Related
Based on this question, I try to plot color coded maps for multiple years. Consider the following minimal example:
library(ggplot2)
states <- map_data("state")
var <- data.frame(table(states$region))
var$x1 = runif(49, 5.0, 7.5)
var$x2 = 2+runif(49, 5.0, 7.5)
states$x1 <- var$x1[match(states$region,var$Var1)]
states$x2 <- var$x2[match(states$region,var$Var1)]
## first plot
map <- ggplot(states, aes(x=long, y=lat,fill=x1,group=group))+ geom_polygon()
map = map + scale_fill_gradient(low='white', high='grey20')
print(map)
## second plot
map <- ggplot(states, aes(x=long, y=lat,fill=x2,group=group))+ geom_polygon()
map = map + scale_fill_gradient(low='white', high='grey20')
print(map)
The plots work, yet the scale is adjusted to each plot. therefore comparing color across graphs is not meaningful. I would like to have a fixed "number to color" scale across all graphs (I am aware that I might lose some details in my graphs by doing so).
Add limits to scale_fill_gradient:
map <- ggplot(states, aes(x=long, y=lat,fill=x1,group=group))+ geom_polygon()
map = map + scale_fill_gradient(low='white', high='grey20', limits=c(1, 10))
print(map)
map <- ggplot(states, aes(x=long, y=lat,fill=x2,group=group))+ geom_polygon()
map = map + scale_fill_gradient(low='white', high='grey20', limits=c(1, 10))
print(map)
I am creating a choropleth map for CO2 emissions in Europe using ggplot2 and ggmap in R. The fill colour for each country corresponds to its CO2 emissions. The colours and continuous legend are implemented with scale_fill_gradient. However, I would also like for a single country to have a colour that is not from the continuous legend (for later use in a Shiny application). I have not worked out how to have scale_fill_gradient only apply to one of multiple geom_polygon layers.
Here is my initial map:
To reproduce this map, download the map data from http://www.naturalearthdata.com/http//www.naturalearthdata.com/download/50m/cultural/ne_50m_admin_0_countries.zip
Here is the code:
library(maptools)
library(ggplot2)
library(ggmap)
# read in a world map
WorldMap <- readShapePoly(fn="ne_50m_admin_0_countries/ne_50m_admin_0_countries")
# reduce shape file to a filtered data frame
WorldMapDf <- fortify(WorldMap,region='iso_a2')
# read in CO2 emissions
GEO <- c('FR','AT','BE','DE','DK','FI','GB','IE','NL','NO','SE')
CO2 <- c(59.5,86.9,137.4,425.8,303.9,353.2,380.3,427.0,476.4,1.8,47.6)
CO2_df <- data.frame(GEO,CO2)
# the range of values that the colour scale should cover
colorbar_range <- range(CO2_df$CO2)
mean_price <- mean(colorbar_range)
# merge map polygons with electricity CO2 emissions
CO2Map <- merge(WorldMapDf, CO2_df, by.x="id", by.y="GEO", all.x = T)
CO2Map <- CO2Map[order(CO2Map$order),]
#limit data to main Europe
europe.limits <- data.frame(matrix(c(35.50,-11.43,70,31.11),nrow=2,ncol=2,byrow = T))
names(europe.limits) <- c('lat','long')
CO2MapSubset <- subset(CO2Map, long > min(europe.limits$lon) & long < max(europe.limits$lon) & lat > min(europe.limits$lat) & lat < max(europe.limits$lat))
# create x and y limits
xrange <- c(min(CO2MapSubset$long),max(CO2MapSubset$long))
yrange <- c(min(CO2MapSubset$lat),max(CO2MapSubset$lat))
initial_map <- ggplot(data=CO2MapSubset) + # data layer
geom_polygon(aes(x=long, y=lat, group=group, fill=CO2)) +
coord_map(projection = "mercator",xlim=xrange,ylim=yrange) +
scale_fill_gradient2(low='gold',mid = "white",high='firebrick2',na.value = "lightgrey",midpoint=mean_price,limits=colorbar_range, name='CO2 (g/kWh)') +
geom_path(aes(x=long, y=lat, group=group), color='black',size=0.2)
# display map
initial_map
Now, I would like to make one country a different colour (for example, blue) that is not on the continuous colour scale as shown in the legend.
I thought I could do this by adding an additional geom_polygon layer to the initial map. To make Denmark blue, I tried this:
map_with_selected_country <- initial_map +
geom_polygon(data = CO2MapSubset[CO2MapSubset$id == 'DK',], aes(x=long, y=lat, group=group, fill='blue'))
but I get the error message: 'Error: Discrete value supplied to continuous scale' because the fill 'blue' conflicts with scale_fill_gradient2. Is there a way to make scale_fill_gradient2 only point to one dataset? Or is there another way to tackle this problem?
Here's an example:
library(ggplot2)
map <- map_data("world")
map$value <- setNames(sample(1:50, 252, T), unique(map$region))[map$region]
p <- ggplot(map, aes(long, lat, group=group, fill=value)) +
geom_polygon() +
coord_quickmap(xlim = c(-50,50), ylim=c(25,75))
p + geom_polygon(data = subset(map, region=="Germany"), fill = "red")
Germany is overplotted using a red fill color:
You can adapt this example to fit your needs.
Thanks to help from some users on this site, I was able to get a nice map plot for some data using geom_point. (Get boundaries to come through on states) However, now I'm trying to clean it up as I have more years to plot and want to make sure the plot is working and providing good information. After some further research, it seems the geom_tile would actually be better for this as it would shy away from points and use a gradient.
The problem I'm running into is getting the code to work with geom_tile. It isn't plot anything and I'm not sure why.
Here's the dataset :
https://www.dropbox.com/s/0evuvrlm49ab9up/PRISM_1895_db.csv?dl=0
Here's the original code with geom_points :
PRISM_1895_db <- read.csv("/.../PRISM_1895_db.csv")
regions<- c("north dakota","south dakota","nebraska","kansas","oklahoma","texas","minnesota","iowa","missouri","arkansas", "illinois", "indiana", "wisconsin")
ggplot() +
geom_polygon(data=subset(map_data("state"), region %in% regions), aes(x=long, y=lat, group=group)) +
geom_point(data = PRISM_1895_db, aes(x = longitude, y = latitude, color = APPT), alpha = .5, size = 3.5) +
geom_polygon(data=subset(map_data("state"), region %in% regions), aes(x=long, y=lat, group=group), color="white", fill=NA)
And here is the code I've been trying, but none of the data is showing up.
ggplot() +
geom_polygon(data=subset(map_data("state"), region %in% regions), aes(x=long, y=lat, group=group)) +
geom_tile(data = PRISM_1895_db, aes(x = longitude, y = latitude, fill = APPT), alpha = 0.5, color = NA)
geom_polygon(data=subset(map_data("state"), region %in% regions), aes(x=long, y=lat, group=group), color="white", fill=NA)
geom_tile needs your x and y values to be sampled on an regular grid. It needs to be able to tile the surface in rectangles. So your data is irregularly sampled, it's not possible to divide up the raw data into a bunch of nice tiles.
One option is to use the stat_summary2d layer to divide your data into boxes and calculate the average APPT for all points in that box. This will allow you to create regular tiles. For example
ggplot() +
geom_polygon(data=subset(map_data("state"), region %in% regions), aes(x=long, y=lat, group=group)) +
stat_summary2d(data=PRISM_1895_db, aes(x = longitude, y = latitude, z = APPT)) +
geom_polygon(data=subset(map_data("state"), region %in% regions), aes(x=long, y=lat, group=group), color="white", fill=NA)
which produces
you can look at other options to control this bin sizes if you like. But as you can see it's "smoothing" out the data by taking averages inside bins.
I'm trying to draw a choropleth map of Germany showing poverty rate by state (inspired by this question).
The problem is that some of the states (Berlin, for example) are completely surrounded by other states (Brandenburg), and I'm having trouble getting ggplot to recognize the "hole" in Brandenburg.
The data for this example is here.
library(rgdal)
library(ggplot2)
library(RColorBrewer)
map <- readOGR(dsn=".", layer="germany3")
pov <- read.csv("gerpoverty.csv")
mrg.df <- data.frame(id=rownames(map#data),ID_1=map#data$ID_1)
mrg.df <- merge(mrg.df,pov, by="ID_1")
map.df <- fortify(map)
map.df <- merge(map.df,mrg.df[,c("id","poverty")], by="id")
ggplot(map.df, aes(x=long, y=lat, group=group)) +
geom_polygon(aes(fill=poverty))+
geom_path(colour="grey50")+
scale_fill_gradientn(colours=brewer.pal(5,"OrRd"))+
labs(x="",y="")+ theme_bw()+
coord_fixed()
Notice how the colors for Berlin and Brandenburg (in the northeast) are identical. They shouldn't be - Berlin's poverty rate is much lower than Brandenburg. It appears that ggplot is rendering the Berlin polygon and then rendering the Brandenburg polygon over it, without the hole.
If I change the call to geom_polygon(...) as suggested here, I can fix the Berlin/Brandenburg problem, but now the three northernmost states are rendered incorrectly.
ggplot(map.df, aes(x=long, y=lat, group=group)) +
geom_polygon(aes(group=poverty, fill=poverty))+
geom_path(colour="grey50")+
scale_fill_gradientn(colours=brewer.pal(5,"OrRd"))+
labs(x="",y="")+ theme_bw()+
coord_fixed()
What am I doing wrong??
This is just an expansion on #Ista's answer, which does not require that one knows which states (Berlin, Bremen) need to be rendered last.
This approach takes advantage of the fact that fortify(...) generates a column, hole which identifies whether a group of coordinates are a hole. So this renders all regions (id's) with any holes before (e.g. underneath) the regions without holes.
Many thanks to #Ista, without whose answer I could not have come up with this (believe me, I spent many hours trying...)
ggplot(map.df, aes(x=long, y=lat, group=group)) +
geom_polygon(data=map.df[map.df$id %in% map.df[map.df$hole,]$id,],aes(fill=poverty))+
geom_polygon(data=map.df[!map.df$id %in% map.df[map.df$hole,]$id,],aes(fill=poverty))+
geom_path(colour="grey50")+
scale_fill_gradientn(colours=brewer.pal(5,"OrRd"))+
labs(x="",y="")+ theme_bw()+
coord_fixed()
You can plot the island polygons in a separate layer, following the example on the ggplot2 wiki. I've modified your merging steps to make this easier:
mrg.df <- data.frame(id=rownames(map#data),ID_1=map#data$ID_1)
mrg.df <- merge(mrg.df,pov, by="ID_1")
map.df <- fortify(map)
map.df <- merge(map.df,mrg.df, by="id")
ggplot(map.df, aes(x=long, y=lat, group=group)) +
geom_polygon(aes(fill=poverty), color = "grey50", data =subset(map.df, !Id1 %in% c("Berlin", "Bremen")))+
geom_polygon(aes(fill=poverty), color = "grey50", data =subset(map.df, Id1 %in% c("Berlin", "Bremen")))+
scale_fill_gradientn(colours=brewer.pal(5,"OrRd"))+
labs(x="",y="")+ theme_bw()+
coord_fixed()
As an unsolicited act of evangelism, I encourage you to consider something like
library(ggmap)
qmap("germany", zoom = 6) +
geom_polygon(aes(x=long, y=lat, group=group, fill=poverty),
color = "grey50", alpha = .7,
data =subset(map.df, !Id1 %in% c("Berlin", "Bremen")))+
geom_polygon(aes(x=long, y=lat, group=group, fill=poverty),
color = "grey50", alpha= .7,
data =subset(map.df, Id1 %in% c("Berlin", "Bremen")))+
scale_fill_gradientn(colours=brewer.pal(5,"OrRd"))
to provide context and familiar reference points.
Just to add another small improvement to #Ista's and #jhoward's answers (thanks a lot for your help!).
The modification of #jhoward could be easily wrapped in a small function like this
gghole <- function(fort){
poly <- fort[fort$id %in% fort[fort$hole,]$id,]
hole <- fort[!fort$id %in% fort[fort$hole,]$id,]
out <- list(poly,hole)
names(out) <- c('poly','hole')
return(out)
}
# input has to be a fortified data.frame
Then, one doesn't need to recall every time how to extract holes info. The code would look like
ggplot(map.df, aes(x=long, y=lat, group=group)) +
geom_polygon(data=gghole(map.df)[[1]],aes(fill=poverty),colour="grey50")+
geom_polygon(data=gghole(map.df)[[2]],aes(fill=poverty),colour="grey50")+
# (optionally). Call by name
# geom_polygon(data=gghole(map.df)$poly,aes(fill=poverty),colour="grey50")+
# geom_polygon(data=gghole(map.df)$hole,aes(fill=poverty),colour="grey50")+
scale_fill_gradientn(colours=brewer.pal(5,"OrRd"))+
labs(x="",y="")+ theme_bw()+
coord_fixed()
Alternatively you could create that map using rworldmap.
library(rworldmap)
library(RColorBrewer)
library(rgdal)
map <- readOGR(dsn=".", layer="germany3")
pov <- read.csv("gerpoverty.csv")
#join data to the map
sPDF <- joinData2Map(pov,nameMap='map',nameJoinIDMap='VARNAME_1',nameJoinColumnData='Id1')
#default map
#mapPolys(sPDF,nameColumnToPlot='poverty')
colours=brewer.pal(5,"OrRd")
mapParams <- mapPolys( sPDF
,nameColumnToPlot='poverty'
,catMethod="pretty"
,numCats=5
,colourPalette=colours
,addLegend=FALSE )
do.call( addMapLegend, c( mapParams
, legendLabels="all"
, legendWidth=0.5
))
#to test state names
#text(pov$x,pov$y,labels=pov$Id1)
I have a data set about all the counties in Minnesota, and one of the columns is its shape. For each county it looks something like this:
For Aitkin County:
<Polygon><outerBoundaryIs><LinearRing><coordinates>-93.051956,46.15767700000001,0 -93.434006,46.15313,0 -93.43261,46.240253,0 -93.80480900000001,46.23817100000001,0 -93.80933400000001,46.580681,0 -93.77426199999999,46.59050400000001,0 -93.77412400000001,46.802605,0 -93.77500100000002,47.030445,0 -93.058258,47.022362,0 -93.05964600000001,46.766071,0 -93.05208600000002,46.417576,0 -93.051956,46.15767700000001,0</coordinates></LinearRing></outerBoundaryIs></Polygon>
I'm fairly new to R and know nothing about Google API, HTML, etc. I'm trying to use the ggplot2 and maps packages to create an intensity map for various aspects of all the counties in Minnesota. Is there a way to use these coordinates as they are to make a layer of counties, or do I need to do something else?
Here's the code I have so far:
Map of MN:
library(maps)
library(ggplot2)
all_states <- map_data("state")
mn<-subset(all_states, region %in% c("minnesota"))
p<-ggplot()
p<-p+geom_polygon(data=mn, aes(x=long, y=lat, group=group), colour="black", fill="white")
p
And my plan is to modify the following to apply to each county, once I get those polygons:
dataset <- data.frame(region=states,val=runif(49, 0,1))
us_state_map <- map_data('state')
map_data <- merge(us_state_map, dataset, by='region', all=T)
map_data <- map_data[order(map_data$order), ]
(qplot(long, lat, data=map_data, geom="polygon", group=group, fill=val)
+ theme_bw() + labs(x="", y="", fill="")
+ scale_fill_gradient(low='#EEEEEE', high='darkgreen')
+ opts(title="Title",
legend.position="bottom", legend.direction="horizontal"))
Any suggestions would be greatly appreciated!