# this only has to be done once (as the root ubuntu user)
# get 'gdal' needed for 'rgdal' and 'rgeos'
# http://robinlovelace.net/r/2013/11/26/installing-rgdal-on-ubuntu.html
# aptitude better package manager than apt-get for gdal

# sudo apt-get update
# sudo apt-get install aptitude
# sudo aptitude install libgdal-dev 
# sudo aptitude install libproj-dev

# choose rstudio as source package repository
# options(repos = c(CRAN = "http://cran.rstudio.com"));

# install.packages('deldir')
# install.packages('dplyr')
# install.packages('jsonlite',dependencies=TRUE)
# install.packages('maptools')
# install.packages('rgeos')
# install.packages('rgdal',dependencies=TRUE)
# install.packages('leaflet')

library(dplyr)
library(jsonlite)
library(maptools); gpclibPermit()
library(deldir)
library(rgeos)
library(rgdal)
library(leaflet)

sessionInfo()

# an [r] command to tell linux shell to 'download' a file from a url
# system() --> wget --> url

# download
# system('wget http://www2.census.gov/geo/tiger/TIGER2015/TRACT/tl_2015_06_tract.zip')

# unzip
# system('mkdir -p ~/notebooks/gis_demog_misc')
# system('unzip /notebooks/gis_demog_misc/tl_2015_06_tract.zip -d ~/notebooks/gis_demog_misc/tl_2015_06_tract')

# to bring up r's help manual, use syntax "?function_name"
?readShapeSpatial

#################
# shapefile - tract
#################

# library(maptools)
# directory location of shapefiles
shapefile_location = '~/notebooks/gis_demog_misc/tl_2015_06_tract/'
ca_shp <- readShapeSpatial(paste0(shapefile_location,'tl_2015_06_tract.shp'))

# specify projection
proj4string(ca_shp) <- "+proj=longlat +datum=WGS84"
plot(ca_shp)

# notice how long it takes, shapefiles are "bloated"

names(ca_shp)

# just subset to la county
la_county = subset(ca_shp, ca_shp$COUNTYFP == '037')
plot(la_county)

#################
# shapefile - tract
#################

# plot(la_county)
# locator to interactively choose points of convex hull for custom bbox

# Click points to draw your own custom boundary then hit keyboard 'esc' to finalize
# chull_locat = locator()
# plot(chull(chull_locat))

# use the new custom boundary points to crop out ignored areas

# library(spatstat)
# bbox_cust = bounding.box.xy(chull_locat)
# bb_poly <- as((bbox_cust), "SpatialPolygons")
# proj4string(bb_poly) <- CRS(proj4string(la_county))

# library(raster)
# la_county_zoom = raster::crop(la_county,bb_poly)
# plot(la_county_zoom)

# save(la_county_zoom,file='/notebooks/gis_demog_misc/la_county_zoom_tvmagic.RData')

load("~/notebooks/gis_demog_misc/la_county_zoom_tvmagic.RData")
plot(la_county_zoom)

####################
# get la county bdry
# ?rgeos::gUnionCascaded to dissolve interior polygons
####################

# plot(la_county_zoom)
# library(rgeos)
la_county_bdry = rgeos::gUnionCascaded(la_county_zoom)
plot(la_county_bdry)

# Plug in your API Key (Census API)
api_key_census = 'foo_your_key'  # blank out

base_url_census = 'http://api.census.gov/data/2015/pdb/tract?get='

query = paste0(base_url_census,
               'Tot_Population_CEN_2010&for=tract:*&in=state:06+county:037&key=',
               api_key_census)

# library(jsonlite)
dat_raw_census = fromJSON(query)
str(dat_raw_census)

# use first row of matrix as variables' name (column names) for data frame
colnames(dat_raw_census) = dat_raw_census[1,]
dat_raw_census = dat_raw_census[-1,]
str(dat_raw_census)
head(dat_raw_census)

dat_census = data.frame(Tot_Population_CEN_2010 = as.numeric(dat_raw_census[,1]),
  state = as.character(dat_raw_census[,2]),
  county = as.character(dat_raw_census[,3]),
  tract = as.character(dat_raw_census[,4]),
  stringsAsFactors=FALSE
)

head(dat_census)

# Plug in your API Key (Google Radar API)
api_key_radar = 'foo_your_key'  # blank out

api_key=api_key_radar
keyword='in-n-out'
types='food'
radius='50000'  # 50000 meters (about 31 miles)

# location='34.072422,-118.0776237'  # 'lat,lng' order # rosemead  # landmark3

# Google's API documentation tells us to treat 
# each gps point as a text string of "latitude,longitude"

# Since we're going to 'glue' the string into a website URL

landmark1 = "33.78619,-117.8751"
landmark2 = "33.92515,-117.5191"
landmark3 = "34.20306,-118.3817"

class(landmark1)

# result example for 1 landmark

location = landmark1

call_url = paste0('https://maps.googleapis.com/maps/api/place/radarsearch/json?',
                  'location=',location,  # 'lat,lng' order
                  '&radius=',radius,
                  '&types=',types,
                  '&keyword=',keyword,
                  '&key=',api_key
                 )


# library(jsonlite)
# ?jsonlite

dat_raw = fromJSON(call_url)
str(dat_raw)

dat_raw$results$geometry$location$lat
dat_raw$results$geometry$location$lng

plot(y=dat_raw$results$geometry$location$lat,
     x=dat_raw$results$geometry$location$lng
    )

# wrap into function so we can iterate over the 3 landmarks

get_gps_custom_func = function(your_landmark){
    location = your_landmark
    call_url = paste0('https://maps.googleapis.com/maps/api/place/radarsearch/json?',
                      'location=',location,  # 'lat,lng' order
                      '&radius=',radius,
                      '&types=',types,
                      '&keyword=',keyword,
                      '&key=',api_key
                     )
    
    library(jsonlite)
    # ?jsonlite
    
    dat_raw = fromJSON(call_url)
    # str(dat_raw)
    
    dat_relevant = cbind(dat_raw$results[-1],
                     location.lat=(dat_raw$results$geometry$location$lat),  # explicit redundancy helpful (already in results)
                     location.lng=(dat_raw$results$geometry$location$lng)
                    )
    
    return(dat_relevant)
}

list(landmark1,landmark2,landmark3)

# apply our custom function to iterate over the list of 3 landmarks
# basically a 'for-loop' but cleaner syntax

out_landmark_123 = lapply(list(landmark1,landmark2,landmark3),
                          get_gps_custom_func
                         )

str(out_landmark_123)

# ?rbind_all row binds the elements of the input list into a single output data frame
dat_all = rbind_all(out_landmark_123)

class(dat_all)
dim(dat_all)
head(dat_all)

names(dat_all)
nrow(dat_all)

dat_all$place_id %>% unique() %>% length()

# alternative to determine unique()
# duplicated(dat_all$place_id)
# !(duplicated(dat_all$place_id))

# since we used 3 landmarks, their 5000 km circles will overlap
# remove duplicate in-n-out via unique identifier 'place_id'

names(dat_all)

# library(dplyr)

dat_all_uniq = dat_all %>%
dplyr::distinct(place_id) %>%  # alternative to uniqe() and !duplicated()
dplyr::select(location.lng,location.lat,place_id)

# note: now in (lng,lat) order, for later processing (later on will expect data.frame() format)
# we could easily convert it here as well

head(dat_all_uniq)
class(dat_all_uniq)
dim(dat_all_uniq)

# use google 'place_id' as row names, to keep track of points
row.names(dat_all_uniq) = dat_all_uniq$place_id

# with row names assigned, no need for place_id as a column
dat_latlng = dat_all_uniq %>%
select(-place_id)

head(dat_latlng)
row.names(dat_latlng)

# we explicitly save the points later when we migrate
# save(dat_latlng,file='/notebooks/gis_demog_misc/dat_latlng.RData')

class(la_county_zoom)

?SpatialPolygonsDataFrame

str(la_county_zoom)

# 2213 data rows
# 2213 polygons

# the spatial-object came with pre-existing data in its data slot
str(la_county_zoom@data)
dim(la_county_zoom@data)
head(la_county_zoom@data)

# our census data that we wish to merge, then inject
dim(dat_census)
head(dat_census)

# make new variable 'GEOID' for census dataset
# eg glue together: state,county,tract

dat_census2 = dat_census %>%
mutate(GEOID=paste0(state,county,tract)) %>%
select(GEOID,TRACTCE=tract,Tot_Population_CEN_2010)  # rename 'tract' to 'TRACTCE' to agree with naming convention

head(dat_census2)
head(la_county_zoom@data)

names(la_county_zoom)
class(la_county_zoom)

names(dat_census2)
class(dat_census2)

dim(la_county_zoom)  # less number of tracts (than census api) because we 'zoomed' in via custom points
dim(dat_census2)

?spCbind

dat_census2 %>% filter(GEOID == '06037800204')

# 'spooky behavior' that number of tract mismatch error is not an issue when using spCbind
# obviously two observations in census table have tract '800204' (geoid '06037800204') (1 is NA 1 is 6740)
# see the two blank tracts in western pan handle

# if we explicitly remove the one that is missing NA
# now, just one blank tract (the correct NA one we skipped)


ind_error = which((dat_census2$GEOID == '06037800204') & is.na(dat_census2$Tot_Population_CEN_2010 == TRUE))
dat_census3 = dat_census2[-ind_error,]

dat_census3 %>% filter(GEOID == '06037800204')


# la_county_zoom[865,'GEOID']
# la_county_zoom[866,'GEOID']

# plot(la_county_zoom[865,])
# plot(la_county_zoom[866,])

dat_census2 %>% filter(GEOID == '06037800204')

# dat_census2_subset %>% filter(GEOID == '06037800204')  # this did matching, which default removed the 6740, while keeping NA

dat_census3 %>% filter(GEOID == '06037800204')

# annoying that we have to dive into rowname of spdf too
# but good assertion check to make sure polygon ID linked to Data ID correctly

xx = la_county_zoom  # spatial polygon data frame
xtra = dat_census3  # data frame of new variables

# we just wrote the two lines above
# to shoehorn our right hand side objects to the naming convention of examples
# all of below is copy/paste example from ?spCbind

# explicitly get rows that have matching GEOID
o = match(xx$GEOID, xtra$GEOID)
length(o)
dim(xtra)

xtra1 <- xtra[o,]
dim(xtra1)


# use geoid as rowname of spatialfoo
# hidden annoyance
xx <- spChFIDs(xx, as.character(xx$GEOID))

# row.names(xx)

#rownames of new dataframe
row.names(xtra1) <- xx$GEOID

# cbind spdf xx to dataframe xtra1
xx1 <- spCbind(xx, xtra1)

la_county_zoom_new = xx1

spplot(la_county_zoom_new,z='Tot_Population_CEN_2010')

# we explicitly save the spdf later when we migrate
# save(la_county_zoom_new,file='/notebooks/gis_demog_misc/la_county_zoom_new.RData')

# library(deldir)
# using bbox of poly

voronoipolygons = function(x, poly) {
  require(deldir)
  if (.hasSlot(x, 'coords')) {
    crds <- x@coords
  } else
      crds = x
      # bb = bbox(poly)  # when is bb used, not used
      rw = as.numeric(t(bbox(poly)))  # using poly's bounding box for output of deldir
      z <- deldir(crds[, 1], crds[, 2],rw=rw)
      w <- tile.list(z)
      
  polys <- vector(mode = 'list', length = length(w))
  require(sp)
  for (i in seq(along = polys)) {
    # each polygon in tile list, extract coordinates
    pcrds <- cbind(w[[i]]$x, w[[i]]$y)
    pcrds <- rbind(pcrds, pcrds[1,])  # closed poly
      
      cust_ind = w[[i]]$ptNum  # original index that deldir outputs
      cust_ind2 = row.names(x[cust_ind,])  # use original index, to look up google place id
      polys[[i]] <- Polygons(list(Polygon(pcrds)), ID = cust_ind2)  # use place id as polygon id
      
  }
  
  SP = SpatialPolygons(polys)
      
  # return deldir::voronoi poly as spdf
      voronoi = SpatialPolygonsDataFrame(SP,
                                      data = data.frame(x = crds[, 1],
                                                        y = crds[, 2],
                                                        row.names = sapply(slot(SP, 'polygons'),
                                                                           function(x)
                                                                               slot(x, 'ID')
                                                                                                                                                              )
                                                                               )
                                                                               )
                                                                               return(voronoi)
}

# the table of gps points expects data.frame(dat_latlng) format
class(dat_latlng)

dat_latlng = data.frame(dat_latlng)

class(dat_latlng)

plot(la_county_bdry)
points(dat_latlng)

plot(la_county_bdry)

temp_sp = SpatialPoints(data.frame(dat_latlng))
raster::crs(temp_sp) <- raster::crs(la_county_bdry)

pts_in_la = raster::crop(temp_sp,la_county_bdry)

points(pts_in_la)

head(pts_in_la)
length(pts_in_la)
length(unique(row.names(pts_in_la)))

names(la_county_zoom_new)

plot(la_county_bdry)
points(pts_in_la)

vor_spdf = voronoipolygons(pts_in_la,la_county_zoom_new)
plot(vor_spdf)
points(pts_in_la)

row.names(vor_spdf)

?sp::aggregate

names(la_county_zoom_new)

# argument 1 is areal unit source
# argument 2 is target spatial support
# areaWeighted = TRUE, uses proportion of area overlap to weight attribute
# outputs spdf

# default function is 'mean()', hence santa monica 8000+ pop is smoothed by surrounding lower pop
# option to choose different function, sum(), to get direct counts (but cannot areaWeighted='TRUE')
# but if use 'areaWeighted = True', forced to use mean() for aggregation function

vor_agg_spdf = aggregate(la_county_zoom_new['Tot_Population_CEN_2010'],  # Tot_Population_CEN_2010
                               by=vor_spdf,
                               areaWeighted = TRUE)


# spplot(vor_agg_spdf)

## Use LA County Boundary to crop our voronoi polygons
# avoid ?gIntersection(poly_1,poly_2, byid=TRUE)
# spooky behavior with mixing up ids
# even with: option in gIntersection(...,id=row.names(vor_agg_spdf))
# instead, use ?raster::crop

## Crop to the vor_agg_spdf extent, then plot
# crs(vor_agg_spdf) <- raster::crs(la_county_bdry)
vor_agg_spdf_crop = raster::crop(vor_agg_spdf, la_county_bdry)
# row.names(vor_agg_spdf_crop)

plot(la_county_bdry)
spplot(vor_agg_spdf)
spplot(vor_agg_spdf_crop)

# str(la_county_zoom_new)
# str(pts_in_la)
# str(vor_agg_spdf_crop)

save(la_county_zoom_new,file='~/notebooks/gis_demog_misc/la_county_zoom_new.RData')
save(pts_in_la,file='~/notebooks/gis_demog_misc/pts_in_la.RData')
save(vor_agg_spdf_crop,file='~/notebooks/gis_demog_misc/vor_agg_spdf_crop.RData')

#####################
# read in the previously exported data
#####################

# jaga: linux environment
# load(file='~/notebooks/gis_demog_misc/la_county_zoom_new.RData')
# load(file='~/notebooks/gis_demog_misc/pts_in_la.RData')
# load(file='~/notebooks/gis_demog_misc/vor_agg_spdf_crop.RData')

# windows environment
# load('C:\\Users\\mtzen\\Documents\\in-n-out\\pts_in_la.RData')
# load('C:\\Users\\mtzen\\Documents\\in-n-out\\la_county_zoom_new.RData')
# load('C:\\Users\\mtzen\\Documents\\in-n-out\\vor_agg_spdf_crop.RData')


## not really needed
# load('C:\\Users\\mtzen\\Documents\\in-n-out\\la_county_zoom_tvmagic.RData')
# load('C:\\Users\\mtzen\\Documents\\in-n-out\\dat_latlng.RData')

#####################
# lexis: linux environment
# Exercises: finish the code below out yourself
#####################

# load('')

library(leaflet)

#############
# Example 1: choropleths of tracts
# https://rstudio.github.io/leaflet/raster.html
#############

names(la_county_zoom_new)

# define 'pal_func' to control colors
palet_func = colorQuantile(
  domain = la_county_zoom_new$Tot_Population_CEN_2010,
  palette = "Reds",
  na.color = "transparent"
)

# build map

leaflet(la_county_zoom_new) %>%
  addPolygons(
    stroke = FALSE, fillOpacity = 0.5, smoothFactor = 0,
    color = ~palet_func(Tot_Population_CEN_2010)
    ) %>%
  addLegend("bottomleft",
            pal=palet_func,
            values = ~Tot_Population_CEN_2010,
            title = "Census 2010 Pop",
            opacity = 1
            )

# library(leaflet)

#############
# Example 1: Voronoi, Simpler
# https://rstudio.github.io/leaflet/raster.html
#############


# names(vor_agg_spdf_crop)
# names(pts_in_la)


# define 'palet_func' to control colors

palet_func = colorQuantile(
  domain = vor_agg_spdf_crop$Tot_Population_CEN_2010,
  palette = "Blues",
  na.color = "transparent"
)

# build map

leaflet(vor_agg_spdf_crop) %>%
  addPolygons(
    color = ~palet_func(Tot_Population_CEN_2010),
    fillOpacity = 0.6, 
    stroke = FALSE, 
    smoothFactor = 0  # optional smooth of polygon line
  ) %>%
  addCircles(lng = pts_in_la$location.lng,
             lat = pts_in_la$location.lat,
             color='red',
             weight = 5
  ) %>%
  addLegend("bottomleft",
            pal=palet_func,
            values = ~Tot_Population_CEN_2010,
            title = "Census 2010 Pop",
            opacity = 1
  )  %>% 
  addProviderTiles("Stamen.Toner")

# On Your Own, Using above code example, try making different maps
# ?leaflet
# ?addPolygons
# https://rstudio.github.io/leaflet/

#############
# Example 2: Voronoi, More Options
# https://rstudio.github.io/leaflet/raster.html
#############

names(vor_agg_spdf_crop)
names(pts_in_la)
names(la_county_zoom_new)


# define 'palet_func' to control colors
palet_func = colorQuantile(
  domain = vor_agg_spdf_crop$Tot_Population_CEN_2010,
  palette = "Blues",
  na.color = "transparent"
)

# build map

leaflet(vor_agg_spdf_crop) %>%
  
  # cosmetics: black bounding box for underlying background
  addRectangles(
    lng1 = -118.95172, lat1 = 33.67688,
    lng2 = -117.6464, lat2 = 34.3834,
    stroke=FALSE, fillColor = "black",
    fillOpacity = 1
  ) %>%
  
  # gps
  addCircles(
    lng = pts_in_la$location.lng,
    lat = pts_in_la$location.lat,
    weight = 10,
    fillOpacity = 0,
    color = 'cyan'
  ) %>%
  
  # voronoi polygons
  addPolygons(
    stroke = TRUE,
    weight = 5,
    color = 'cyan',
    fillOpacity = 0,
    smoothFactor = 0
  )  %>%
  
  # tract polygons (overlay it on map)
  # note: 'la_county_zoom' is second spdf source
  addPolygons(data = la_county_zoom_new,
              stroke = TRUE,
              color = 'orange',
              weight = 1) %>%
  
  # show voronoi aggregated data
  addPolygons(data = vor_agg_spdf_crop,
              stroke = FALSE, fillOpacity = 0.8, smoothFactor = 0,
              color = ~palet_func(Tot_Population_CEN_2010)
  ) %>%
  
  # finishing sprinkles
  addLegend("bottomleft",
            pal=palet_func,
            values = ~Tot_Population_CEN_2010,
            title = "Census 2010 Pop",
            opacity = 1
  ) %>% 
  
  # http://leaflet-extras.github.io/leaflet-providers/preview/
  addProviderTiles("Stamen.Toner")
  # addProviderTiles("OpenTopoMap")
  # addProviderTiles("NASAGIBS.ViirsEarthAtNight2012")  # zoom out