I have created a map of the labor mobility in Spain in 2014. I have followed this link to adapt the code: http://www.r-bloggers.com/mapping-flows-in-r/
Now, I would like to add the names of the cities to the map. Any ideas?
Here is the code I used:
# Load the data about the labor flow
input <- read.csv("~/Desktop/flujo.csv", sep=";")
# Now we need to associate the Spanish regions with geographical coordinates.
centroids <- read.csv("~/Desktop/coordinates.csv", sep=";")
# Join the coordinates with the cities
or.xy<- merge(input, centroids, by.x="origin", by.y="origin")
or.xy$o_name<-or.xy$origin
names(or.xy)<- c("origin", "destination", "trips", "oX", "oY","o_name")
dest.xy<- merge(or.xy, centroids, by.x="destination", by.y="origin")
dest.xy$d_name<-dest.xy$destination
names(dest.xy)<- c("origin", "destination", "trips", "oX", "oY","o_name", "dX", "dY","d_name")
# Now for plotting with ggplot2.This first step removes the axes in the resulting plot.
xquiet<- scale_x_continuous("", breaks=NULL)
yquiet<-scale_y_continuous("", breaks=NULL)
quiet<-list(xquiet, yquiet)
# Let’s build the plot. First we specify the dataframe we need, with a filter excluding flows of <10
mapa<-ggplot(dest.xy[which(dest.xy$trips>1),], aes(oY,oX))+
# The next line tells ggplot that we wish to plot line segments. The “alpha=” is line transparency and used below
geom_segment(aes(x=oX, y=oY,xend=dX, yend=dY, alpha=trips), col="white")+
# Here is the magic bit that sets line transparency – essential to make the plot readable
scale_alpha_continuous(range = c(0.07,0.07))+
# Set black background, remove axes and fix aspect ratio
theme(panel.background = element_rect(fill="black",colour='black'))+quiet+coord_equal()
Related
I have a map of Russia with regional subdivision
library(raster)
data <- getData('GADM', country='RUS', level=1)
http://www.gks.ru/bgd/regl/B16_14p/IssWWW.exe/Stg/d01/08-01.doc
The link is to a Word.doc with data (table) on crime rates for Russian regions. I can extract this data and use it in R. I want to take 2015 year and colorize regions on the map depending on the crime rate (also add a legend). How can I do this? The problem is that names of regions are sometimes different in the shape file (NL_NAME_1) and in the data from www.gks.ru.
I also have this code for graph that I need, except that here we have meaningless colors:
library(sp)
library(RColorBrewer)
data$region <- as.factor(iconv(as.character(data$NAME_1)))
spplot(data, "region", xlim=c(15,190), ylim=c(40,83),
col.regions=colorRampPalette(brewer.pal(12, "Set3"))(85), col = "white")
If I understand your question properly, you just need to add your data to the spatial object for making colors meaningful.
Note, please, that the data is a reserved word in R. So, it's better to modify a little your variable name:
geo_data <- getData('GADM', country = 'RUS', level = 1)
Let's emulate some data to demonstrate a visualization strategy:
set.seed(23)
geo_data#data["data_to_plot"] <- sample(1:100, length(geo_data#data$NAME_1))
Using a default GADM projection would cut the most eastern part of the country. A simple transformation helps to fit the whole area to a plot:
# fit Russian area inside the plot
geo_data_trsf <- spTransform(geo_data, CRS("+proj=longlat +lon_wrap=180"))
Draw the map selecting data_to_plot instead of region:
max_data_val <- max(geo_data_trsf#data$data_to_plot)
spplot(geo_data_trsf, zcol = "data_to_plot",
col.regions = colorRampPalette(brewer.pal(12, "Set3"))(max_data_val),
col = "white")
The plot limits are adjusted automatically for the transformed spatial data geo_data_trsf, making possible to omit xlim and ylim.
As for the problem with the names, I can't provide any ready-to-use solution. Obviously, the regions' names of NL_NAME_1 need some additional treatment to use them as labels. I think, it would be better to use NAME_1 as an identifier in your code to ensure that it'll be no troubles with encoding. The NL_NAME_1 column is perfectly suitable to set the correspondence between your Word-data and the data inside the spatial object geo_data.
I'm trying to add this plot of a function defined on Veneto (italian region)
obtained by an image and contour:
image(X,Y,evalmati,col=heat.colors(100), xlab="", ylab="", asp=1,zlim=zlimits,main=title)
contour(X,Y,evalmati,add=T)
(here you can find objects: https://dl.dropboxusercontent.com/u/47720440/bounty.RData)
on a Google Map background.
I tried two ways:
PACKAGE RGoogleMaps
I downloaded the map mbackground
MapVeneto<-GetMap.bbox(lonR=c(10.53,13.18),latR=c(44.7,46.76),size = c(640,640),MINIMUMSIZE=TRUE)
PlotOnStaticMap(MapVeneto)
but i don't know the commands useful to add the plot defined by image and contour to the map
PACKAGE loa
I tried this way:
lat.loa<-NULL
lon.loa<-NULL
z.loa<-NULL
nx=dim(evalmati)[1]
ny=dim(evalmati)[2]
for (i in 1:nx)
{
for (j in 1:ny)
{
if(!is.na(evalmati[i,j]))
{
lon.loa<-c(lon.loa,X[i])
lat.loa<-c(lat.loa,Y[j])
z.loa<-c(z.loa,evalmati[i,j])
}
}
}
GoogleMap(z.loa ~ lat.loa*lon.loa,col.regions=c("red","yellow"),labels=TRUE,contour=TRUE,alpha.regions=list(alpha=.5, alpha=.5),panel=panel.contourplot)
but the plot wasn't like the first one:
in the legend of this plot I have 7 colors, and the plot use only these values. image plot is more accurate.
How can I add image plot to GoogleMaps background?
If the use of a GoogleMap map is not mandatory (e.g. if you only need to visualize the coastline + some depth/altitude information on the map), you could use the package marmap to do what you want. Please note that you will need to install the latest development version of marmap available on github to use readGEBCO.bathy() since the format of the files generated when downloading GEBCO files has been altered recently. The data from the NOAA servers is fine but not very accurate in your region of interest (only one minute resolution vs half a minute for GEBCO). Here is the data from GEBCO I used to produce the map : GEBCO file
library(marmap)
# Get hypsometric and bathymetric data from either NOAA or GEBCO servers
# bath <- getNOAA.bathy(lon1=10, lon2=14, lat1=44, lat2=47, res=1, keep=TRUE)
bath <- readGEBCO.bathy("GEBCO_2014_2D_10.0_44.0_14.0_47.0.nc")
# Create color palettes for sea and land
blues <- c("lightsteelblue4", "lightsteelblue3", "lightsteelblue2", "lightsteelblue1")
greys <- c(grey(0.6), grey(0.93), grey(0.99))
# Plot the hypsometric/bathymetric map
plot(bath, land=T, im=T, lwd=.03, bpal = list(c(0, max(bath), greys), c(min(bath), 0, blues)))
plot(bath, n=1, add=T, lwd=.5) # Add coastline
# Transform your data into a bathy object
rownames(evalmati) <- X
colnames(evalmati) <- Y
class(evalmati) <- "bathy"
# Overlay evalmati on the map
plot(evalmati, land=T, im=T, lwd=.1, bpal=col2alpha(heat.colors(100),.7), add=T, drawlabels=TRUE) # use deep= shallow= step= to adjust contour lines
plot(outline.buffer(evalmati),add=TRUE, n=1) # Outline of the data
# Add cities locations and names
library(maps)
map.cities(country="Italy", label=T, minpop=50000)
Since your evalmati data is now a bathy object, you can adjust its appearance on the map like you would for the map background (adjust the number and width of contour lines, adjust the color gradient, etc). plot.bath() uses both image() and contour() so you should be able to get the same results as when you plot with image(). Please take a look at the help for plot.bathy() and the package vignettes for more examples.
I am not realy inside the subject, but Lovelace, R. "Introduction to visualising spatial data in R" might help you
https://github.com/Robinlovelace/Creating-maps-in-R/raw/master/intro-spatial-rl.pdf From section "Adding base maps to ggplot2 with ggmap" with small changes and data from https://github.com/Robinlovelace/Creating-maps-in-R/archive/master.zip
library(dplyr)
library(ggmap)
library(rgdal)
lnd_sport_wgs84 <- readOGR(dsn = "./Creating-maps-in-R-master/data",
layer = "london_sport") %>%
spTransform(CRS("+init=epsg:4326"))
lnd_wgs84_f <- lnd_sport_wgs84 %>%
fortify(region = "ons_label") %>%
left_join(lnd_sport_wgs84#data,
by = c("id" = "ons_label"))
ggmap(get_map(location = bbox(lnd_sport_wgs84) )) +
geom_polygon(data = lnd_wgs84_f,
aes(x = long, y = lat, group = group, fill = Partic_Per),
alpha = 0.5)
I'm using the R code shown below, which loads libraries maps and RColorBrewer, to create a map of the world with countries color-coded by population rank. As you can see in the image below, I'm using a green palette in which the darker the green, the larger the population.
I'd like to add a continuous color legend showing the full palette to denote that light green = small population and dark green = large population, but I can't find a way to do it via maps. Could you tell me what is the easiest way to add a continuous color legend (or color key/color scale) to my map?
# Load libraries
library(maps)
library(RColorBrewer)
# Load world data
data(world.cities)
# Calculate world population by country
world.pop = aggregate(x=world.cities$pop, by=list(world.cities$country.etc),
FUN=sum)
world.pop = setNames(world.pop, c('Country', 'Population'))
# Create a color palette
palette = colorRampPalette(brewer.pal(n=9, name='Greens'))(nrow(world.pop))
# Sort the colors in the same order as the countries' populations
palette = palette[rank(-world.pop$Population)]
# Draw a map of the world
map(database='world', fill=T, col=palette, bg='light blue')
The world map in the maps package is about 30 years old (e.g., has USSR & Yugoslavia).
Plus you have a glitch in your code that causes the overpopulated Greenland that #Jealie noticed (and India is less populated than Antarctica).
You can create a continuousish legend with a modern world using rworldmap.
library(rworldmap)
library(RColorBrewer)
#get a coarse resolution map
sPDF <- getMap()
#using your green colours
mapDevice('x11') #create a map shaped device
numCats <- 100 #set number of categories to use
palette = colorRampPalette(brewer.pal(n=9, name='Greens'))(numCats)
mapCountryData(sPDF,
nameColumnToPlot="POP_EST",
catMethod="fixedWidth",
numCats=numCats,
colourPalette=palette)
You can alter the legend adding more labels etc. by doing something like this :
mapParams <- mapCountryData(sPDF, nameColumnToPlot="POP_EST", catMethod="pretty", numCats=100, colourPalette=palette, addLegend=FALSE)
#add a modified legend using the same initial parameters as mapCountryData
do.call( addMapLegend, c( mapParams
, legendLabels="all"
, legendWidth=0.5
))
Just briefly to explore the glitch in your code. It occurs because you create a palette for the number of countries in world.cities (239) and then apply it to the number of polygons in the world database from maps (2026). So it probably gets recycled and the colours of your countries have no relation to population. The code below demonstrates the source of your problem.
#find the countries used in the maps world map
mapCountries <- unique( map('world',namesonly=TRUE) )
length(mapCountries)
#[1] 2026
#exclude those containing ':' e.g. "USA:Alaska:Baranof Island"
mapCountries2 <- mapCountries[-grep(':',mapCountries)]
length(mapCountries2)
#[1] 186
#which don't match between the map and world.cities ?
#cityCountries <- unique( world.cities$country.etc )
cityCountries <- world.pop$Country
length(cityCountries)
#[1] 239
#which countries are in the map but not in world.cities ?
mapCountries2[ is.na(match(mapCountries2,cityCountries)) ]
#includes USSR, Yugoslavia & Czechoslovakia
Within the library SDMTools there is the function legend.gradient
adding this code to the end of your code should give the desired result:
# Draw a map of the world
map(database='world', fill=T, col=palette, bg='light blue')
x = c(-20, -15, -15, -20)
y = c(0, 60, 60, 0)
legend.gradient(cbind(x = x - 150, y = y - 30),
cols = brewer.pal(n=9, name='Greens'), title = "TITLE", limits = "")
You will need to fiddle with the x & y coordinates to get the legend into the desired location however.
EDIT
The x and y coordinates also adjust the shape of the box so I changed the code so that the box shape would not change if you only alter the numbers within the legend.gradient function. Below is what this code should produce
my goal is to create a 3D-Visualization in R. I have a shapefile of urban districts (Ortsteile) in Berlin and want to highlight the value (inhabitants/km²) as a z-value. I have implemented the shapefile into R and coloured the value for desnity ("Einwohnerd") as followed:
library(rgdal)
library(sp)
berlin=readOGR(dsn="C...etc.", layer="Ortsteile")
berlin#data
col <- rainbow(length(levels(berlin#data$Name)))
spplot(berlin, "Einwohnerd", col.regions=col, main="Ortsteil Berlins", sub="Datensatz der Stadt Berlin", lwd=.8, col="black")
How it is posible to refer a certain polygon (urban district) to a z-value (inhabitant/km²) and how can I highlight this z-value?
Hope that someone will have an answer!
Best regars
SB
Thanks for the answer, but I am still on my wy to find out the best to use the density as z-value so that I can create a 3D Model. I found out that it is not possible to use the polygons of the shape but that it is possible to rasterize the polygon and to use a matrix for a different perspective and rotation.
Here is the code but the final 3D visualization looks not sharp and good enough. Maybe it would be better to calculate the the z-value in anther way so that the first values did not start so high or to use the center of the polygon and than to draw a column in z-direction:
library(rgdal)
library(sp)
setwd("C:\\...")
berlin=readOGR(dsn="C:\\...\\Ortsteile", layer="Ortsteile")
col <- rainbow(length(levels(berlin#data$Name)))
spplot(berlin, "Einwohnerd", col.regions=col, main="Ortsteil Berlins",
sub="Datensatz der Stadt Berlin", lwd=.8, col="black")
library(raster)
raster <- raster(nrows=100, ncols=200, extent(berlin))
test <- rasterize(berlin, raster, field="Einwohnerd")
persp(test, theta = 40, phi = 40, col = "gold", border = NA, shade = 0.5)
for(i in seq(0,90,10)){
persp(test, theta = 40, phi = i, col = "gold", border = NA, shade = 0.5)
}
library(rgl)
library(colorRamps)
mat <- matrix(test[], nrow=test#nrows, byrow=TRUE)
image(mat)
persp3d(z = mat, clab = "m")
persp3d(z = mat, col = rainbow(10),border = "black")
persp3d(z = mat, facets = FALSE, curtain = TRUE)
Is this what you had in mind?
library(ggplot2)
library(rgdal) # for readOGR(...) and spTransform(...)
library(RColorBrewer) # for brewer.pal(...)
setwd("<directory with shapefile>")
map <- readOGR(dsn=".",layer="Ortsteile")
map <- spTransform(map,CRS=CRS("+init=epsg:4839"))
map.data <- data.frame(id=rownames(map#data), map#data)
map.df <- fortify(map)
map.df <- merge(map.df,map.data,by="id")
ggplot(map.df, aes(x=long, y=lat, group=group))+
geom_polygon(aes(fill=Einwohnerd))+
geom_path(colour="grey")+
scale_fill_gradientn(colours=rev(brewer.pal(10,"Spectral")))+
theme(axis.text=element_blank())+
labs(title="Berlin Ortsteile", x="", y="")+
coord_fixed()
Explanation
This is a great question, in that it provides an example of a very basic choropleth map using ggplot in R.
Shapefiles can be read into R using readOGR(...), producing SpatialDataFrame objects. The latter have basically two sections: a polygons section containing the coordinates of the polygon boundaries, and a data section containing information from the attributes table in the shapefile. These can be referenced, respectively, as map#polygons and map#data.
The code above reads the shapefile and transforms the coordinates to epsg:4839. Then we prepend the polygon ids (stored in the rownames) to the other information in map#data, creating map.data. Then we use the fortify(...) function in ggplot to convert the polygons to a dataframe suitable for plotting (map.df). This dataframe has a column id which corresponds to the id column in map.data. Then we merge the attribute information (map.data) into map.df based on the id column.
The ggplot calls create the map layers and render the map, as follows:
ggplot: set the default dataset to map.df; identify x- and y-axis columns
geom_polygon: identify column for fill (color of polygon)
geom_path: polygon boundaries
theme: turn off axis text
labs: title, turn off x- and y-axis labels
coord_fixed: ensures that the map is not distorted
A note on scale_fill_gradientn(...): this function assigns colors to the fill values by interpolating a color palette provided in the colours= parameter. Here we use the Spectral palette from www.colorbrewer.org. Unfotrunately, this palette has the colors revered (blue - red), so we use rev(...) to reverse the color order (high=red, low=blue). If you prefer the more highly saturated colors common in matlab, use library(colorRamps) and replace the call to scale_fill_gradientn(...) with:
scale_fill_gradientn(colours=matlab.like(10))+
I have 3 shp files representing the house, room, and beds of a house respectively. I need to plot them on a graph using R so that they all overlap with each other. I know that in plot function, I can use line to plot new lines on top of the existing plot, is there anything equivalent in spplot? Thanks.
Here's one approach, using the nifty layer() function from the latticeExtra package:
# (1) Load required libraries
library(sp)
library(rgeos) # For its readWKT() function
library(latticeExtra) # For layer()
# (2) Prepare some example data
sp1 = readWKT("POLYGON((0 0,1 0,1 1,0 1,0 0))")
sp2 = readWKT("POLYGON((0 1,0.5 1.5,1 1,0 1))")
sp3 = readWKT("POLYGON((0.5 0,0.5 0.5,0.75 0.5,0.75 0, 0.5 0))")
# spplot provides "Plot methods for spatial data with attributes",
# so at least the first object plotted needs a (dummy) data.frame attached to it.
spdf1 <- SpatialPolygonsDataFrame(sp1, data=data.frame(1), match.ID=1)
# (3) Plot several layers in a single panel
spplot(spdf1, xlim=c(-0.5, 2), ylim=c(-0.5, 2),
col.regions="grey90", colorkey=FALSE) +
layer(sp.polygons(sp2, fill="saddlebrown")) +
layer(sp.polygons(sp3, fill="yellow"))
Alternatively, you can achieve the same result via spplot()'s sp.layout= argument. (Specifying first=FALSE ensures that the 'roof' and 'door' will be plotted after/above the grey square given as spplot()'s first argument.)
spplot(spdf1, xlim=c(-0.5, 2), ylim=c(-0.5, 2),
col.regions="grey90", colorkey=FALSE,
sp.layout = list(list(sp2, fill="saddlebrown", first=FALSE),
list(sp3, fill="yellow", first=FALSE)))
You can use the sp.layout argument in spplot. Alternatively, you can use ggplot2. Some example code (untested):
library(ggplot2)
shp1_data.frame = fortify(shp1)
shp1_data.frame$id = "shp1"
shp2_data.frame = fortify(shp2)
shp2_data.frame$id = "shp2"
shp = rbind(shp1_data.frame, shp2_data.frame)
ggplot(aes(x = x, y = y, group = group, col = id), data = shp) + geom_path()
In ggplot2, columns in the data are linked to graphical scales in the plot. In this case x is the x-coordinate, y is the y-coordinate, group is a column in the data.frame shp which specifies to which polygon a point belongs, and col is the color of the polygon. The geometry I used is geom_path, which draws a series of lines based on the polygon input data.frame. An alternative is to use geom_poly, which also supports filling the polygon.