#### plotGoogleMaps
setwd("/Users/hughsturrock/Documents/Work/teaching/Spatial Epi UCSF")
library(plotGoogleMaps)
library(maptools)
library(RColorBrewer)
library(wesanderson)

# Data preparation
# Point data
data(meuse)
head(meuse)

# The meuse point data set consists of 155 samples of top soil heavy metal concentrations (ppm), 
# along with a number of soil and landscape variables. 
# The samples were collected in a flood plain of the river Meuse, near the village Stein (Germany).

# Basic visualization
plot(meuse$x,meuse$y)
plot(meuse$x,meuse$y,cex=meuse$cadmium/5) # point size proportional to cadmium

# Convert to a SpatialPointsDataFrame
# For more info see https://cran.r-project.org/web/packages/sp/vignettes/intro_sp.pdf
coordinates(meuse)<-~x+y # convert to SPDF

# adding Coordinate Referent Sys.
proj4string(meuse) <- CRS('+init=epsg:28992')
# For nice overview of CRS 'https://www.nceas.ucsb.edu/~frazier/RSpatialGuides/OverviewCoordinateReferenceSystems.pdf'

# Create web map of Point data
m=plotGoogleMaps(meuse,filename='myMap1.htm') # saves an html file you can open later and share

# change some parameters
m<-plotGoogleMaps(meuse,
                  filename='myMap2.htm',
                  mapTypeId='SATELLITE',
                  zcol='copper',
                  layerName = 'MEUSE POINTS')

# Check out http://kml4earth.appspot.com/icons.html for other icon options

# Creat a bubble plot
m<-bubbleGoogleMaps(meuse,zcol='zinc', max.radius = 80,
                    filename='myMap3.htm')

# change color palette
m<-bubbleGoogleMaps(meuse,zcol='zinc', max.radius = 80,
                    filename='myMap3.htm',colPalette=heat.colors(5))

# Plot pie charts with multivariate data
m<-segmentGoogleMaps(meuse, zcol=c('zinc','lead','copper'),
                     mapTypeId='ROADMAP', filename='myMap4.htm')

# Check out ?plotGoogleMaps for more options to play with


# Plot raster data
data(meuse.grid) # for a desciptoin see http://www.dpi.inpe.br/gilberto/tutorials/software/R-contrib/sp/html/meuse.grid.html
coordinates(meuse.grid)<-c('x','y')
meuse.grid<-as(meuse.grid,'SpatialPixelsDataFrame')
proj4string(meuse.grid) <- CRS('+init=epsg:28992')
plotGoogleMaps(meuse.grid, zcol='dist', mapTypeId='ROADMAP') # distance to meuse river

# Plot lines
# Convert to SpatialPixelsDataFrame
im<-as.image.SpatialGridDataFrame(meuse.grid['dist'])

# Us eht ContourLines to Spatial Lines dataframe function in maptools to 
# generate contour lines you can plot 
cl<-ContourLines2SLDF(contourLines(im))
proj4string(cl) <- CRS('+init=epsg:28992')
mapMeuseCl<- plotGoogleMaps(cl, zcol='level', strokeWeight=1:9 ,
                            filename='myMap5.htm', mapTypeId='ROADMAP')


### plotting polygon data
# For plotting spatial polygon data frame is used shapefile provided by maptools package. This is
# for the 100 counties of North Carolina, and includes counts of numbers of live births (also nonwhite
#live births) and numbers of sudden infant deaths, for the July 1, 1974 to June 30, 1978 and
# July 1, 1979 to June 30, 1984 periods
nc <- readShapeSpatial(system.file("shapes/sids.shp",
                                    package="maptools")[1], 
                                    proj4string=CRS("+init=epsg:4326"))

# Plot numbers of SIDs between 1979 and 1984
plotGoogleMaps(nc,
               zcol="SID79",
               filename='MyMap6.htm',
               mapTypeId='TERRAIN',
               colPalette= wes_palette('Royal1')[c(1,3,4,2)],
               strokeColor="white")



### Combining different data
# To plot multiple data types, the function should contain
# argument add=TRUE. The next plot should have the name of previous map the argument
# previousMap = <name of saved map produced by functions from
# plotGoogleMaps package> .
m1<- plotGoogleMaps(cl, zcol='level', strokeWeight=1:9 , add=
                      TRUE)
m2<-bubbleGoogleMaps(meuse,zcol='zinc', add=T,
                     colPalette= brewer.pal(5,"Accent"),
                     max.radius = 80,previousMap= m1)
m3<- plotGoogleMaps(meuse.grid, zcol='dist',colPalette=
                      brewer.pal(6,"Oranges"),previousMap= m2,
                    filename='combination.htm')


# Export as kml file for Google Earth
library(plotKML)
plotKML(nc["SID79"]) # saves a similarly named kml file in your working directory.

# Or change colors
plotKML(nc["SID79"],colour_scale = heat.colors(5))

# More resources
# http://pakillo.github.io/R-GIS-tutorial/