Boston Trash Pickup

Load in trash data, play around with it:

trash <- read.csv("trash-schedules-by-address.csv",stringsAsFactors = F)
head(trash)

table(trash$Trash)

            773428.4924           F           M         MTH         MWF           T          TF          TH           W 
       3461           1       35775       36610       33071       29105       48745       45667       49871       56778 

table(trash$Recycling)

            2950378.065           F           M           T          TH           W 
       3461           1       64880       69681       94412       49871       56778 

trash <- mutate(trash, freq_trash = ifelse(nchar(Trash)==1,1,
                                           ifelse(Trash=="MTH",2,
                                                  ifelse(Trash=="MWF",3,
                                                         ifelse(Trash=="TF",2,
                                                                ifelse(Trash=="TH",1,NA))))),
                freq_recycling = ifelse(nchar(Recycling)==1,1,
                                           ifelse(Recycling=="MTH",2,
                                                  ifelse(Recycling=="MWF",3,
                                                         ifelse(Recycling=="TF",2,
                                                                ifelse(Recycling=="TH",1,NA))))))

In order to do any spatial things with these data, we need to turn into a spatial points dataframe:

trash$lat <- as.numeric(gsub("\\((\\d+\\.\\d+), (-\\d+\\.\\d+)\\)","\\1",trash$Location))
trash$long <- as.numeric(gsub("\\((\\d+\\.\\d+), (-\\d+\\.\\d+)\\)","\\2",trash$Location))
trash <- trash[!is.na(trash$long),]
trash.spdf <- SpatialPointsDataFrame(coords = cbind(trash$long,trash$lat),
                                               data = trash,
                                               proj4string=CRS("+proj=longlat +datum=NAD83 +no_defs +ellps=GRS80 +towgs84=0,0,0") # these data are just Long/Lat coords
                                               )

Bring in census geo shapefiles and subset them to Boston area:

Map the Census geographies:

b0 <- ggmap(boston_base, extent="device",legend="none",maprange=T)

Scale for 'x' is already present. Adding another scale for 'x', which will replace the existing scale.
Scale for 'y' is already present. Adding another scale for 'y', which will replace the existing scale.
`panel.margin` is deprecated. Please use `panel.spacing` property instead

(b1 <- b0 + 
      geom_polygon(aes(x=long,y=lat,group=group),data=boston_bg,color = "black",alpha=0.4))

Regions defined for each Polygons

(b1 <- b0 + 
      geom_polygon(aes(x=long,y=lat,group=group),data=boston_tracts,color = "black",alpha=0.4))

Regions defined for each Polygons

Look at trash frequency within tracts:

Output tract-level summary for use elsewhere:

Now can map this tabular data in WorldMap via Dataverse, if needed.