I am new to R programming, I have a csv/excel file of 20 towns in a country which contains the in the below format,
Towns UTM Cordinates UTM Cordinaetes
xxxxxxxx 1377777 249514
yyyyyyyy 142145 228942
I am unable to plot them into a map, Does anyone have any idea of how t plot these UTM Cordinates.
Is it possible to plot towns in R programming with UTM? If so can anyone help me out here.
I have the shape file for the country with me as well. But I am not sure how to process.
myfilepath <- file.choose()
Cordinates<-read.csv(myfilepath,header=TRUE)
Cordinates
str(Cordinates)
library(rgdal)
library(sp)
library(data.table)
library(ggplot2)
library(tibble)
myfilepath <- file.choose()
Shapefile<-readOGR(myfilepath)
plot(Shapefile)
ggmap(Shapefile)+geom_point(aes(x=Easting,y=Northing,col=Cordinates))
Any help would be appreciated.
Here is a sf-solution, making use of all the hard work from #Dave2e to find the correct coordinate system used...
#convert to simple feature
library( sf )
mysf <- sf::st_as_sf( mydata, coords = c("Easting", "Northing"), crs = 29902)
#plot for visual inspection
mapview::mapview(mysf)
The trick is to determine the grid system used. After much searching the code for standard Republic of Ireland grid is epsg:29902
The first step is to transform the Irish grid coordinates to a standard latitude and longitude. This is accomplished with the "rgdal" library.
library(rgdal)
points <- read.table(header=TRUE, text = "Towns Easting Northing
Belclare 137777 249514
Carnmore 142145 228942")
#Pull out the location columns and rename
point <- points[,2:3]
names(point) <-c('x', 'y')
#convert to corrdinates and define initial coordinates systems
coordinates(point) <- c('x', 'y')
proj4string(point)=CRS("+init=epsg:29902") #29903 is the grid for Ireland
#Transform the Ireland's grid to longitude & latitude
coord<-spTransform(point,CRS("+init=epsg:4326"))
coord
This will transform your list of coordinates, please search this site on how to plot to a map. There are many options available.
Related
i'm new in the R world and i'm trying to do a species distribution model, but when i plot my result, the points stay out from my map, i tried to change CRS but i didn't solve my problem, now i'll go to show you my code
library(dismo)
library(raster)
library(dplyr)
library(rnaturalearth)
Here i downloaded my species from gbif
gbif("Miniopterus", "schreibersii" , download=F)
minio<- gbif("Miniopterus", "schreibersii" , download=T) #you need 2 min approximately
i saw the basis of record and then i selected 2 different types
table(minio$basisOfRecord)
#Filter data minio----
minio<- minio%>%
filter(!is.na(lat))%>%
filter(!is.na(lon))%>%
filter(year>1980)%>%
filter(basisOfRecord %in% c("HUMAN_OBSERVATION", "OBSERVATION"))
class(minio)
nrow(minio)
i selected only longitude and latitude
miniogeo<-minio%>%
select(lon,lat)
head(miniogeo)
miniogeo$species<-1
head(miniogeo)
nrow(miniogeo)
And i created the coordinates and set the crs
coordinates(miniogeo) <-c("lon","lat")
crs(miniogeo) <- "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs"
proj4string(miniogeo) <- CRS("+init=epsg:4326")
Here start problems for me, i tried a lot of type of function for create a map but this is the most efficient (the most efficient of those functions I have found). I need to have a zoom of spain and portugal, and i need to exclude "Africa".
Europe <- ne_countries(scale="medium", type="map_units", returnclass="sf", continent="Europe")
Worldclim<-raster::getData('worldclim', var='bio', res=2.5)
Europe <- Europe %>%
dplyr::select(geometry,name_long) %>%
filter(name_long!='Russian Federation')
plot(Worldclim[[1]]) #or plot(worldclim$bio1)
plot(st_geometry(Europe))
points(miniogeo, cex=0.1)
envData<-crop(Worldclim, Europe)
EuropePred <- mask(envData, Europe) #we create a new raster without NA value
And here i plotted my points but, as you can see, my points went out of my map
plot(EuropePred[[1]]) #example
points(miniogeo, cex=0.2)
then i tried to do a zoom to Spain and Portugal.
extSpnPrt<-extent(c(-11,10,35,56))
miniogeo<-crop(miniogeo,extSpnPrt)
SpainPort<-crop(EuropePred,extSpnPrt)
plot(SpainPort$bio2)
points(miniogeo, cex=0.1)
There is someone that can understand my problem? i'm really really sorry, i tried a lot of time for undestand better but my level in R is so basics for now.
I say thank you to all that dedicate the time for read this. I hope you have a good day
This is the result of my map with only geometry and with worldclim data
enter image description here
One way to create a SpatialPointsDataFrame from your data.frame minio is:
coordinates(minio) <- ~ lon + lat
crs(minio) <- "+proj=longlat"
This is NOT correct:
coordinates(minio) <- c("lon", "lat")
Result:
plot(minio, cex=.5, col="red")
lines(as(Europe, "Spatial"))
I am trying to create a script that will generate a 2d topographic or contour map for a given set of coordinates. My goal is something similar to what is produced by
contour(volcano)
but for any location set by the user. This has proved surprisingly challenging! I have tried:
library(elevatr)
library(tidyr)
# Generate a data frame of lat/long coordinates.
ex.df <- data.frame(x=seq(from=-73, to=-71, length.out=10),
y=seq(from=41, to=45, length.out=10))
# Specify projection.
prj_dd <- "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs"
# Use elevatr package to get elevation data for each point.
df.sp <- get_elev_point(ex.df, prj = prj_dd, src = "epqs")
# Convert from spatial to regular data frame, remove extra column.
# Use tidyr to convert to lat x lon table with elevation as fill.
# Sorry for the terrible code, I know this is sloppy.
df <- as.data.frame(df.sp)
df$elev_units <- NULL
df.w <- df %>% spread(y, elevation)
df.w <- as.matrix(df.w)
This creates a matrix similar to the volcano dataset but filled with NAs except for the 10 lat/lon pairs with elevation data. contour can handle NAs, but the result of contour(df.w) has only a single tiny line on it. I'm not sure where to go from here. Do I simply need more points? Thanks in advance for any help--I'm pretty new to R and I think I've bitten off more than I can chew with this project.
Sorry for delay in responding. I suppose I need to check SO for elevatr questions!
I would use elevatr::get_elev_raster(), which returns a raster object which can be plotted directly with raster::contour().
Code example below grabs a smaller area and at a pretty coarse resolution. Resultant contour looks decent though.
library(elevatr)
library(raster)
# Generate a data frame of lat/long coordinates.
ex.df <- data.frame(x=seq(from=-73, to=-72.5, length.out=10),
y=seq(from=41, to=41.5, length.out=10))
# Specify projection.
prj_dd <- "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs"
# Use elevatr package to get elevation data for each point.
elev <- get_elev_raster(ex.df, prj = prj_dd, z = 10, clip = "bbox")
raster::contour(elev)
If it is a requirement to use graphic::contour(), you'll need to convert the raster object to a matrix first with raster::as.matrix(elev). That flips the coords though and I haven't spent enough time to try and get that part figured out... Hopefully the raster solution works for you.
I am facing problem in creating map. I have downloaded a shape file from-
location link: "https://data.humdata.org/dataset/80920682-bbb5-421e-b7ac-f89b7b640a5c/resource/c545d196-bc2c-44ed-9028-316ab080a41c"
zip file link: https://data.humdata.org/dataset/80920682-bbb5-421e-b7ac-f89b7b640a5c/resource/c545d196-bc2c-44ed-9028-316ab080a41c/download/bgd_poi_healthfacilities_lged.zip
After extracting the data I have found a shape file. I am trying to plot this shape file in google map using R code. But It is not showing anything?
library(maptools)
library(ggmap)
counties.mpl <- readShapePoints("bgd_poi_healthfacilities_lged")
#Coordinates looks like:
counties.mpl#coords
coords.x1 coords.x2
0 531533.8 2524464
1 531004.7 2531410
2 533228.5 2525061
3 531723.1 2488972
4 532347.8 2492098
5 518104.8 2520361
#map code:
mymap <- get_map(location="Bangladesh", zoom=6)
ggmap(mymap)+
geom_point(data=counties.mpl#coords,
aes(x=counties.mpl#coords[,1], y=counties.mpl#coords[,2]))
Could anybody please help me to solve this? Thanks in advance.
As the others have noted, your shapefile uses a different coordinate system, & you'll need to transform it to lat / lon before the geom_point() layer can sit nicely over mymap.
Your shapefile's .prj file begins with:
PROJCS["BangladeshTM WGS1984",GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984", ...
The link here explains what each part means, but for our purpose, you just need to know that the shapefile's projected coordinate system is "BangladeshTM WGS1984", i.e. Bangladesh Transverse Mercator, coded as EPSG:3106.
The typical lat / lon coordinate system that ggmap() expects is WGS 84, coded as EPSG:4326.
TLDR: Convert your data's projection from EPSG:3106 to EPSG:4326, and plot accordingly.
counties.mpl <- readShapePoints("bgd_poi_healthfacilities_lged")
# define current projection
proj4string(counties.mpl) <- CRS("+init=epsg:3106") # Bangladesh Transverse Mercator system
# remap to lat / long projection
counties.mpl.remapped <- spTransform(counties.mpl, CRS("+init=epsg:4326"))
# extract the coordinates as a data frame.
df <- as.data.frame(counties.mpl.remapped#coords)
colnames(df) <- c("lon", "lat")
# plot results
mymap <- get_map(location="Bangladesh", zoom=6)
ggmap(mymap) +
geom_point(data = df)
Rworldmap looks like exactly what I need for mapping climate data, but I'm having a problem lining up the base map with the climate data. What I am mapping is ocean temperature data from JAMSTEC for August, 2015 from here:
http://www.jamstec.go.jp/ARGO/argo_web/ancient/MapQ/Mapdataset_e.html
The dataset name is TS_201508_GLB.nc. The R script I'm using is below. The country outlines are fine, but the data is for the oceans only and the data does not show in the oceans it is offset somehow. Can you tell me how to align the data to the map?
I've read lots of articles but I cannot tell how to align the two. The data I have is longitude and latitude. South latitude is negative and west longitude is negative, I don't see how they could be confused. How is the map shown, is there some sort of special convention for the lat/longs?
Thanks for any help you can provide.
The code:
library(RNetCDF)
library(sp)
library(rworldmap)
library(rgeos)
library(RColorBrewer)
library (classInt)
library(grid)
library(spam)
library(maps)
library(maptools)
library(fields)
library(methods)
library(rgdal)
library(rworldxtra)
fname <- "G:/Climate_Change/Ocean_Warming/MOAA_GPV_Jamstec_Temperature/TS_201508_GLB.nc"
moaa <- open.nc(fname)
# moaa
print.nc(moaa)
file.inq.nc(moaa)
#TOI is the temperature array extracted from the NCDF file
TOI = var.get.nc(moaa,"TOI",start=c(1,1,1),count=c(360,132,25))
TOI[1,1,1]
Long = var.get.nc(moaa,"LONGITUDE")
Lat = var.get.nc(moaa, "LATITUDE")
Pres = var.get.nc(moaa,"PRES")
# create grid
offset=c(-179.5,-60.50)
cellsize = c(abs(Long[1]-Long[2]),abs(Lat[1]-Lat[2]))
cells.dim = c(dim(Long), dim(Lat))
# create gt
gt <- GridTopology(cellcentre.offset=offset,cellsize=cellsize,cells.dim=cells.dim)
# create map window
mapDevice()
# Create a color pallette
colourPalette=c('blue','lightblue','white',brewer.pal(9,'YlOrRd'))
# Values at 2000 decibar for August 2015
ncMatrix <- TOI[,,25]
# Gridvalues
gridVals <-data.frame(att=as.vector(ncMatrix))
# create a spatialGridDataFrame
sGDF <-SpatialGridDataFrame(gt,data=gridVals)
# Vector to classify data
catMethod=seq(from=0,to=4,by=.33)
# plotting the map and getting params for legend
mapParams <- mapGriddedData( sGDF, nameColumnToPlot='att',catMethod=catMethod,colourPalette=colourPalette,addLegend=FALSE)
I finally figured it out. rworldmap wants the data organized from the upper left of the map(Northwest corner), that is Long = -180, Lat=90. The NetCDF data starts at Long=0 and Lat=-90(the middle of the map and south edge). So we have to reverse the values in the North-South direction:
#
# Flip the Latitude values so south is last
ncMatrix2 <- ncMatrix[,dim(Lat):1]
Then switch the values for east longitude and west longitude:
#
#Longitude values need to be from -180 to 0 then 0 to 180
# So we divide into East and West, then recombine with rbind
East_Long_values <-ncMatrix2[1:180,]
West_Long_Values <-ncMatrix2[181:360,]
ncMatrix3 <- rbind(West_Long_Values,East_Long_values)
Then everything else works.
I have the longitude and latitude of 5449 trees in NYC, as well as a shapefile for 55 different Neighborhood Tabulation Areas (NTAs). Each NTA has a unique NTACode in the shapefile, and I need to append a third column to the long/lat table telling me which NTA (if any) each tree falls under.
I've made some progress already using other point-in-polygon threads on stackoverflow, especially this one that looks at multiple polygons, but I'm still getting errors when trying to use gContains and don't know how I could check/label each tree for different polygons (I'm guessing some sort of sapply or for loop?).
Below is my code. Data/shapefiles can be found here: http://bit.ly/1BMJubM
library(rgdal)
library(rgeos)
library(ggplot2)
#import data
setwd("< path here >")
xy <- read.csv("lonlat.csv")
#import shapefile
map <- readOGR(dsn="CPI_Zones-NTA", layer="CPI_Zones-NTA", p4s="+init=epsg:25832")
map <- spTransform(map, CRS("+proj=longlat +datum=WGS84"))
#generate the polygons, though this doesn't seem to be generating all of the NTAs
nPolys <- sapply(map#polygons, function(x)length(x#Polygons))
region <- map[which(nPolys==max(nPolys)),]
plot(region, col="lightgreen")
#setting the region and points
region.df <- fortify(region)
points <- data.frame(long=xy$INTPTLON10,
lat =xy$INTPTLAT10,
id =c(1:5449),
stringsAsFactors=F)
#drawing the points / polygon overlay; currently only the points are appearing
ggplot(region.df, aes(x=long,y=lat,group=group))+
geom_polygon(fill="lightgreen")+
geom_path(colour="grey50")+
geom_point(data=points,aes(x=long,y=lat,group=NULL, color=id), size=1)+
xlim(-74.25, -73.7)+
ylim(40.5, 40.92)+
coord_fixed()
#this should check whether each tree falls into **any** of the NTAs, but I need it to specifically return **which** NTA
sapply(1:5449,function(i)
list(id=points[i,]$id, gContains(region,SpatialPoints(points[i,1:2],proj4string=CRS(proj4string(region))))))
#this is something I tried earlier to see if writing a new column using the over() function could work, but I ended up with a column of NAs
pts = SpatialPoints(xy)
nyc <- readShapeSpatial("< path to shapefile here >")
xy$nrow=over(pts,SpatialPolygons(nyc#polygons), returnlist=TRUE)
The NTAs we're checking for are these ones (visualized in GIS): http://bit.ly/1A3jEcE
Try simply:
ShapeFile <- readShapeSpatial("Shapefile.shp")
points <- data.frame(long=xy$INTPTLON10,
lat =xy$INTPTLAT10,
stringsAsFactors=F)
dimnames(points)[[1]] <- seq(1, length(xy$INTPTLON10), 1)
points <- SpatialPoints(points)
df <- over(points, ShapeFile)
I omitted transformation of shapefile because this is not the main subject here.