CLIWOC15.csv]):
library(ggplot2) # FYI you need v2.0 library(dplyr) # yes, i could have not done this and just used 'subset' instead of 'filter' library(ggalt) # devtools::install_github("hrbrmstr/ggalt") library(ggthemes) # theme_map and tableau colors world <- map_data("world") world <- world[world$region != "Antarctica",] # intercourse antarctica dat <- read.csv("CLIWOC15.csv") # having factors here by default isn't a bad thing dat <- filter(dat, Nation != "Sweden") # I kinda feel bad for Sweden but 4 panels look better than 5 and it doesn't have much data gg <- ggplot() gg <- gg + geom_map(data=world, map=world, aes(x=long, y=lat, map_id=region), color="white", fill="#7f7f7f", size=0.05, alpha=1/4) gg <- gg + geom_point(data=dat, aes(x=Lon3, y=Lat3, color=Nation), size=0.15, alpha=1/100) gg <- gg + scale_color_tableau() gg <- gg + coord_proj("+proj=wintri") gg <- gg + facet_wrap(~Nation) gg <- gg + theme_map() gg <- gg + theme(strip.background=element_blank()) gg <- gg + theme(legend.position="none") gg |
You can use a separate shapefile if you want, but this is quite minimalist (a feature James suggests is desirable) and emphasizes the routes quite nicely IMO.
R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...