Related
I'm trying to display labels on GIS polygon features in R using the st_centroid function in the sf library. Unfortunately, while the head() function seems to show that each polygon has different x and y coordinates associated with it, all labels get rendered overlapping at a single point on the map (which is apparently the centroid of one particular polygon). What am I doing wrong here?
Current code setup:
library("ggplot2")
library("sf")
sf::sf_use_s2(FALSE) #makes centroids not break
world <- st_read("C:/prgrm/gis/source/10m_land_and_islands.shp")
prov <- st_read("C:/prgrm/gis/edited ncm/ncm_provinces.shp")
prov <- cbind(prov, st_coordinates(st_centroid(prov))) #attaches centroids to 'prov' dataset
head(prov)
ggplot(data = world) +
geom_sf() +
geom_sf(data=prov, aes(fill="blue")) +
geom_text(data=prov, aes(X,Y, label=provname_r), size=5) +
coord_sf(xlim=c(-2000000,1000000),ylim=c(-1500000, 3000000), crs=st_crs(3310))
You may be better off with specifying the centroid placement via fun.geometry argument of the geom_sf_text() call / by the way the default is sf::st_point_on_surface() - which is a good default as it makes sure that the label is not placed inside a hole, should the polygon have one.
Consider this example, using the well known & much loved nc.shp shapefile that ships with {sf}.
library(sf)
library(ggplot2)
# in place of your world dataset
shape <- st_read(system.file("shape/nc.shp", package="sf")) # included with sf package
# in place of your prov dataset
ashe <- shape[1, ]
ggplot(data = shape) +
geom_sf() +
geom_sf(data = ashe, fill = "blue") +
geom_sf_text(data = ashe,
aes(label = NAME),
color = "red",
fun.geometry = st_centroid)
This may be a wish list thing, not sure (i.e. maybe there would need to be the creation of geom_pie for this to occur). I saw a map today (LINK) with pie graphs on it as seen here.
I don't want to debate the merits of a pie graph, this was more of an exercise of can I do this in ggplot?
I have provided a data set below (loaded from my drop box) that has the mapping data to make a New York State map and some purely fabricated data on racial percentages by county. I have given this racial make up as a merge with the main data set and as a separate data set called key. I also think Bryan Goodrich's response to me in another post (HERE) on centering county names will be helpful to this concept.
How can we make the map above with ggplot2?
A data set and the map without the pie graphs:
load(url("http://dl.dropbox.com/u/61803503/nycounty.RData"))
head(ny); head(key) #view the data set from my drop box
library(ggplot2)
ggplot(ny, aes(long, lat, group=group)) + geom_polygon(colour='black', fill=NA)
# Now how can we plot a pie chart of race on each county
# (sizing of the pie would also be controllable via a size
# parameter like other `geom_` functions).
Thanks in advance for your ideas.
EDIT: I just saw another case at junkcharts that screams for this type of capability:
Three years later this is solved. I've put together a number of processes together and thanks to #Guangchuang Yu's excellent ggtree package this can be done fairly easily. Note that as of (9/3/2015) you need to have version 1.0.18 of ggtree installed but these will eventually trickle down to their respective repositories.
I've used the following resources to make this (the links will give greater detail):
ggtree blog
move ggplot legend
correct ggtree version
centering things in polygons
Here's the code:
load(url("http://dl.dropbox.com/u/61803503/nycounty.RData"))
head(ny); head(key) #view the data set from my drop box
if (!require("pacman")) install.packages("pacman")
p_load(ggplot2, ggtree, dplyr, tidyr, sp, maps, pipeR, grid, XML, gtable)
getLabelPoint <- function(county) {Polygon(county[c('long', 'lat')])#labpt}
df <- map_data('county', 'new york') # NY region county data
centroids <- by(df, df$subregion, getLabelPoint) # Returns list
centroids <- do.call("rbind.data.frame", centroids) # Convert to Data Frame
names(centroids) <- c('long', 'lat') # Appropriate Header
pops <- "http://data.newsday.com/long-island/data/census/county-population-estimates-2012/" %>%
readHTMLTable(which=1) %>%
tbl_df() %>%
select(1:2) %>%
setNames(c("region", "population")) %>%
mutate(
population = {as.numeric(gsub("\\D", "", population))},
region = tolower(gsub("\\s+[Cc]ounty|\\.", "", region)),
#weight = ((1 - (1/(1 + exp(population/sum(population)))))/11)
weight = exp(population/sum(population)),
weight = sqrt(weight/sum(weight))/3
)
race_data_long <- add_rownames(centroids, "region") %>>%
left_join({distinct(select(ny, region:other))}) %>>%
left_join(pops) %>>%
(~ race_data) %>>%
gather(race, prop, white:other) %>%
split(., .$region)
pies <- setNames(lapply(1:length(race_data_long), function(i){
ggplot(race_data_long[[i]], aes(x=1, prop, fill=race)) +
geom_bar(stat="identity", width=1) +
coord_polar(theta="y") +
theme_tree() +
xlab(NULL) +
ylab(NULL) +
theme_transparent() +
theme(plot.margin=unit(c(0,0,0,0),"mm"))
}), names(race_data_long))
e1 <- ggplot(race_data_long[[1]], aes(x=1, prop, fill=race)) +
geom_bar(stat="identity", width=1) +
coord_polar(theta="y")
leg1 <- gtable_filter(ggplot_gtable(ggplot_build(e1)), "guide-box")
p <- ggplot(ny, aes(long, lat, group=group)) +
geom_polygon(colour='black', fill=NA) +
theme_bw() +
annotation_custom(grob = leg1, xmin = -77.5, xmax = -78.5, ymin = 44, ymax = 45)
n <- length(pies)
for (i in 1:n) {
nms <- names(pies)[i]
dat <- race_data[which(race_data$region == nms)[1], ]
p <- subview(p, pies[[i]], x=unlist(dat[["long"]])[1], y=unlist(dat[["lat"]])[1], dat[["weight"]], dat[["weight"]])
}
print(p)
This functionality should be in ggplot, I think it is coming to ggplot soonish, but it is currently available in base plots. I thought I would post this just for comparison's sake.
load(url("http://dl.dropbox.com/u/61803503/nycounty.RData"))
library(plotrix)
e=10^-5
myglyff=function(gi) {
floating.pie(mean(gi$long),
mean(gi$lat),
x=c(gi[1,"white"]+e,
gi[1,"black"]+e,
gi[1,"hispanic"]+e,
gi[1,"asian"]+e,
gi[1,"other"]+e),
radius=.1) #insert size variable here
}
g1=ny[which(ny$group==1),]
plot(g1$long,
g1$lat,
type='l',
xlim=c(-80,-71.5),
ylim=c(40.5,45.1))
myglyff(g1)
for(i in 2:62)
{gi=ny[which(ny$group==i),]
lines(gi$long,gi$lat)
myglyff(gi)
}
Also, there may be (probably are) more elegant ways of doing this in the base graphics.
As, you can see, there are quite a few problems with this that need to be solved. A fill color for the counties. The pie charts tend to be too small or overlap. The lat and long do not take a projection so sizes of counties are distorted.
In any event, I am interested in what others can come up with.
I've written some code to do this using grid graphics. There is an example here: https://qdrsite.wordpress.com/2016/06/26/pies-on-a-map/
The goal here was to associate the pie charts with specific points on the map, and not necessarily regions. For this particular solution, it is necessary to convert the map coordinates (latitude and longitude) to a (0,1) scale so they can be plotted in the proper locations on the map. The grid package is used to print to the viewport that contains the plot panel.
Code:
# Pies On A Map
# Demonstration script
# By QDR
# Uses NLCD land cover data for different sites in the National Ecological Observatory Network.
# Each site consists of a number of different plots, and each plot has its own land cover classification.
# On a US map, plot a pie chart at the location of each site with the proportion of plots at that site within each land cover class.
# For this demo script, I've hard coded in the color scale, and included the data as a CSV linked from dropbox.
# Custom color scale (taken from the official NLCD legend)
nlcdcolors <- structure(c("#7F7F7F", "#FFB3CC", "#00B200", "#00FFFF", "#006600", "#E5CC99", "#00B2B2", "#FFFF00", "#B2B200", "#80FFCC"), .Names = c("unknown", "cultivatedCrops", "deciduousForest", "emergentHerbaceousWetlands", "evergreenForest", "grasslandHerbaceous", "mixedForest", "pastureHay", "shrubScrub", "woodyWetlands"))
# NLCD data for the NEON plots
nlcdtable_long <- read.csv(file='https://www.dropbox.com/s/x95p4dvoegfspax/demo_nlcdneon.csv?raw=1', row.names=NULL, stringsAsFactors=FALSE)
library(ggplot2)
library(plyr)
library(grid)
# Create a blank state map. The geom_tile() is included because it allows a legend for all the pie charts to be printed, although it does not
statemap <- ggplot(nlcdtable_long, aes(decimalLongitude,decimalLatitude,fill=nlcdClass)) +
geom_tile() +
borders('state', fill='beige') + coord_map() +
scale_x_continuous(limits=c(-125,-65), expand=c(0,0), name = 'Longitude') +
scale_y_continuous(limits=c(25, 50), expand=c(0,0), name = 'Latitude') +
scale_fill_manual(values = nlcdcolors, name = 'NLCD Classification')
# Create a list of ggplot objects. Each one is the pie chart for each site with all labels removed.
pies <- dlply(nlcdtable_long, .(siteID), function(z)
ggplot(z, aes(x=factor(1), y=prop_plots, fill=nlcdClass)) +
geom_bar(stat='identity', width=1) +
coord_polar(theta='y') +
scale_fill_manual(values = nlcdcolors) +
theme(axis.line=element_blank(),
axis.text.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks=element_blank(),
axis.title.x=element_blank(),
axis.title.y=element_blank(),
legend.position="none",
panel.background=element_blank(),
panel.border=element_blank(),
panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
plot.background=element_blank()))
# Use the latitude and longitude maxima and minima from the map to calculate the coordinates of each site location on a scale of 0 to 1, within the map panel.
piecoords <- ddply(nlcdtable_long, .(siteID), function(x) with(x, data.frame(
siteID = siteID[1],
x = (decimalLongitude[1]+125)/60,
y = (decimalLatitude[1]-25)/25
)))
# Print the state map.
statemap
# Use a function from the grid package to move into the viewport that contains the plot panel, so that we can plot the individual pies in their correct locations on the map.
downViewport('panel.3-4-3-4')
# Here is the fun part: loop through the pies list. At each iteration, print the ggplot object at the correct location on the viewport. The y coordinate is shifted by half the height of the pie (set at 10% of the height of the map) so that the pie will be centered at the correct coordinate.
for (i in 1:length(pies))
print(pies[[i]], vp=dataViewport(xData=c(-125,-65), yData=c(25,50), clip='off',xscale = c(-125,-65), yscale=c(25,50), x=piecoords$x[i], y=piecoords$y[i]-.06, height=.12, width=.12))
The result looks like this:
I stumbled upon what looks like a function to do this: "add.pie" in the "mapplots" package.
The example from the package is below.
plot(NA,NA, xlim=c(-1,1), ylim=c(-1,1) )
add.pie(z=rpois(6,10), x=-0.5, y=0.5, radius=0.5)
add.pie(z=rpois(4,10), x=0.5, y=-0.5, radius=0.3)
A slight variation on the OP's original requirements, but this seems like an appropriate answer/update.
If you want an interactive Google Map, as of googleway v2.6.0 you can add charts inside info_windows of map layers.
see ?googleway::google_charts for documentation and examples
library(googleway)
set_key("GOOGLE_MAP_KEY")
## create some dummy chart data
markerCharts <- data.frame(stop_id = rep(tram_stops$stop_id, each = 3))
markerCharts$variable <- c("yes", "no", "maybe")
markerCharts$value <- sample(1:10, size = nrow(markerCharts), replace = T)
chartList <- list(
data = markerCharts
, type = 'pie'
, options = list(
title = "my pie"
, is3D = TRUE
, height = 240
, width = 240
, colors = c('#440154', '#21908C', '#FDE725')
)
)
google_map() %>%
add_markers(
data = tram_stops
, id = "stop_id"
, info_window = chartList
)
I want to plot the (spatial) correlation pattern between two variables and overlapped by countries borders. I use borders() function with ggplot but the xlim and ylim didn't give the exact limit I expect, instead, they give all the country. I want to have the exact limits to overlap the data.
library(ncdf4)
library(ggplot2)
library(reshape)
library(maps)
library(stringr)
library(reshape2)
library(mapdata)
library(maptools)
ncfile <- nc_open("/Volumes/KIT/CHIRPS/index/correlation/correlation.amm.allindex.nc")
lon <- ncvar_get(ncfile, "lon")
lat <- ncvar_get(ncfile, "lat")
pr <- ncvar_get(ncfile, "prptotcorrel")
ret <- list ("lat"=lat, "lon"=lon,"pr"= pr)
##
str(ret)
######## melt function for pr
melt_pr <- function(L) {
dimnames(L$pr) <- list(lon = L$lon, lat = L$lat)
rett <- melt(L$pr, value.name ="pr")
}
######
mpr <- melt_pr(ret)
head(mpr)
#
xlims=range(mpr$lon); xlims
ylims=range(mpr$lat); ylims
gcorrelation=ggplot(data = mpr, aes(x = lon, y = lat, fill = pr)) +
geom_tile() +
borders('world', xlim=xlims, ylim=ylims, colour='black', size=.2) +
coord_fixed(xlim=xlims, ylim=ylims) +
labs(x = "Longitude", y = "Latitude") +
scale_fill_gradient2(low = "blue", high = "red", mid = "white",
midpoint = 0, limit = c(-1,1), na.value = NA, name="correlation") +
theme_bw() +
coord_fixed(1.3)
gcorrelation
I expect to have country borders that fit exactly the data.
Here is what I'd like the plot to look like. Here is the image with the countries; I want just to overlap with the exact limit of shaded data.
Welcome to stack overflow. Without having a sample of your data (as code), it will be hard to help. I believe that coord_cartesian() will do what you want instead of coord_fixed()
Instead of coord_fixed() or coord_cartesian(), use the xlim and ylim parameters of coord_map().
coord_map() projects a portion of the earth, which is approximately spherical, onto a flat 2D plane using any projection defined by the mapproj package.
(As #yake84 suggested, it would be best to present a simplified version of your problem that contains code and data so people can run and test their solutions.)
Taking a step back, if you are developing fresh code, I'd avoid outdated packages like reshape, reshape2, and some of the spatial functions. Instead build on their replacements (often developed by the same people) such as tidyr, sf, and the associated functions in ggplot2, such as ggsf().
Hi #yake84 and #wibeasley, sorry I'm pretty new on Stack overflow, thanks for your suggestions, coord_map() gave the right expected map. Thanks also for the packages suggestion.
I've been having difficulties with a map I created in R. I'm trying to make a map with a grid where in each square, there's a value corresponding to a dataframe. So far, I made the shapefile and the script following some tutorials and posts from here (Download), but the final result got kinda weird.
library(xlsx)
library(ggplot2)
library(sp)
library(raster)
library(plyr)
library(dplyr)
library(tidyr)
library(sp)
library(raster)
library(rgeos)
library(rgbif)
library(viridis)
library(gridExtra)
library(rasterVis)
library(ggplot2)
library(maps)
library(rgdal)
br <- readOGR(choose.files(), "brgrid")
plot(br)
class(br)
str(br#data)
br#data$id <- rownames(br#data)
br.df <- fortify(br)
br.df <- join(br.df, br#data, by="id")
str(br.df)
tail(br.df)
names(br.df)
tail(br.df$id)
dados <- read.xlsx("ptsgrid.xlsx",6)
names(dados)
br.df <- merge(br.df, dados, by.x="id", by.y="id", all.x=T, a..ly=F)
str(br.df)
ggp <- ggplot(data=br.df, aes(x=long, y=lat, group=group))
ggp <- ggp + geom_polygon(aes(fill=value)) # draw polygons
ggp <- ggp + geom_path(color="grey", linestyle=1) # draw boundaries
ggp <- ggp + coord_equal()
ggp <- ggp + scale_fill_gradient(low = "#ffffcc", high = "#ff4444",
space = "Lab", na.value = "grey50",
guide = "colourbar")
print(ggp)
I want to know why the lines got crossed in the map, and how to fix it. Also, I wanna know if it's possible to omit some squares (I only have interest in coastal region, so it'll look better if I omit the rest). And finally, I don't know why the map boundaries got "overlayed" (maybe a projection issue?).
Welcome to Stack Overflow! What have you done so far/what does your code look like? I will share with you a map/code I made that functions as I intended, so you can replace values in my code with what you need for your needs/with your data Below is code for a map I created in R for income in the United States:
library(maps)
library(ggplot2)
usa=map_data("state")
ggplot(usa)+geom_polygon(aes(x=long,y=lat,group=group,fill=region),color="white")+coord_fixed(1.3)+guides(fill=FALSE,color=FALSE)
The result of the above code:
I downloaded your data set. I did not examine your code. But I think when you created br.df using merge(), order got messed up. In your code, I am talking about this line (br.df<-merge(br.df, dados, by.x="id", by.y="id", all.x=T, a..ly=F). You may want to check this question. You wanted to combine all data sets. But you do not have to do that.
In your data set, dados, you have 148 data points. But you have 150 grids in your map data. So I modified your EXCEL data; I added id = 0 and id = 149. Each data point has 0 and 495, respectively. As long as there is a common column name (in this case, id), you can do the following. I used geom_cartogram() in the ggalt package. The second geom_cartogram() is adding colors to the grids.
library(rgdal)
library(ggplot2)
library(ggalt)
library(readxl)
# Create a map data
foo <- readOGR(dsn = "brgrid.shp")
mygrid <- fortify(foo)
# Import the EXCEL data
dados <- read_excel("ptsgrid.xlsx", sheet = 6)
ggplot() +
geom_cartogram(data = mygrid, map = mygrid,
aes(x = long, y = lat, map_id = id),
color = "black", alpha = 0.5) +
geom_cartogram(data = dados, map = mygrid,
aes(fill = value, map_id = id)) +
scale_fill_gradient(low = "#ffffcc", high = "#ff4444",
space = "Lab", na.value = "grey50",
guide = "colourbar")
This may be a wish list thing, not sure (i.e. maybe there would need to be the creation of geom_pie for this to occur). I saw a map today (LINK) with pie graphs on it as seen here.
I don't want to debate the merits of a pie graph, this was more of an exercise of can I do this in ggplot?
I have provided a data set below (loaded from my drop box) that has the mapping data to make a New York State map and some purely fabricated data on racial percentages by county. I have given this racial make up as a merge with the main data set and as a separate data set called key. I also think Bryan Goodrich's response to me in another post (HERE) on centering county names will be helpful to this concept.
How can we make the map above with ggplot2?
A data set and the map without the pie graphs:
load(url("http://dl.dropbox.com/u/61803503/nycounty.RData"))
head(ny); head(key) #view the data set from my drop box
library(ggplot2)
ggplot(ny, aes(long, lat, group=group)) + geom_polygon(colour='black', fill=NA)
# Now how can we plot a pie chart of race on each county
# (sizing of the pie would also be controllable via a size
# parameter like other `geom_` functions).
Thanks in advance for your ideas.
EDIT: I just saw another case at junkcharts that screams for this type of capability:
Three years later this is solved. I've put together a number of processes together and thanks to #Guangchuang Yu's excellent ggtree package this can be done fairly easily. Note that as of (9/3/2015) you need to have version 1.0.18 of ggtree installed but these will eventually trickle down to their respective repositories.
I've used the following resources to make this (the links will give greater detail):
ggtree blog
move ggplot legend
correct ggtree version
centering things in polygons
Here's the code:
load(url("http://dl.dropbox.com/u/61803503/nycounty.RData"))
head(ny); head(key) #view the data set from my drop box
if (!require("pacman")) install.packages("pacman")
p_load(ggplot2, ggtree, dplyr, tidyr, sp, maps, pipeR, grid, XML, gtable)
getLabelPoint <- function(county) {Polygon(county[c('long', 'lat')])#labpt}
df <- map_data('county', 'new york') # NY region county data
centroids <- by(df, df$subregion, getLabelPoint) # Returns list
centroids <- do.call("rbind.data.frame", centroids) # Convert to Data Frame
names(centroids) <- c('long', 'lat') # Appropriate Header
pops <- "http://data.newsday.com/long-island/data/census/county-population-estimates-2012/" %>%
readHTMLTable(which=1) %>%
tbl_df() %>%
select(1:2) %>%
setNames(c("region", "population")) %>%
mutate(
population = {as.numeric(gsub("\\D", "", population))},
region = tolower(gsub("\\s+[Cc]ounty|\\.", "", region)),
#weight = ((1 - (1/(1 + exp(population/sum(population)))))/11)
weight = exp(population/sum(population)),
weight = sqrt(weight/sum(weight))/3
)
race_data_long <- add_rownames(centroids, "region") %>>%
left_join({distinct(select(ny, region:other))}) %>>%
left_join(pops) %>>%
(~ race_data) %>>%
gather(race, prop, white:other) %>%
split(., .$region)
pies <- setNames(lapply(1:length(race_data_long), function(i){
ggplot(race_data_long[[i]], aes(x=1, prop, fill=race)) +
geom_bar(stat="identity", width=1) +
coord_polar(theta="y") +
theme_tree() +
xlab(NULL) +
ylab(NULL) +
theme_transparent() +
theme(plot.margin=unit(c(0,0,0,0),"mm"))
}), names(race_data_long))
e1 <- ggplot(race_data_long[[1]], aes(x=1, prop, fill=race)) +
geom_bar(stat="identity", width=1) +
coord_polar(theta="y")
leg1 <- gtable_filter(ggplot_gtable(ggplot_build(e1)), "guide-box")
p <- ggplot(ny, aes(long, lat, group=group)) +
geom_polygon(colour='black', fill=NA) +
theme_bw() +
annotation_custom(grob = leg1, xmin = -77.5, xmax = -78.5, ymin = 44, ymax = 45)
n <- length(pies)
for (i in 1:n) {
nms <- names(pies)[i]
dat <- race_data[which(race_data$region == nms)[1], ]
p <- subview(p, pies[[i]], x=unlist(dat[["long"]])[1], y=unlist(dat[["lat"]])[1], dat[["weight"]], dat[["weight"]])
}
print(p)
This functionality should be in ggplot, I think it is coming to ggplot soonish, but it is currently available in base plots. I thought I would post this just for comparison's sake.
load(url("http://dl.dropbox.com/u/61803503/nycounty.RData"))
library(plotrix)
e=10^-5
myglyff=function(gi) {
floating.pie(mean(gi$long),
mean(gi$lat),
x=c(gi[1,"white"]+e,
gi[1,"black"]+e,
gi[1,"hispanic"]+e,
gi[1,"asian"]+e,
gi[1,"other"]+e),
radius=.1) #insert size variable here
}
g1=ny[which(ny$group==1),]
plot(g1$long,
g1$lat,
type='l',
xlim=c(-80,-71.5),
ylim=c(40.5,45.1))
myglyff(g1)
for(i in 2:62)
{gi=ny[which(ny$group==i),]
lines(gi$long,gi$lat)
myglyff(gi)
}
Also, there may be (probably are) more elegant ways of doing this in the base graphics.
As, you can see, there are quite a few problems with this that need to be solved. A fill color for the counties. The pie charts tend to be too small or overlap. The lat and long do not take a projection so sizes of counties are distorted.
In any event, I am interested in what others can come up with.
I've written some code to do this using grid graphics. There is an example here: https://qdrsite.wordpress.com/2016/06/26/pies-on-a-map/
The goal here was to associate the pie charts with specific points on the map, and not necessarily regions. For this particular solution, it is necessary to convert the map coordinates (latitude and longitude) to a (0,1) scale so they can be plotted in the proper locations on the map. The grid package is used to print to the viewport that contains the plot panel.
Code:
# Pies On A Map
# Demonstration script
# By QDR
# Uses NLCD land cover data for different sites in the National Ecological Observatory Network.
# Each site consists of a number of different plots, and each plot has its own land cover classification.
# On a US map, plot a pie chart at the location of each site with the proportion of plots at that site within each land cover class.
# For this demo script, I've hard coded in the color scale, and included the data as a CSV linked from dropbox.
# Custom color scale (taken from the official NLCD legend)
nlcdcolors <- structure(c("#7F7F7F", "#FFB3CC", "#00B200", "#00FFFF", "#006600", "#E5CC99", "#00B2B2", "#FFFF00", "#B2B200", "#80FFCC"), .Names = c("unknown", "cultivatedCrops", "deciduousForest", "emergentHerbaceousWetlands", "evergreenForest", "grasslandHerbaceous", "mixedForest", "pastureHay", "shrubScrub", "woodyWetlands"))
# NLCD data for the NEON plots
nlcdtable_long <- read.csv(file='https://www.dropbox.com/s/x95p4dvoegfspax/demo_nlcdneon.csv?raw=1', row.names=NULL, stringsAsFactors=FALSE)
library(ggplot2)
library(plyr)
library(grid)
# Create a blank state map. The geom_tile() is included because it allows a legend for all the pie charts to be printed, although it does not
statemap <- ggplot(nlcdtable_long, aes(decimalLongitude,decimalLatitude,fill=nlcdClass)) +
geom_tile() +
borders('state', fill='beige') + coord_map() +
scale_x_continuous(limits=c(-125,-65), expand=c(0,0), name = 'Longitude') +
scale_y_continuous(limits=c(25, 50), expand=c(0,0), name = 'Latitude') +
scale_fill_manual(values = nlcdcolors, name = 'NLCD Classification')
# Create a list of ggplot objects. Each one is the pie chart for each site with all labels removed.
pies <- dlply(nlcdtable_long, .(siteID), function(z)
ggplot(z, aes(x=factor(1), y=prop_plots, fill=nlcdClass)) +
geom_bar(stat='identity', width=1) +
coord_polar(theta='y') +
scale_fill_manual(values = nlcdcolors) +
theme(axis.line=element_blank(),
axis.text.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks=element_blank(),
axis.title.x=element_blank(),
axis.title.y=element_blank(),
legend.position="none",
panel.background=element_blank(),
panel.border=element_blank(),
panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
plot.background=element_blank()))
# Use the latitude and longitude maxima and minima from the map to calculate the coordinates of each site location on a scale of 0 to 1, within the map panel.
piecoords <- ddply(nlcdtable_long, .(siteID), function(x) with(x, data.frame(
siteID = siteID[1],
x = (decimalLongitude[1]+125)/60,
y = (decimalLatitude[1]-25)/25
)))
# Print the state map.
statemap
# Use a function from the grid package to move into the viewport that contains the plot panel, so that we can plot the individual pies in their correct locations on the map.
downViewport('panel.3-4-3-4')
# Here is the fun part: loop through the pies list. At each iteration, print the ggplot object at the correct location on the viewport. The y coordinate is shifted by half the height of the pie (set at 10% of the height of the map) so that the pie will be centered at the correct coordinate.
for (i in 1:length(pies))
print(pies[[i]], vp=dataViewport(xData=c(-125,-65), yData=c(25,50), clip='off',xscale = c(-125,-65), yscale=c(25,50), x=piecoords$x[i], y=piecoords$y[i]-.06, height=.12, width=.12))
The result looks like this:
I stumbled upon what looks like a function to do this: "add.pie" in the "mapplots" package.
The example from the package is below.
plot(NA,NA, xlim=c(-1,1), ylim=c(-1,1) )
add.pie(z=rpois(6,10), x=-0.5, y=0.5, radius=0.5)
add.pie(z=rpois(4,10), x=0.5, y=-0.5, radius=0.3)
A slight variation on the OP's original requirements, but this seems like an appropriate answer/update.
If you want an interactive Google Map, as of googleway v2.6.0 you can add charts inside info_windows of map layers.
see ?googleway::google_charts for documentation and examples
library(googleway)
set_key("GOOGLE_MAP_KEY")
## create some dummy chart data
markerCharts <- data.frame(stop_id = rep(tram_stops$stop_id, each = 3))
markerCharts$variable <- c("yes", "no", "maybe")
markerCharts$value <- sample(1:10, size = nrow(markerCharts), replace = T)
chartList <- list(
data = markerCharts
, type = 'pie'
, options = list(
title = "my pie"
, is3D = TRUE
, height = 240
, width = 240
, colors = c('#440154', '#21908C', '#FDE725')
)
)
google_map() %>%
add_markers(
data = tram_stops
, id = "stop_id"
, info_window = chartList
)