pmur002

## Main code based on https://www.r-spatial.org//r/2018/10/25/ggplot2-sf-3.html

library("ggplot2")
theme_set(theme_bw())
## Requires up to date gdal
## (e.g., from https://launchpad.net/~ubuntugis/+archive/ubuntu/ppa)
library("sf")

library("rworldmap")
library("rworldxtra")

Pakillo

polygonizer <- function(x, outshape=NULL, gdalformat = 'ESRI Shapefile', 
                        pypath=NULL, readpoly=TRUE, quietish=TRUE) {
  # x: an R Raster layer, or the file path to a raster file recognised by GDAL
  # outshape: the path to the output shapefile (if NULL, a temporary file will be created)
  # gdalformat: the desired OGR vector format
  # pypath: the path to gdal_polygonize.py (if NULL, an attempt will be made to determine the location
  # readpoly: should the polygon shapefile be read back into R, and returned by this function? (logical)
  # quietish: should (some) messages be suppressed? (logical)
  if (isTRUE(readpoly)) require(rgdal)
  if (is.null(pypath)) {

oscarperpinan

library(sp)
library(ggmap)
## latticeExtra must be loaded after ggmap because both ggplot2 and
## latticeExtra define a 'layer' function. We need the definition from
## latticeExtra.
library(latticeExtra)
## We only need maptools to get an example
library(maptools)
SIDS <- readShapePoly(system.file("shapes/sids.shp", package="maptools")[1],
                      IDvar="FIPSNO",

oscarperpinan

library(sp)
library(ggmap)
library(RColorBrewer)

## Get data from OpenPV. Open this URL:
## https://openpv.nrel.gov/search?&state=CA&minsize=100&maxsize=30600&pagenum=1&nppage=25
## and export the results as a CSV file named "californiaOpenPV.csv"
caPV <- read.csv('californiaOpenPV.csv')
## Longitude and Latitude are the names of columns where the spatial information is stored.
## With the coordinates<- method caPV is now an SpatialPointsDataFrame