In MatthewSchumwinger/mkedata: Repository for Processed Milwaukee Data

mkedata takes care of much of the grunt work of acquiring and processing Milwaukee data. This puts you much closer to doing what you most care about: analyzing and visualizing Milwaukee data.

Exploring Milwaukee Crime Data

What's the citywide trend in homicides?

library(mkedata)
data("crimes.munged")
h <- subset(crimes.munged, OFFENSE1 == "HOMICIDE")
barplot(table(h$year))

Mapping Milwaukee Data

Let's look at where those homicides took place in 2015. First overall, then in and around the Avenues West Business Improvement District.

library(sp)
# mkeoutline <- to_nad27(mkeoutline)
h2015 <- subset(crimes.munged, OFFENSE1 == "HOMICIDE" & year == "2015")
plot(mkeoutline)
plot(h2015, col = "salmon", add = T)

# bids <- to_nad27(bids)
plot(subset(bids, Name == "Avenues West"))
plot(h2015, col = "salmon", add = T)

Using ggmap and a transformation of the coordinates to the web map standard projection (WGS84), we can plot these crimes on a basemap for context:

library(ggmap)
h.wgs84 <- to_wgs84(h2015) 
m <- data.frame(slot(h.wgs84, "coords"), slot(h.wgs84, "data"))
    names(m)[1:2] <- c("lon", "lat")
    qmplot(lon, lat, data = m, color = I("salmon"))