Summary: how do I use a SpatRaster object as a base map for ggmap?
Hi, I'm doing maps in R using ggplot. I want a base map from OpenStreetMaps, and then I want to plot polygons etc on top.
The ggmap package used to be perfect for this, but using Google maps is too complicated now, and OpenStreetMaps doesn't work at all.
I've come across ggspatial::annotation_map_tile(), but it doesn't allow that many map types, and it's also super slow, so I'd like to avoid it if I can.
I've also come across maptiles::get_tiles(), which has more options and seems way faster. 👍
The problem is that the SpatRaster object (from the terra package) it returns doesn't automatically work as a base map for ggmap. Is there a way to convert it?
I've managed to get part of the way there, by looking at what happens in ggmap's get_stamenmap(), but the map comes out green. I assume it's because raster::raster() and raster::as.raster() don't work as I'd hope they might on this object type, but I don't know anything about these classes so I don't know where to go next.
library(dplyr)
library(ggmap)
library(sf)
library(maptiles)
nc <- st_read(system.file("shape/nc.shp", package = "sf"), quiet = TRUE)
nc_osm <- get_tiles(nc, crop = TRUE)
# This is what it should look like
plot_tiles(nc_osm)
# I don't know these data types I'm trying to convert to
nc_ggmap <- nc_osm %>%
raster::raster() %>%
raster::as.raster()
# Set attributes manually, like in get_stamenmap()
class(nc_ggmap) <- c("ggmap", "raster")
attr(nc_ggmap, "bb") <- data.frame(ll.lat = 33.8,
ll.lon = -84.3,
ur.lat = 36.5,
ur.lon = -75.4)
attr(nc_ggmap, "source") <- ""
# Try to map it... green 💚
nc_ggmap %>%
ggmap()
By the way, I have managed to plot the object using ggplot, by calculating the colours of each pixel and using them for the fill aesthetic, but I need to use that aesthetic for my polygons later on, which is why I'm quite keen to use the ggmap approach.
nc_osm %>%
terra::as.data.frame(xy = TRUE) %>%
as_tibble() %>%
mutate(hex = rgb(lyr.1, lyr.2, lyr.3, maxColorValue = 255)) %>%
ggplot(aes(x, y, fill = hex)) +
geom_tile() +
scale_fill_identity() +
coord_fixed() +
theme_void()
I don't really understand your question because it seems to me that you ask for something, and then afterwards you write that you managed to find a workaround to what you asked in the beginning.
That said, if what you need is simply having more than one fill aesthetic, you you can use the ggnewscale package, here.
This way you can plot multiple layers with a different fill (or color, if needed) aesthetic for every layer.
Related
Is it possible to create custom graphs using ggplot2, for example I want to create a graph of kissing people.
Simple variant
Not completely, but partially, I was able to reproduce it, everything except for the "lines of the eyes" is not clear how to mark them
But how to make a more complex graph of kissing people. In general, is it possible to somehow approximate such a curve, more voluminou?
thank you for your help.
perhaps not what you are looking for, but if you have already got the image, and want to reproduce it in ggplot, then you can use the following method:
library(tidyverse)
library(magick)
library(terra)
# read image
im <- image_read("./data/kiss_1.png")
# conver to black/white image
im2 <- im %>%
image_quantize(
max = 2,
colorspace = "gray" )
# get a matrix of the pixel-colors
m <- as.raster(im2) %>% as.matrix()
# extract coordinates of the black pixels
df <- as.data.frame(which(m == "#000000ff", arr.ind=TRUE))
df$row <- df$row * -1
# plot point
ggplot(df, aes(x = col, y = row)) + geom_point()
I am trying to create a global cartogram using the cartogram package in R. I am trying to use the data from wrld_simpl. What I expect is a cartogram in which the Population ("Pop2005" variable) is plotted. The code I have developed is this:
data(wrld_simpl)
world<-wrld_simpl
world_sf = st_as_sf(world)
world_sf_proj = st_transform(world_sf, crs = 3785)
world_cartogram <- cartogram_cont(world_sf_proj, "POP2005")
plot(world_cartogram)
Nonetheless, this has resulted in the following figure:
Do you know what is wrong with the code? Maybe the CRS? I have tried to use others CRS but I got the following error:
"Error: Using an unprojected map. This function does not give correct centroids and distances for longitude/latitude data:
Use "st_transform()" to transform coordinates to another projection."
Taken from this documentation, it is stated that
The default plot of an sf object is a multi-plot of all attributes, up
to a reasonable maximum
If you want to use the base R plot function, then use st_geometry(your_map) to plot (the geometry) an sf object.
Another possibility (which I don't recommend) is to set plot options to 1 plot maximum (options(sf_max.plot=1)), but this plots the first variable, and it might not be the best idea.
library(sf)
library(spData)
library(cartogram)
library(tidyverse)
world_sf = st_as_sf(world)
world_sf_proj = st_transform(world_sf, crs = 3785)
world_cartogram <- cartogram_cont(world_sf_proj, "pop")
plot(st_geometry(world_cartogram))
Now, sf is particularly well suited with ggplot2 and the tidyverse. In that setting, just use ggplot in combination with geom_sf.
ggplot(world_cartogram) +
geom_sf()
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$brks, include.lowest = T), labels = lab_vec))
# map
ggplot(test_33_simple) +
geom_sf(aes(fill = faktor_class), size= 0.2) +
scale_fill_viridis_d() +
theme_minimal()
I'm constructing world maps with countries color-filled with the (continuous) value depending on a column in a data frame called temp.sp. I want to put several of these maps in a graph. I construct each map using ggplot with geom_map and then construct and display the graphs using multiplot() which uses grid code.
I'm using a GeoJSON map (world <- readOGR(dsn = "ne_50m_admin_0_countries.geojson", layer = "OGRGeoJSON")). The resulting SpatialPolygonsDataFrame is 4.1 Mb and the dataframe that results from worldMap <- broom::tidy(world, region = "iso_a3") has 93391 rows. So when I run multiplot with 4 plot files, it takes a long time.
I thought that I could speed up the printing by simplifying the world map with gSimplify using code like world.simp <- gSimplify(world, tol = .1, topologyPreserve = TRUE). The resulting data frame, worldMap.simp only has 27033 rows but when I use this map I get the error message Error in unit(x, default.units) : 'x' and 'units' must have length > 0.
The error message is generated when I run this code with worldMap.simp. When I use worldMap I have no problems.
gg <- ggplot(temp.sp, aes(map_id = id))
gg <- gg + geom_map(aes(fill = temp.sp$value), map = worldMap.simp, color = "white").
I tried converting temp.sp$value to factor but it made no difference.
To summarize, using a gSimplified map causes the displaying of a graph produced with ggplot and geom_map to fail.
Rather than try to figure out what was going wrong with gSimplify, I found and downloaded a lower resolution map from http://geojson.xyz. The one I'm currently using is
https://d2ad6b4ur7yvpq.cloudfront.net/naturalearth-3.3.0/ne_110m_admin_0_countries.geojson
Note that it has a similar filename, but with 110m instead of 50m.
I have a few rasters I would like to plot using gplot in the rasterVis package. I just discovered gplot (which is fantastic and so much faster than doing data.frame(rasterToPoints(r))). However, I can't get a discrete image to show. Normally if r is a raster, I'd do:
rdf=data.frame(rasterToPoints(r))
rdf$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.