I\'m attempting to use ggplot2 and maps to plot the names of the counties in NY state. My approach was to find the means of latitude and longitude by county (I assume this
It sorta seems like kmeans centers would be useful... Here is a poor start... its late!
center.points <- ddply(ny, .(group), function(df) kmeans(df[,1:2], centers=1)$centers)
center.points$county <- ny$county[ny$group == center.points$group]
p + geom_text(data=center.points, aes(x=V1, y=V2, label=county))
Since you are creating two layers (one for the polygons and the second for the labels), you need to specify the data source and mapping correctly for each layer:
ggplot(ny, aes(long, lat)) +
geom_polygon(aes(group=group), colour='black', fill=NA) +
geom_text(data=cnames, aes(long, lat, label = subregion), size=2)
Note:
long
and lat
occur in both data frames, you can use aes(long, lat)
in the first call to ggplot. Any mapping you declare here is available to all layers.aes(group=group)
inside the polygon layer.aes
.Once you've done that, and the map plots, you'll realize that the midpoint is better approximated by the mean of range
, and to use a map coordinate system that respects the aspect ratio and projection:
cnames <- aggregate(cbind(long, lat) ~ subregion, data=ny,
FUN=function(x)mean(range(x)))
ggplot(ny, aes(long, lat)) +
geom_polygon(aes(group=group), colour='black', fill=NA) +
geom_text(data=cnames, aes(long, lat, label = subregion), size=2) +
coord_map()
I know this is an old question that's been answered, but I wanted to add this in case anyone looks here for future help.
The maps package has the map.text
function, which uses polygon centroids to place labels. Looking at its code, one can see that it uses the apply.polygon
and centroid.polygon
functions to find the centroids. These functions aren't visible when the package is loaded, but can still be accessed:
library(ggplot2); library(maps)
county_df <- map_data('county') #mappings of counties by state
ny <- subset(county_df, region=="new york") #subset just for NYS
ny$county <- ny$subregion
cnames <- aggregate(cbind(long, lat) ~ subregion, data=ny, FUN=mean)
# Use the map function to get the polygon data, then find the centroids
county_poly <- map("county", "new york", plot=FALSE, fill = TRUE)
county_centroids <- maps:::apply.polygon(county_poly, maps:::centroid.polygon)
# Create a data frame for graphing out of the centroids of each polygon
# with a non-missing name, since these are the major county polygons.
county_centroids <- county_centroids[!is.na(names(county_centroids))]
centroid_array <- Reduce(rbind, county_centroids)
dimnames(centroid_array) <- list(gsub("[^,]*,", "", names(county_centroids)),
c("long", "lat"))
label_df <- as.data.frame(centroid_array)
label_df$county <- rownames(label_df)
p <- ggplot(ny, aes(long, lat, group=group)) + geom_polygon(colour='black', fill=NA)
plabels <- geom_text(data=label_df, aes(label=county, group=county))
p + plabels
It was pointed out to me by @tjebo while I was trying out to make a new stat, that this stat would be an appropriate solution for this question. It's not on CRAN (yet) but lives on github.
For other people dealing with a similar problem, here is how that would work:
library(ggh4x) # devtools::install_github("teunbrand/ggh4x")
#> Loading required package: ggplot2
#> Warning: package 'ggplot2' was built under R version 4.0.2
library(maps)
county_df <- map_data('county')
ny <- subset(county_df, region=="new york")
ny$county <- ny$subregion
ggplot(ny, aes(x = long, y = lat, group = group)) +
geom_polygon(colour='black', fill=NA) +
stat_midpoint(aes(label = subregion), geom = "text",size=3) +
coord_map()
Created on 2020-07-06 by the reprex package (v0.3.0)