I stumbled upon fastshp library and according to description (and my quick cursory tests) it really does offer improvements in time of reading large shapefiles compared to three other methods.
I'm using read.shp function to load exemplary dataset from maptools package:
library("maptools")
setwd(system.file("shapes", package="maptools"))
shp <- read.shp("columbus.shp", format="polygon")
I chose 'polygon' format since accordng to docs:
This is typically the preferred format for plotting.
My question is how can I plot these polygons using ggplot2 package?
Since read.shp in the fastshp package returns the polygon data in the form of a list of lists, it is then a matter of reducing it to a single dataframe required for plotting in ggplot2.
library(fastshp)
library(ggplot2)
setwd(system.file("shapes", package="maptools"))
shp <- read.shp("columbus.shp", format="polygon")
shp.list <- sapply(shp, FUN = function(x) do.call(cbind, x[c("id","x","y")]))
shp.df <- as.data.frame(do.call(rbind, shp.list))
shp.gg <- ggplot(shp.df, aes(x = x, y=y, group = id))+geom_polygon()
EDIT: Based on #otsaw's comment regarding polygon holes, the following solution requires a couple of more steps but ensures that the holes are plotted last. It takes advantage that shp.df$hole is logical and polygons with hole==TRUE will be plotted last.
shp.list <- sapply(shp, FUN = function(x) Polygon(cbind(lon = x$x, lat = x$y)))
shp.poly <- Polygons(shp.list, "area")
shp.df <- fortify(shp.poly, region = "area")
shp.gg <- ggplot(shp.df, aes(x = long, y=lat, group = piece, order = hole))+geom_polygon()
Related
I'm trying to perform Kernel density estimation in R using some GPS data that I have. My aim is to create a contoured output with each line representing 10% of the KDE. From here i want to import the output (as a shapefile or raster) into either QGIS or arcmap so I can overlay the output on top of existing environmental layers.
So far i have used AdehabitatHR to create the following output using the below code:
kud<-kernelUD(locs1[,1], h="href")
vud<-getvolumeUD(kud)
vud <- estUDm2spixdf(vud)
xyzv <- as.image.SpatialGridDataFrame(vud)
contoured<-contour(xyzv, add=TRUE)
Aside from being able to remove the colour, this is how i wish the output to appear (or near to). However i am struggling to figure out how i can export this as either a shapefile or raster? Any suggestions would be gratefully received.
With the amt package this should be relatively straightforward:
library(adehabitatHR)
library(sf)
library(amt)
data("puechabonsp")
relocs <- puechabonsp$relocs
hr <- as.data.frame(relocs) %>% make_track(X, Y, name = Name) %>%
hr_kde(trast = raster(amt::bbox(., buffer = 2000), res = 50)) %>%
hr_isopleths(level = seq(0.05, 0.95, 0.1))
# Use the sf package to write a shape file, or any other supported format
st_write(hr, "~/tmp/home_ranges.shp")
Note, it is also relatively easy to plot
library(ggplot2)
ggplot(hr) + geom_sf(fill = NA, aes(col = level))
I am trying to work with municipality data in Norway, and I'm totally new to QGIS, shapefiles and plotting this in R. I download the municipalities from here:
Administrative enheter kommuner / Administrative units municipalities
Reproducible files are here:
Joanna's github
I have downloaded QGIS, so I can open the GEOJson file there and convert it to a shapefile. I am able to do this, and read the data into R:
library(sf)
test=st_read("C:/municipality_shape.shp")
head(test)
I have on my own given the different municipalities different values/ranks that I call faktor, and I have stored this classification in a dataframe that I call df_new. I wish to merge this "classification" on to my "test" object above, and wish to plot the map with the classification attribute onto the map:
test33=merge(test, df_new[,c("Kommunekode_str","faktor")],
by=c("Kommunekode_str"), all.x=TRUE)
This works, but when I am to plot this with tmap,
library(tmap)
tmap_mode("view")
tm_shape(test33) +
tm_fill(col="faktor", alpha=0.6, n=20, palette=c("wheat3","red3")) +
tm_borders(col="#000000", lwd=0.2)
it throws this error:
Error in object[-omit, , drop = FALSE] : incorrect number of
dimensions
If I just use base plot,
plot(test33)
I get the picture:
You see I get three plots. Does this has something to do with my error above?
I think the main issue here is that the shapes you are trying to plot are too complex so tmap is struggling to load all of this data. ggplot also fails to load the polygons.
You probably don't need so much accuracy in your polygons if you are making a choropleth map so I would suggest first simplifying your polygons. In my experience the best way to do this is using the package rmapshaper:
# keep = 0.02 will keep just 2% of the points in your polygons.
test_33_simple <- rmapshaper::ms_simplify(test33, keep = 0.02)
I can now use your code to produce the following:
tmap_mode("view")
tm_shape(test_33_simple) +
tm_fill(col="faktor", alpha=0.6, n=20, palette=c("wheat3","red3")) +
tm_borders(col="#000000", lwd=0.2)
This produces an interactive map and the colour scheme is not ideal to tell differences between municipalities.
static version
Since you say in the comments that you are not sure if you want an interactive map or a static one, I will give an example with a static map and some example colour schemes.
The below uses the classInt package to set up breaks for your map. A popular break scheme is 'fisher' which uses the fisher-jenks algorithm. Make sure you research the various different options to pick one that suits your scenario:
library(ggplot2)
library(dplyr)
library(sf)
library(classInt)
breaks <- classIntervals(test_33_simple$faktor, n = 6, style = 'fisher')
#label breaks
lab_vec <- vector(length = length(breaks$brks)-1)
rounded_breaks <- round(breaks$brks,2)
lab_vec[1] <- paste0('[', rounded_breaks[1],' - ', rounded_breaks[2],']')
for(i in 2:(length(breaks$brks) - 1)){
lab_vec[i] <- paste0('(',rounded_breaks[i], ' - ', rounded_breaks[i+1], ']')
}
test_33_simple <- test_33_simple %>%
mutate(faktor_class = factor(cut(faktor, breaks$brks, include.lowest = T), labels = lab_vec))
# map
ggplot(test_33_simple) +
geom_sf(aes(fill = faktor_class), size= 0.2) +
scale_fill_viridis_d() +
theme_minimal()
My problem: I want to draw a map obtained via rastermap package with ggplot2.
Searching for alternatives to ggmap package I found the rastermap package which provides an easy way to obtain maps from external sources. The readme provides a very simple example:
# install.packages("devtools")
devtools::install_github("hadley/rastermap")
houston <- fetch_region(c(-95.80204, -94.92313), c(29.38048, 30.14344),
stamen("terrain"))
houston
plot(houston)
The problem comes whether I try to plot using ggplot. So far I've tried several ways but none of them seems to work. Is it possible? Any idea?
rastermap generates a matrix of colours in hexadecimal strings (#RRGGBB format). It may be simplest to convert this to a more common form for spatial data, a multiband raster brick, with separate layers for the red, green and blue.
We can write a short helper function to convert hexadecimal strings into the separate integer values (i.e. this is the reverse of the rgb() function):
un_rgb = function (x) {
x = unlist(str_split(x, ''))
r = paste0(x[2], x[3])
g = paste0(x[4], x[5])
b = paste0(x[6], x[7])
strtoi(c(r,g,b), 16)
}
Using this function we convert the rastermap matrix into a three band raster brick:
library(raster)
m = as.matrix(houston)
l=lapply(m[], un_rgb)
r=g=b=matrix(, dim(m)[1], dim(m)[2])
r[] = sapply(l, function(i) i[1])
g[] = sapply(l, function(i) i[2])
b[] = sapply(l, function(i) i[3])
rgb.brick = brick(raster(r), raster(g), raster(b))
And set the extent of the new raster to that of the original rastermap
extent(rgb.brick) = extent(matrix(unlist(attributes(houston)$bb), nrow=2))
Now that we have a more usual form of raster object, we can do various things with it. For example, we can plot it in ggplot using library(RStoolbox):
ggRGB(rgb.brick, r=1, g=2, b=3)
Or we can save it as an image to use as an annotation background in ggplot:
png('test.png', dim(rgb.brick)[2], dim(rgb.brick)[1])
plotRGB(rgb.brick, 1, 2, 3)
dev.off()
img <- png::readPNG("test.png")
gr <- grid::rasterGrob(img, interpolate=TRUE)
ggplot() + annotation_custom(gr, -Inf, Inf, -Inf, Inf)
Why would you want an alternative? You can get a stamen map from ggmap:
library(ggmap)
ggmap(get_stamenmap(c(-95.80204, 29.38048, -94.92313, 30.14344))) +
# some points to plot
geom_point(aes(x = seq(-95.5, -95.2, 0.1), y = seq(29.7, 30, 0.1)), color = "red")
I have a few rasters I would like to plot using gplot in the rasterVis package. I just discovered gplot (which is fantastic and so much faster than doing data.frame(rasterToPoints(r))). However, I can't get a discrete image to show. Normally if r is a raster, I'd do:
rdf=data.frame(rasterToPoints(r))
rdf$cuts=cut(rdf$value,breaks=seq(0,max(rdf$value),length.out=5))
ggplot(rdf)+geom_raster(aes(x,y,fill=cuts))
But is there a way to avoid the call to rasterToPoints? It is very slow with large rasters. I did find I could do:
cuts=cut_interval(r#data#values,n=5)
but if you set the fill to cuts it plots the integer representation of the factors.
Here is some reproducible data:
x=seq(-107,-106,.1)
y=seq(33,34,.1)
coords=expand.grid(x,y)
rdf=data.frame(coords,depth=runif(nrow(coords),0,2)))
names(rdf)=c('x','y','value')
r=rasterFromXYZ(rdf)
Thanks
gplot is a very simple wrapper around ggplot so don't expect too
much from it. Instead, you can use part of its code to build your own
solution. The main point here is to use sampleRegular to reduce the
number of points to be displayed.
library(raster)
library(ggplot2)
x <- sampleRegular(r, size=5000, asRaster = TRUE)
dat <- as.data.frame(r, xy=TRUE)
dat$cuts <- cut(dat$value,
breaks=seq(0, max(dat$value), length.out=5))
ggplot(aes(x = x, y = y), data = dat) +
geom_raster(aes(x, y, fill=cuts))
However, if you are open to plot without ggplot2 you may find useful
this other
answer.
I have a set of data which contains around 150,000 observations of 800 subjects. Each observation has: subject ID, latitude, longitude, and the time that the subject was at those coordinates. The data covers a 24-hour period.
If I plot all the data at once I just get a blob. Is anyone able to give me some tips as to how I can animate this data so that I can observe the paths of the subjects as a function of time?
I've read the spacetime vignette but I'm not entirely sure it will do what I want. At this point I'm spending a whole lot of time googling but not really coming up with anything that meets my needs.
Any tips and pointers greatly appreciated!
Here my first use of animation package. It was easier than I anticipated and especially the saveHTML is really amazing. Here my scenario(even I think that my R-code will be clearer:)
I generate some data
I plot a basic plot for all persons as a background plot.
I reshape data to get to a wide format in a way I can plot an arrow between present and next position for each person.
I loop over hours , to generate many plots. I put the llop within the powerful saveHTML function.
You get a html file with a nice animation. I show here one intermediate plot.
Here my code:
library(animation)
library(ggplot2)
library(grid)
## creating some data of hours
N.hour <- 24
dat <- data.frame(person=rep(paste0('p',1:3),N.hour),
lat=sample(1:10,3*N.hour,rep=TRUE),
long=sample(1:10,3*N.hour,rep=TRUE),
time=rep(1:N.hour,each=3))
# the base plot with
base <- ggplot() +
geom_point(data=dat,aes(x=lat, y=long,colour = person),
size=5)+ theme(legend.position = "none")
## reshape data to lat and long formats
library(plyr)
dat.segs <- ddply(dat,.(person),function(x){
dd <- do.call(rbind,
lapply(seq(N.hour-1),
function(y)c(y,x[x$time %in% c(y,y+1),]$lat,
x[x$time %in% c(y,y+1),]$long)))
dd
})
colnames(dat.segs) <- c('person','path','x1','x2','y1','y2')
# a function to create the animation
oopt <- ani.options(interval = 0.5)
saveHTML({
print(base)
interval = ani.options("interval")
for(hour in seq(N.hour-1)){
# a segment for each time
tn <- geom_segment(aes(x= x1, y= y1, xend = x2,
yend = y2,colour = person),
arrow = arrow(), inherit.aes = FALSE,
data =subset(dat.segs,path==hour))
print(base <- base + tn)
ani.pause()
}
}, img.name = "plots", imgdir = "plots_dir",
htmlfile = "random.html", autobrowse = FALSE,
title = "Demo of animated lat/long for different persons",
outdir=getwd())
Your question is a bit vague, but I will share how I have done this kind of animation in the past.
Create a function that plots all the subject locations for one time slice:
plot_time = function(dataset, time_id) {
# make a plot with your favorite plotting package (e.g. `ggplot2`)
# Save it as a file on disk (e.g. using `ggsave`), under a regular name,
# frame001.png, frame002.png, see sprintf('frame%03d', time_index)
}
Call this function on each of your timeslices, e.g. using lapply:
lapply(start_time_id:stop_time_id, plot_time)
leading to a set of graphics files on the hard drive called frame001 to framexxx.
Use a tool to render those frames into a movie, e.g. using ffmpeg, see for example.
This is a general workflow, which has been already implemented in the animation package (thanks for reminding me #mdsummer). You can probably leverage that package to get your animation.