rud.is

maine <- readOGR("data/maine.geojson", "OGRGeoJSON")
 
map <- ggplot2::fortify(maine, region="name")
 
map %>%
  ggvis(~long, ~lat) %>%
  group_by(group, id) %>%
  layer_paths(strokeOpacity:=0.5, stroke:="#7f7f7f") %>%
  hide_legend("fill") %>%
  hide_axis("x") %>% hide_axis("y") %>%
  set_options(width=400, height=600, keep_aspect=TRUE)

county_centers <- maine %>%
  gCentroid(byid=TRUE) %>%
  data.frame %>%
  cbind(name=maine$name %>% gsub(" County, ME", "", .) )
 
map %>%
  group_by(group, id) %>%
  ggvis(~long, ~lat) %>%
  layer_paths(strokeWidth:=0.25, stroke:="#7f7f7f") %>%
  layer_points(data=county_centers, x=~x, y=~y, size:=8) %>%
  layer_text(data=county_centers,
             x=~x+0.05, y=~y, text:=~name,
             baseline:="middle", fontSize:=8) %>%
  hide_legend("fill") %>%
  hide_axis("x") %>% hide_axis("y") %>%
  set_options(width=400, height=600, keep_aspect=TRUE)

# read in some crime & population data for maine counties
me_pop <- read.csv("data/me_pop.csv", stringsAsFactors=FALSE)
me_crime <- read.csv("data/me_crime.csv", stringsAsFactors=FALSE)
 
# get it into a form we can use (and only use 2013 data)
 
crime_1k <- me_crime %>%
  filter(year==2013) %>%
  select(1,5:12) %>%
  left_join(me_pop) %>%
  mutate(murder_1k=1000*(murder/population_2010),
         rape_1k=1000*(rape/population_2010),
         robbery_1k=1000*(robbery/population_2010),
         aggravated_assault_1k=1000*(aggravated_assault/population_2010),
         burglary_1k=1000*(burglary/population_2010),
         larceny_1k=1000*(larceny/population_2010),
         motor_vehicle_theft_1k=1000*(motor_vehicle_theft/population_2010),
         arson_1k=1000*(arson/population_2010))
 
# normalize the county names
 
map %<>% mutate(id=gsub(" County, ME", "", id)) %>%
  left_join(crime_1k, by=c("id"="county"))
 
# this is for the tooltip. it does a lookup into the crime data frame and
# then uses those values for the popup
 
crime_values <- function(x) {
  if(is.null(x)) return(NULL)
  y <- me_crime %>% filter(year==2013, county==x$id) %>% select(1,5:12)
  sprintf("<table width='100%%'>%s</table>",
          paste0("<tr><td style='text-align:left'>", names(y),
         ":</td><td style='text-align:right'>", format(y), collapse="</td></tr>"))
}
 
map %>%
  group_by(group, id) %>%
  ggvis(~long, ~lat) %>%
  layer_paths(fill=input_select(label="Crime:",
                                choices=crime_1k %>%
                                  select(ends_with("1k")) %>%
                                  colnames %>% sort,
                                id="Crime",
                                map=as.name),
              strokeWidth:=0.5, stroke:="white") %>%
  scale_numeric("fill", range=c("#bfd3e6", "#8c6bb1" ,"#4d004b")) %>%
  add_tooltip(crime_values, "hover") %>%
  add_legend("fill", title="Crime Rate/1K Pop") %>%
  hide_axis("x") %>% hide_axis("y") %>%
  set_options(width=400, height=600, keep_aspect=TRUE)

us <- readOGR("data/us.geojson", "OGRGeoJSON")
us <- us[!us$STATEFP %in% c("02", "15", "72"),]
 
# same method to change the projection
 
us_aea <- spTransform(us, CRS("+proj=laea +lat_0=45 +lon_0=-100 +x_0=0 +y_0=0 +a=6370997 +b=6370997 +units=m +no_defs"))
 
map <- ggplot2::fortify(us_aea, region="GEOID")
 
droughts <- read.csv("data/dm_export_county_20141223.csv")
droughts$id <- sprintf("%05d", as.numeric(as.character(droughts$FIPS)))
droughts$total <- with(droughts, (D0+D1+D2+D3+D4)/5)
 
map_d <- merge(map, droughts, all.x=TRUE)
 
# pre-make custom colors per county
 
ramp <- colorRampPalette(c("white", brewer.pal(n=9, name="YlOrRd")), space="Lab")
 
map_d$fill_col <- as.character(cut(map_d$total, seq(0,100,10), include.lowest=TRUE, labels=ramp(10)))
map_d$fill_col <- ifelse(is.na(map_d$fill_col), "#FFFFFF", map_d$fill_col)
 
drought_values <- function(x) {
  if(is.null(x) | !(x$id %in% droughts$id)) return(NULL)
  y <- droughts %>% filter(id==x$id) %>% select(1,3,4,6:10)
  sprintf("<table width='100%%'>%s</table>",
          paste0("<tr><td style='text-align:left'>", names(y),
         ":</td><td style='text-align:right'>", format(y), collapse="</td></tr>"))
}
 
map_d %>%
  group_by(group, id) %>%
  ggvis(~long, ~lat) %>%
  layer_paths(fill:=~fill_col, strokeOpacity := 0.5, strokeWidth := 0.25) %>%
  add_tooltip(drought_values, "hover") %>%
  hide_legend("fill") %>%
  hide_axis("x") %>% hide_axis("y") %>%
  set_options(width=900, height=600, keep_aspect=TRUE)

world <- readOGR("data/ne_50m_admin_0_countries.geojson", layer="OGRGeoJSON")
world <- world[!world$iso_a3 %in% c("ATA"),]
world <- spTransform(world, CRS("+proj=wintri"))
 
map_w <- ggplot2::fortify(world, region="iso_a3")
 
# really quick way to get coords from a KML file
 
launch_sites <- rbindlist(lapply(ogrListLayers("data/launch-sites.kml")[c language="(1:2,4:9)"][/c], function(layer) {
  tmp <- readOGR("data/launch-sites.kml", layer)
  places <- data.table(coordinates(tmp)[,1:2], as.character(tmp$Name))
}))
setnames(launch_sites, colnames(launch_sites), c("lon", "lat", "name"))
 
# now, project the coordinates we extracted
 
coordinates(launch_sites) <-  ~lon+lat
launch_sites <- as.data.frame(SpatialPointsDataFrame(spTransform(
  SpatialPoints(launch_sites, CRS("+proj=longlat")), CRS("+proj=wintri")),
  launch_sites@data))
 
map_w %>%
  group_by(group, id) %>%
  ggvis(~long, ~lat) %>%
  layer_paths(fill:="#252525", stroke:="white", strokeOpacity:=0.5, strokeWidth:=0.25) %>%
  layer_points(data=launch_sites, 
               x=~lon, y=~lat, 
               fill:="#cb181d", stroke:="white", 
               size:=25, fillOpacity:=0.5, strokeWidth:=0.25) %>%
  hide_legend("fill") %>%
  hide_axis("x") %>% hide_axis("y") %>%
  set_options(width=900, height=500, keep_aspect=TRUE)