library(sp)
library(spdep)
library(ggplot2)
library(ggmap)
library(rgdal)
Get and fiddle with data:
nc.sids <- readShapePoly(system.file("etc/shapes/sids.shp", package="spdep")[1],ID="FIPSNO", proj4string=CRS("+proj=longlat +ellps=clrk66"))
nc.sids=spTransform(nc.sids,CRS("+init=epsg:4326"))
Get background map from stamen.com, plot, looks nice:
ncmap = get_map(location=as.vector(bbox(nc.sids)),source="stamen",maptype="toner",zoom=7)
ggmap(ncmap)
Create a data frame with long,lat,Z, and plot over the map and a blank plot:
ncP = data.frame(coordinates(nc.sids),runif(nrow(nc.sids)))
colnames(ncP)=c("long","lat","Z")
ggmap(ncmap)+geom_point(aes(x=long,y=lat,col=Z),data=ncP)
ggplot()+geom_point(aes(x=long,y=lat,col=Z),data=ncP)
give it some unique ids called 'id' and fortify (with vitamins and iron?)
nc.sids#data[,1]=1:nrow(nc.sids)
names(nc.sids)[1]="id"
ncFort = fortify(nc.sids)
Now, my map and my limits, I want to plot the 74 birth rate:
myMap = geom_map(aes(fill=BIR74,map_id=id),map=ncFort,data=nc.sids#data)
Limits = expand_limits(x=ncFort$long,y=ncFort$lat)
and on a blank plot I can:
ggplot() + myMap + Limits
but on a ggmap I can't:
ggmap(ncmap) + myMap + Limits
# Error in eval(expr, envir, enclos) : object 'lon' not found
Some versions:
> packageDescription("ggplot2")$Version
[1] "0.9.0"
> packageDescription("ggmap")$Version
[1] "2.0"
I can add geom_polygon to ggplot or ggmap and it works as expected. So something is up with geom_map....
The error message is, I think, the result of an inheritance issue. Typically, it comes about when different data frames are used in subsequent layers.
In ggplot2, every layer inherits default aes mappings set globally in the initial call to ggplot. For instance, ggplot(data = data, aes(x = x, y = y)) sets x and y mappings globally so that all subsequent layers expect to see x and y in whatever data frame has been assigned to them. If x and y are not present, an error message similar to the one you got results. See here for a similar problem and a range of solutions.
In your case, it's not obvious because the first call is to ggmap - you can't see the mappings nor how they are set because ggmap is all nicely wrapped up. Nevertheless, ggmap calls ggplot somewhere, and so default aesthetic mappings must have been set somewhere in the initial call to ggmap. It follows then that ggmap followed by geom_map without taking account of inheritance issues results in the error.
So, Kohske's advice in the earlier post applies - "you need to nullify the lon aes in geom_map when you use a different dataset". Without knowing too much about what has been set or how they've been set, it's probably simplest to globber the lot by adding inherit.aes = FALSE to the second layer - the call to geom_map.
Note that you don't get the error message with ggplot() + myMap + Limits because no aesthetics have been set in the ggplot call.
In what follows, I'm using R version 2.15.0, ggplot2 version 0.9.1, and ggmap version 2.1. I use your code almost exactly, except for the addition of inherit.aes = FALSE in the call to geom_map. That one small change allows ggmap and geom_map to be superimposed:
library(sp)
library(spdep)
library(ggplot2)
library(ggmap)
library(rgdal)
#Get and fiddle with data:
nc.sids <- readShapePoly(system.file("etc/shapes/sids.shp", package="spdep")[1],ID="FIPSNO", proj4string=CRS("+proj=longlat +ellps=clrk66"))
nc.sids=spTransform(nc.sids,CRS("+init=epsg:4326"))
#Get background map from stamen.com, plot, looks nice:
ncmap = get_map(location=as.vector(bbox(nc.sids)),source="stamen",maptype="toner",zoom=7)
ggmap(ncmap)
#Create a data frame with long,lat,Z, and plot over the map and a blank plot:
ncP = data.frame(coordinates(nc.sids),runif(nrow(nc.sids)))
colnames(ncP)=c("long","lat","Z")
ggmap(ncmap)+geom_point(aes(x=long,y=lat,col=Z),data=ncP)
ggplot()+geom_point(aes(x=long,y=lat,col=Z),data=ncP)
#give it some unique ids called 'id' and fortify (with vitamins and iron?)
nc.sids#data[,1]=1:nrow(nc.sids)
names(nc.sids)[1]="id"
ncFort = fortify(nc.sids)
#Now, my map and my limits, I want to plot the 74 birth rate:
myMap = geom_map(inherit.aes = FALSE, aes(fill=BIR74,map_id=id), map=ncFort,data=nc.sids#data)
Limits = expand_limits(x=ncFort$long,y=ncFort$lat)
# and on a blank plot I can:
ggplot() + myMap + Limits
# but on a ggmap I cant:
ggmap(ncmap) + myMap + Limits
The result from the last line of code is:
Related
I have applied DBSCAN algorithm on built-in dataset iris in R. But I am getting error when tried to visualise the output using the plot( ).
Following is my code.
library(fpc)
library(dbscan)
data("iris")
head(iris,2)
data1 <- iris[,1:4]
head(data1,2)
set.seed(220)
db <- dbscan(data1,eps = 0.45,minPts = 5)
table(db$cluster,iris$Species)
plot(db,data1,main = 'DBSCAN')
Error: Error in axis(side = side, at = at, labels = labels, ...) :
invalid value specified for graphical parameter "pch"
How to rectify this error?
I have a suggestion below, but first I see two issues:
You're loading two packages, fpc and dbscan, both of which have different functions named dbscan(). This could create tricky bugs later (e.g. if you change the order in which you load the packages, different functions will be run).
It's not clear what you're trying to plot, either what the x- or y-axes should be or the type of plot. The function plot() generally takes a vector of values for the x-axis and another for the y-axis (although not always, consult ?plot), but here you're passing it a data.frame and a dbscan object, and it doesn't know how to handle it.
Here's one way of approaching it, using ggplot() to make a scatterplot, and dplyr for some convenience functions:
# load our packages
# note: only loading dbscacn, not loading fpc since we're not using it
library(dbscan)
library(ggplot2)
library(dplyr)
# run dbscan::dbscan() on the first four columns of iris
db <- dbscan::dbscan(iris[,1:4],eps = 0.45,minPts = 5)
# create a new data frame by binding the derived clusters to the original data
# this keeps our input and output in the same dataframe for ease of reference
data2 <- bind_cols(iris, cluster = factor(db$cluster))
# make a table to confirm it gives the same results as the original code
table(data2$cluster, data2$Species)
# using ggplot, make a point plot with "jitter" so each point is visible
# x-axis is species, y-axis is cluster, also coloured according to cluster
ggplot(data2) +
geom_point(mapping = aes(x=Species, y = cluster, colour = cluster),
position = "jitter") +
labs(title = "DBSCAN")
Here's the image it generates:
If you're looking for something else, please be more specific about what the final plot should look like.
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 would like to plot a series of lat-lon points of a seal track, each coloured according to an attribute, on to a map that shows the bathymetry (100m contours) and coastline. I learnt how to create a map to show the bathymetry+coastline using marmap and ggplot2. The code is here:
dat <- getNOAA.bathy(-58,-62.5,43,46.0,res=0, keep=TRUE)
plot(dat,image=TRUE,bpal = list(c(min(dat), 0, "darkblue", "blue","lightblue"), c(0, max(dat), "gray90","gray10")),drawlabels=TRUE,deep=c(-500,200,0),shallow=c(-500,100,0),step=c(500,100,0),lwd=c(1,1,1),lty=c(1,1,1),land=TRUE)+
scaleBathy(dat, deg=1.232, x="bottomleft", inset=5) #100km
This created a useful map. However, I am stalled over how to add the seal track on to this map.
I could do this in ggmap (using the code below) but I much prefer the marmap map
myLocation <- c(-62.5,43,-58,46)
seal_map2<-get_map(location=myLocation,maptype="watercolor",source="stamen",zoom=10)
ggmap(seal_map2)+
geom_point(data=sealtrack,aes(color=category),size=0.5)+
scale_color_gradientn(colours=rainbow(6), breaks=seq(1,6,by=1))
Any guidance will be much appreciated
You should be able to add the bathymetric info from marmap as a contour layer on your plot after "fortifying" it. Without your data it's difficult to make sure that it works (and the NOAA server is down for me right now):
library(ggplot2)
library(marmap)
dat <- getNOAA.bathy(-58,-62.5,43,46.0,res=0, keep=TRUE)
dat <- fortify(dat)
ggmap(seal_map2) +
geom_contour(dat, aes(x = x, y = y, z = z)) +
geom_point(data=sealtrack,aes(color=category),size=0.5) +
scale_color_gradientn(colours=rainbow(6), breaks=seq(1,6,by=1))
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 want to use the gglocator() function from the ggmaps package to identify coordinates on a map to position sub-plots. The following example yields an error message.
library(maptools)
library(ggmap)
## Get shapefiles for plotting
download.file("http://ec.europa.eu/eurostat/cache/GISCO/geodatafiles/NUTS_2010_60M_SH.zip",paste0(tempdir(),"/NUTS_2010_60M_SH.zip"))
unzip(paste0(tempdir(),"/NUTS_2010_60M_SH.zip"),exdir=tempdir())
eurMap <- readShapePoly(fn=paste0(tempdir(),"/NUTS_2010_60M_SH/data/NUTS_RG_60M_2010"))
eurMapDf <- fortify(eurMap, region='NUTS_ID')
## Create the figure
gg <– ggplot(data=eurMapDf) + geom_polygon(aes(x=long, y=lat,group=group))
gg
## use gglocator()
gglocator()
## > Error in `[.data.frame`(data, , deparse(mapping$x)) :
## undefined columns selected
gglocator() works fine with the following example which leads me to suspect that the problem is related to the data passed into the plotting function (the shapefile).
## gglocator works fine with this example
qmap("National Capital Region", zoom = 11)
gglocator(2)
Is there a way to plot shapefiles with ggplot and use gglocator() to identify points? If not, is there any alternative locator function I could use? grid.locator() doesn't work either as it does not output coordinates in the right units. Any hint would be greatly appreciated.
Update
It looks like the mapping slot is not always defined when creating the gg object. I can make gglocator() run by explicitly setting the mapping to the variables of the data slot. E.g. the following works:
gg$mapping$x <- substitute(long)
gg$mapping$y <- substitute(lat)
gg
## gglocator() works as expected
gglocator()
So I guess my real question is how to ensure that mapping is set when calling ggplot.
Update 2
All works fine when the mapping is provided in the ggplot function call and not as argument to geom_polygon(). E.g.
gg2 <- ggplot(data=eurMapDf,aes(x=long, y=lat,group=group)) + geom_polygon()
gg2
## works fine now
gglocator()
I am not sure if there is a reason why mapping needs to be declared in the ggplot() call or if this is a bug. Many of the examples found around the web use the syntax I used first (or to put it differently, the syntax I used first was copy-pasted from one of the many examples I found around the web).