Overlay decimal coordinates (New Jersey) on NAD83 Stateplane polygon in R - r

I am trying to make a plot with points (decimal coordinates in New Jersey) on polyline shapefile with projection NAD 83 Stateplane (feet) (New Jersey). How can I do it? So far, I could plot the points and the shapefile separately but cannot overlay.
Plotted the shapefile using the following code:
orgListLayers("Counties.shp") # Shows the available layers for the shpaefile "Counties:
shape=readOGR("Counties.shp", layer="Counties") # Load the layer of the shapefile
plot(shape) # Plots the shapefile
Plotted points (vectors are lat1,long1) using the following code after transforming the points into Stateplane in ArcGIS:
dpts <- as.data.frame(cbind(long1,lat1))
plot(dpts2)
How can I overlay these points on the polyline shapefile?
Ultimately, I will have multiple sets of points which I want to plot on the shapefile as circles whose size would be dependent on values associated with the points. e.g. if each point represents a town, I want a bigger circle for a town having higher population.

You didn't provide any data, so this may be a partial answer.
Using the ggplot package it is easy to create layered maps. This map, of universities in NJ, was created with the code snippet that follows. It demonstrates plotting points and boundaries on the same map, and sizing the points based on a datum of the university (here, enrollment).
library(ggplot2)
library(rgdal)
setwd("<directory containing your data and maps")
states <- readOGR(dsn=".",layer="tl_2013_us_state")
nj.map <- states[states$NAME=="New Jersey",]
univ.map <- readOGR(dsn=".",layer="NJ_College_Univ_NAD83njsp")
nj.df <- fortify(nj.map)
univ.df <- univ.map#data
univ.df$ENROLL <- as.numeric(as.character(univ.df$ENROLL))
# create the layers
ggMap <- ggplot(nj.df)
ggMap <- ggMap + geom_path(aes(x=long,y=lat, group=group)) # NJ boundary
ggMap <- ggMap + geom_point(data=univ.df, aes(x=X, y=Y, size=ENROLL),color="red", alpha=0.7)
ggMap <- ggMap + coord_fixed()
ggMap <- ggMap + scale_size_continuous(breaks=c(5000,10000,15000,20000,25000,30000), range=c(0,10))
# render the map
ggMap
The TIGER/Line shapefile of US States was obtained here. The NJ Universities were obtained here.
Explanation:
The call to ggplot(...) defines the NJ map as the default dataset.
The call to geom_path(...) adds a layer to draw the NJ boundary.
The call to geom_point(...) adds a point layer locating the universities, with point size proportional to enrollment.
The call to coord_fixed(...) ensures that the map will not be distorted.
The call to scale_size_continuous(...) establishes breaks for the legend labels.

Related

How to extract New Mexico state from USA boundaries package in R

I am trying to make a Tmap with two points but I am having some issues.
`library(tmap)
library(maps)
library(sf)
library(USAboundaries)
#Open soil pit location data
data<-read.csv("SoilPit_Locations___.csv")
#Converting locations to an sf object
points<-st_as_sf(data,coords=c("Longitude","Latitude"),crs=4326)
#Set up map mode as view for an interactive map
tmap_mode("plot")
#Set up style as natural to view topography
tmap_style("natural")
#Map us borders and add points for locations as dots
#tm_shape(us) + tm_borders("black", lwd = .5) +
m=tm_shape(points)+tm_dots()
m
tmap_save(m, "my_mapp.png")`
The issue I am having is that when I try run my code the two points are on the corners of the map, I would like to plot them on the New Mexico state map so they can look nicer.
I have tried extracting the NM state boundary from the world map but I have been unsuccessful.
If you do:
library(USAboundariesData)
then you get states_contemporary_lores (amongst other things) which are the USA state boundaries, so you can subset NM like this:
nm = states_contemporary_lores[
states_contemporary_lores$state_abbr=="NM",
]
and then nm is an sf object which is just New Mexico. You can map this in tmap using tm_shape(nm) + tm_borders() and then adding whatever other layers you have.

Specific number of points within each polygon of a shape in R

I have a shapefile with around 400 polygons of different sizes. I have been trying to create a random point layer with a specific number of points within each of the 400 polygons.
I tried with spsample function but it generates a random layer of points that does not respect each polygon (it is a general random point layers of the total shapefile).
As an example:
Shape file
Download: biogeo.ucdavis.edu/data/diva/adm/USA_adm.zip
it is the file "USA_adm1"
This shapefile contains 52 polygons. I am looking to put a specific number of random points inside every one of the 52 polygons. For example a random distribution of 100 points inside each of the states.
I hope you can help me.
thank you
From the sf package using st_sample on an sf object:
library(sf)
library(ggplot2)
# using data included with sf package,
# it contains 100 polygons
nc <- st_read(system.file("shape/nc.shp", package="sf"))
# st_sample needs a vector telling it how many samples for each polygon
# here we're using 3 for each polygon
samples_per_polygon <- rep(3, nrow(nc))
samples <- st_sample(nc, samples_per_polygon)
ggplot() + geom_sf(data = nc) + geom_sf(data = samples)

geom_sf does not use geometry coordinates in axes but plots correct shape of polygon?

My overall aim is to combine multiple shape files (polygons of river sub-basins from within a large river basin) into one file and plot as a map. This new combined file will later combine with variable data e.g.(rainfall) and plot by aes().
My problem is:
ggplot()+geom_sf() plots the correct shapes of the polygons but doesn't have the correct co-ordinates on the axes - it doesn't use the values given in the geometry column on the axes.
My thoughts on what is wrong, but I'm not sure how to correct:
The shape file read in has geometry in 'long' 'lat' (crs= 4326) but the crs is saying the coordinates are in UTM Zone 48N WGS84 (crs=32648). If I try and force the crs to 4326 the coordinate values change as if the conversion formula is trying to correct them.
geom_sf and coord_sf are doing something that I don't understand!
.
library(sp)
library(raster)
library(ggplot2)
library(sf)
library(ggsf)
library(rgdal)
library(plyr)
library(dplyr)
library(purrr)
setwd("/Users/.../Sub_Basin_Outlines_withSdata/")
list.files('/Users/.../Sub_Basin_Outlines_withSdata/', pattern='\\.shp$')
Read in individual polygon shape files from folder. Combine with ID.
bangsai <- st_read("./without_S_data/", "Nam Bang Sai")
BasinID <- "BGS"
bangsai <- cbind(bangsai,BasinID)
ing <- st_read("./without_S_data/", "Nam Ing Outline")
BasinID <- "ING"
The two individual shape files import as simple features, see image of R code
Combine the individual sub-basin polygon shape files into one shapefile with multiple features.
all_sub_basins <- rbind(bangsai,ing)
The image shows the values of the coordinates of the polygons/features in all_sub_basins$geometry. They are long lat format yet the proj4sting suggests UTM?
Plot the all_sub_basins simple feature shapefile in ggplot
subbasins <- ggplot()+
geom_sf(data=all_sub_basins, colour="red", fill=NA)
subbasins
The result is a correctly plotted shape file with multiple features (there are more polygons in this image than read in above). However the axes are incorrect (nonsense values) and are not plotting the same values as in the geometry field.
If I add in coord_sf and confirm the crs:
subbasins <- ggplot() +
geom_sf(data=all_sub_basins, colour="red", fill=NA)
coord_sf(datum=st_crs(32648), xlim = c(94,110), ylim = c(9,34))
subbasins
Then I get the Correct axes values but not as coordinates with N and E. It seems as if the geometry isn't recognised as coordinates, just as forced numbers?
I don't mind if the coordinates are UTM Zone 48N or lat long. Could I fix it in any of these ways? If so, how do I achieve that?
Change the shape file crs without changing the values in the geometry column so geom_sf would know to plot the correct axes text.
Extract the geometry from the shape file into a two column .csv file with long and lat columns. Convert csv into a sf and create my own shape file with correct crs.
Last resort, leave the plot as it is and replace new axes text manually.
Any help is much appreciated!

Calculating distance to nearest shore from multiple GPS coordinates

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.

How to plot contours on a map with ggplot2 when data is on an irregular grid?

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.

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