RandomPointsSameData

## this is my test for the Local Knowledge tool
## first use a copy of the spatial points dataframe from the ExperienceFilter
## this will be my user experience calcualtion
##set work space
setwd("~/Rindstud")
## load libraries
## multiple at once
x<- c("raster", "rgdal", "maptools",
      "sp", "dismo", "plyr", "rgeos", 
      "biomod2", "ggmap", "ggplot2")
require(x)
lapply(x, require, character.only=TRUE)
## define WGS84 common coordinate system/ projection
crs.geo <- CRS("+proj=longlat +ellps=WGS84 +datum=WGS84")  # geographical, datum WGS84
## now read the CPW shapefile
BEobs<- readOGR(dsn=".", layer = "BEcoloexact")
## now read the CPW shapefile not needed right now
cpwBE<- readOGR(dsn=".", layer = "BaldEhabitatMerge")
## using >summary(cpwBE) shows that the shapefile is projected
## in NAD1983 UTM zone 13N
## Project cpw polygon shapefile
crs.geo <- CRS("+proj=longlat +ellps=WGS84 +datum=WGS84")  # geographical, datum WGS84
cpwBE<- spTransform(cpwBE, crs.geo)
proj4string(cpwBE) <- crs.geo
## as is the State Boundary
StColo<- readOGR(dsn=".", layer = "ColoradoGCSNAD1983")
StColo<- spTransform(StColo, crs.geo)

bald<- BEobs
d<-c("recordedby", "catalognum", "collection", "month", "day", "year",
     "decimallon", "decimallat", "locality", "species", "gbifid")
BEobspoints<- bald[d]

bald.df<- data.frame(BEobspoints)
##double check it is a 'data frame'
## to see internal structure: str(bald.df)
## must remove rows with missing values
str(bald.df)  ## note number of records (observations)
complete.cases(bald.df) ## this produces a logical vector for complete rows 'TRUE'
BEcomplete<- bald.df[complete.cases(bald.df), ]
## now back to spatial points data frame
coordinates(BEcomplete) = c("decimallon", "decimallat")
crs.geo <- CRS("+proj=longlat +ellps=WGS84 +datum=WGS84")  # geographical, datum WGS84
proj4string(BEcomplete) <- crs.geo  # define projection system of our data

## now generate random points within the State Boundary (as bbox)
rsam<- spsample(StColo, n=100, type="random")
nsamp= nrow(BEcomplete)
oname<- as.data.frame(BEcomplete$recordedby)
urs<- unique(oname)
numIDs = nrow(urs)
randsamp= spsample(StColo, n=nsamp, type="random")
## generate random Observer ID vector
## Use same 'recordedby' values for OID column Observer ID
BEdata <- as.data.frame(BEcomplete@data)
randspdf <- SpatialPointsDataFrame(coords=randsamp, data = BEdata)