I am attempting to plot coordinate data that includes both polygons and points (in separate files) in a single window so that I can later run tests to see what patterns exist. I am fairly new to R (and very new to spatstat) so I really appreciate any advice on how best to create a single plot with multiple types of spatial data.
library(sp)
library(maptools)
library(mgcv)
library(spatstat)
##read in the shapefiles (from Pathfinder)
data<-readShapeSpatial("SouthC1")
regions<-slot(data, "polygons")
regions<-lapply(regions, function(data){SpatialPolygons(list(data))})
windows<-lapply(regions, as.owin)
spatstat.options(checkpolygons=FALSE)
y<-as(data, "owin")
spatstat.options(checkpolygons=TRUE)
points<-readShapeSpatial("Plants1")
##Define points and polygons as objects that can be read into owin?
I suspect that I'm suffering from novice-itis and that reading different types of spatial data into a single window is not difficult. Sorry.
Side note: some of the polygons do overlap, which is why I don't want spatstat to check the polygons. I am aware that this creates complication, but that is not a pressing concern.
As an alternative you might plot your polygons first using
plot(regions)
points("Plants1")
If you use spplot from the sp package, you can use the sp.layout argument. Note the examples below combine a spatial grid with spatial points, but the exact same techniques can be used for points and polygons.
library(sp)
library(lattice)
trellis.par.set(sp.theme()) # sets bpy.colors() ramp
data(meuse)
coordinates(meuse) <- ~x+y
data(meuse.grid)
gridded(meuse.grid) <- ~x+y
spplot(meuse.grid, c("ffreq"), sp.layout = list("sp.points", meuse))
or use ggplot2 (my preference):
library(ggplot2)
# Note that you can use `fortify` to transform a SpatialPolygons* object to a data.frame
# for `ggplot2`.
pt_data = as.data.frame(meuse)
grid_data = as.data.frame(meuse.grid)
ggplot(grid_data, aes(x = x, y = y)) + geom_tile(aes(fill = ffreq)) +
geom_point(data = pt_data)
In spatstat you can plot objects on top of each other using the layered class.
In your example, regions is a list of windows (class owin). Just type
plot(as.layered(as.solist(regions)))
Here as.solist converts a vanilla list to a list of spatial objects; as.layered converts this to a layered object.
Related
I have tried using the response to this question to solve this problem but I cannot apply it in my case since I have many coordinates distributed at a global scale.
Does anyone have a way to calculate the minimum distance in km from a series of points to the nearest shore using a loop? This is a subset of the points I am using (DATA HERE)
#setwd and load directories----
setwd("your_wd")
require (ggplot2)
require (ggmap)
#build a map to plot distribution of sample sites ----
sites<-read.csv("sites.csv", header=T)
#Using GGPLOT, plot the Base World Map
mp <- NULL
mapWorld <- borders("world", colour="gray50", fill="gray50") # create a layer of borders
mp <- ggplot() + mapWorld
#Now Layer the sites on top
Lon<-sites$x
Lat<-sites$y
mp <- mp+ geom_point(aes(x=Lon, y=Lat),color="blue", size=3)
mp
Have a look at the rgeos package
library(rgeos)
gDistance(spPoints, spPolygon, byid = TRUE)
spPoints will be a SpatialPoints object holding the coordinates. spPolygon will be a SpatialPolygons objects with landmasses. See the sp package. Make sure that both object have the same projection and have a sensible projection.
I've inherited a geodatabase of polygons of lakes for which I am trying to create sampling grids on each lake. My current strategy is to export spatial data to CSV, use R to run a loop to create the grids on each lake, and then write to a new shapefile. However, here is my problem, when exporting to a CSV the WKT strings get messed up and put onto different lines. Okay, no problem, I moved on to exporting just the geometry to CSV so that I get X-Y values. When I simply plot the points they look perfect (using plot(y~x)), but the points are not in order. So, when I transform the data to a SpatialPolygon in the sp package in R using the following sequence:
XY-points -> Polygon -> Polygons -> SpatialPolygon
and then plot the SpatialPolygon I get this:
I know this is an artifact of incorrectly ordered points, because when I order the points by X and then by Y and run the same procedure here is what I get:
This is what the correct plotting is supposed to look like (X-Y data plotted with open circles):
Here is a short reproducible example of what I am trying to deal with:
library(sp)
# correct polygon
data <- data.frame(x=c(1:10, 10:1), y=c(5:1, 1:10, 10:6))
# plot(y~x, data=data)
correct.data.points <- rbind(data, data[1,]) # to close the ring for a polygon
correct.data.coords <- as.matrix(cbind(correct.data.points))
correct.data.poly <- Polygon(correct.data.coords, hole=F)
correct.data.poly <- Polygons(list(correct.data.poly), ID=0)
correct.data.poly.sp <- SpatialPolygons(list(correct.data.poly))
plot(correct.data.poly.sp)
# incorrect polygon
scr.data <- data[c(sample(1:20)),]
# plot(y~x, data=scr.data)
scr.data.points <- rbind(scr.data, scr.data[1,]) # to close the ring for a polygon
scr.data.coords <- as.matrix(cbind(scr.data.points))
scr.data.poly <- Polygon(scr.data.coords, hole=F)
scr.data.poly <- Polygons(list(scr.data.poly), ID=0)
scr.data.poly.sp <- SpatialPolygons(list(scr.data.poly))
plot(scr.data.poly.sp)
Any thoughts? Thanks for any help or insight anyone can provide. Also, for reference I am using QGIS 2.6.0 and the MMQGIS Python plugin to do the geometry exporting.
I have a spatial polygon object and a spatial points object. The latter was created from xy latlong data vectors (called latitude and longitude, respectively) in a dataframe, while the former was simply read into R directly using rgdal. My code is as follows:
boros <- readOGR(dsn = ".", "nybb")
rats <- read.csv("nycrats_missing_latlong_removed_4.2.14.csv", header = TRUE)
coordinates(rats) <- ~longitude + latitude
At this point neither spatial object is projected. If I project these objects as follows:
proj4string(boros) <- CRS("+proj=lcc")
proj4string(rats) <- CRS("+proj=lcc")
Both objects are now projected, and both will successfully map with the plot() function as follows:
plot(boros)
plot(rats)
However when I try to plot them together:
plot(boros)
plot(rats, add = TRUE)
I get the first plot only, without the rats object superimposed over boros. However, and this is the big problem, I get NO error message, so I have been unable to determine where the disconnection is between these two spatial objects being able to speak to each other. Both commands run smoothly without error or warning, yet I am left with just the single plot. And when I check the projections of each object with proj4string() I get the same projection returned for each object.
I have now spent many, many hours over several days trying various ways of creating two spatial objects whose CRS and projections match such that they can be mapped together using plot(). Incidentally, one approach I took was to create a shapefile in ArcGIS for the rats object, which worked fine to create the file. But I am still left with the same inability of the two spatial objects to work together in R. I have tried many different projections for both objects, spTransform on both objects, nothing seems to work.
Any help would be most appreciated. I have also included a dropbox link with the 2 data files I have described above:
https://www.dropbox.com/sh/x0urdo6guprnw8y/tQdfzSZ384
So, as some of the comments point out, the problem is that your data and your maps have different projections.
It looks like your map comes from the NYC Department of City Planning. The shapefile is definitely not in WGS84 (longlat), but the CRS is not included in the file (which is very disappointing by the way...). Nevertheless, there is a metadata file which indicates that the shapefile is projected as EPSG 2263.
In order to make use of this in R we need a projection string. The idiomatic way to get this in R is:
library(rgdal)
EPSG <- make_EPSG()
NY <- with(EPSG,EPSG[grepl("New York",note) & code==2263,]$prj4)
NY
# [1] "+proj=lcc +lat_1=41.03333333333333 +lat_2=40.66666666666666 +lat_0=40.16666666666666 +lon_0=-74 +x_0=300000.0000000001 +y_0=0 +datum=NAD83 +units=us-ft +no_defs"
Now we can either take your map and reproject that into WGS84, or take your data and reproject that into the map CRS.
setwd("< directory with all your files >")
data <- read.csv("nycrats_missing_latlong_removed_4.2.14.csv")
# First approach: reproject map into long-lat
wgs.84 <- "+proj=longlat +datum=WGS84"
map <- readOGR(dsn=".",layer="nybb",p4s=NY)
map.wgs84 <- spTransform(map,CRS(wgs.84))
map.wgs84.df <- fortify(map.wgs84)
library(ggplot2)
ggplot(map.wgs84.df, aes(x=long,y=lat,group=group))+
geom_path()+
geom_point(data=data, aes(x=longitude,y=latitude, group=NULL),
colour="red", alpha=0.2, size=1)+
ggtitle("Projection: WGS84 longlat")+
coord_fixed()
# Second approach: reproject data
map.df <- fortify(map)
rats <- SpatialPoints(data[,c("longitude","latitude")],proj4string=CRS(wgs.84))
rats <- spTransform(rats,CRS(NY))
rats.df <- data.frame(coordinates(rats))
ggplot(map.df, aes(x=long,y=lat,group=group))+
geom_path()+
geom_point(data=rats.df, aes(x=longitude,y=latitude, group=NULL),
colour="red", alpha=0.2, size=1)+
ggtitle("Projection: NAD83.2263")+
coord_fixed()
No rats in Central Park?
Sorry for the wall of text, but I explain the question, include the data, and provide some code :)
QUESTION:
I have some climate data that I want to plot using R. I am working with data that is on an irregular, 277x349 grid, where (x=longitude, y=latitude, z=observation). Say z is a measure of pressure (500 hPa height (m)). I tried to plot contours (or isobars) on top of a map using the package ggplot2, but I am having some trouble due to the structure of the data.
The data comes from a regular, evenly spaced out 277x349 grid on a Lambert conformal projection, and for each grid point we have the actual longitude, latitude, and pressure measurement. It is a regular grid on the projection, but if I plot the data as points on a map using the actual longitude and latitude where the observations were recorded, I get the following:
I can make it look a little nicer by translating the rightmost piece to the left (maybe this can be done with some function, but I did this manually) or by ignoring the rightmost piece. Here is the plot with the right piece translated to the left:
(An aside) Just for fun, I tried my best to re-apply the original projection. I have some of the parameters for applying the projection from the data source, but I do not know what these parameters mean. Also, I do not know how R handles projections (I did read the help files...), so this plot was produced through some trial and error:
I tried to add the contour lines using the geom_contour function in ggplot2, but it froze my R. After trying it on a very small subset of the data, I found that out after some googling that ggplot was complaining because the data was on an irregular grid. I also found out that that is the reason geom_tile was not working. I am guessing that I have to make my grid of points evenly spaced out - probably by projecting it back into the original projection (?), or by evenly spacing out my data by either sampling a regular grid (?) or by extrapolating between points (?).
My questions are:
How can I draw contours on top of the map (preferably using ggplot2) for my data?
Bonus questions:
How do I transform my data back to a regular grid on the Lambert conformal projection? The parameters of the projection according to the data file include (mpLambertParallel1F=50, mpLambertParallel2F=50, mpLambertMeridianF=253, corners, La1=1, Lo1=214.5, Lov=253). I have no idea what these are.
How do I center my maps so that one side is not clipped (like in the first map)?
How do I make the projected plot of the map look nice (without the unnecessary parts of the map hanging around)? I tried adjusting the xlim and ylim, but it seems to apply the axes limits before projecting.
DATA:
I uploaded the data as rds files on Google drive. You can read in the files using the readRDS function in R.
lat2d: The actual latitude for the observations on the 2d grid
lon2d: The actual longitude for the observations on the 2d grid
z500: The observed height (m) where pressure is 500 millibars
dat: The data arranged in a nice data frame (for ggplot2)
I am told that the data is from the North American Regional Reanalysis data base.
MY CODE (THUS FAR):
library(ggplot2)
library(ggmap)
library(maps)
library(mapdata)
library(maptools)
gpclibPermit()
library(mapproj)
lat2d <- readRDS('lat2d.rds')
lon2d <- readRDS('lon2d.rds')
z500 <- readRDS('z500.rds')
dat <- readRDS('dat.rds')
# Get the map outlines
outlines <- as.data.frame(map("world", plot = FALSE,
xlim = c(min(lon2d), max(lon2d)),
ylim = c(min(lat2d), max(lat2d)))[c("x","y")])
worldmap <-geom_path(aes(x, y), inherit.aes = FALSE,
data = outlines, alpha = 0.8, show_guide = FALSE)
# The layer for the observed variable
z500map <- geom_point(aes(x=lon, y=lat, colour=z500), data=dat)
# Plot the first map
ggplot() + z500map + worldmap
# Fix the wrapping issue
dat2 <- dat
dat2$lon <- ifelse(dat2$lon>0, dat2$lon-max(dat2$lon)+min(dat2$lon), dat2$lon)
# Remake the outlines
outlines2 <- as.data.frame(map("world", plot = FALSE,
xlim = c(max(min(dat2$lon)), max(dat2$lon)),
ylim = c(min(dat2$lat), max(dat2$lat)))[c("x","y")])
worldmap2 <- geom_path(aes(x, y), inherit.aes = FALSE,
data = outlines2, alpha = 0.8, show_guide = FALSE)
# Remake the variable layer
ggp <- ggplot(aes(x=lon, y=lat), data=dat2)
z500map2 <- geom_point(aes(colour=z500), shape=15)
# Try a projection
projection <- coord_map(projection="lambert", lat0=30, lat1=60,
orientation=c(87.5,0,255))
# Plot
# Without projection
ggp + z500map2 + worldmap2
# With projection
ggp + z500map + worldmap + projection
Thanks!
UPDATE 1
Thanks to Spacedman's suggestions, I think I have made some progress. Using the raster package, I can directly read from an netcdf file and plot the contours:
library(raster)
# Note: ncdf4 may be a pain to install on windows.
# Try installing package 'ncdf' if this doesn't work
library(ncdf4)
# band=13 corresponds to the layer of interest, the 500 millibar height (m)
r <- raster(filename, band=13)
plot(r)
contour(r, add=TRUE)
Now all I need to do is get the map outlines to show under the contours! It sounds easy, but I'm guessing that the parameters for the projection need to be inputted correctly to do things properly.
The file in netcdf format, for those that are interested.
UPDATE 2
After much sleuthing, I made some more progress. I think I have the proper PROJ4 parameters now. I also found the proper values for the bounding box (I think). At the very least, I am able to roughly plot the same area as I did in ggplot.
# From running proj +proj=lcc +lat_1=50.0 +lat_2=50.0 +units=km +lon_0=-107
# in the command line and inputting the lat/lon corners of the grid
x2 <- c(-5628.21, -5648.71, 5680.72, 5660.14)
y2 <- c( 1481.40, 10430.58,10430.62, 1481.52)
plot(x2,y2)
# Read in the data as a raster
p4 <- "+proj=lcc +lat_1=50.0 +lat_2=50.0 +units=km +lon_0=-107 +lat_0=1.0"
r <- raster(nc.file.list[1], band=13, crs=CRS(p4))
r
# For some reason the coordinate system is not set properly
projection(r) <- CRS(p4)
extent(r) <- c(range(x2), range(y2))
r
# The contour map on the original Lambert grid
plot(r)
# Project to the lon/lat
p <- projectRaster(r, crs=CRS("+proj=longlat"))
p
extent(p)
str(p)
plot(p)
contour(p, add=TRUE)
Thanks to Spacedman for his help. I will probably start a new question about overlaying shapefiles if I can't figure things out!
Ditch the maps and ggplot packages for now.
Use package:raster and package:sp. Work in the projected coordinate system where everything is nicely on a grid. Use the standard contouring functions.
For map background, get a shapefile and read into a SpatialPolygonsDataFrame.
The names of the parameters for the projection don't match up with any standard names, and I can only find them in NCL code such as this
whereas the standard projection library, PROJ.4, wants these
So I think:
p4 = "+proj=lcc +lat_1=50 +lat_2=50 +lat_0=0 +lon_0=253 +x_0=0 +y_0=0"
is a good stab at a PROJ4 string for your data.
Now if I use that string to reproject your coordinates back (using rgdal:spTransform) I get a pretty regular grid, but not quite regular enough to transform to a SpatialPixelsDataFrame. Without knowing the original regular grid or the exact parameters that NCL uses we're a bit stuck for absolute precision here. But we can blunder on a bit with a good guess - basically just take the transformed bounding box and assume a regular grid in that:
coordinates(dat)=~lon+lat
proj4string(dat)=CRS("+init=epsg:4326")
dat2=spTransform(dat,CRS(p4))
bb=bbox(dat2)
lonx=seq(bb[1,1], bb[1,2],len=277)
laty=seq(bb[2,1], bb[2,2],len=349)
r=raster(list(x=laty,y=lonx,z=md))
plot(r)
contour(r,add=TRUE)
Now if you get a shapefile of your area you can transform it to this CRS to do a country overlay... But I would definitely try and get the original coordinates first.
I am working with shapefiles in R, one is point.shp the other is a polygon.shp.
Now, I would like to intersect the points with the polygon, meaning that all the values from the polygon should be attached to the table of the point.shp.
I tried overlay() and spRbind in package sp, but nothing did what I expected them to do.
Could anyone give me a hint?
With the new sf package this is now fast and easy:
library(sf)
out <- st_intersection(points, poly)
Additional options
If you do not want all fields from the polygon added to the point feature, just call dplyr::select() on the polygon feature before:
library(magrittr)
library(dplyr)
library(sf)
poly %>%
select(column-name1, column-name2, etc.) -> poly
out <- st_intersection(points, poly)
If you encounter issues, make sure that your polygon is valid:
st_is_valid(poly)
If you see some FALSE outputs here, try to make it valid:
poly <- st_make_valid(poly)
Note that these 'valid' functions depend on a sf installation compiled with liblwgeom.
If you do overlay(pts, polys) where pts is a SpatialPointsDataFrame object and polys is a SpatialPolygonsDataFrame object then you get back a vector the same length as the points giving the row of the polygons data frame. So all you then need to do to combine the polygon data onto the points data frame is:
o = overlay(pts, polys)
pts#data = cbind(pts#data, polys[o,])
HOWEVER! If any of your points fall outside all your polygons, then overlay returns an NA, which will cause polys[o,] to fail, so either make sure all your points are inside polygons or you'll have to think of another way to assign values for points outside the polygon...
You do this in one line with point.in.poly fom spatialEco package.
library(spatialEco)
new_shape <- point.in.poly(pts, polys)
from the documentation: point.in.poly "intersects point and polygon feature classes and adds polygon attributes to points".