srinikumar

# Get Montgomery County geocode first to get the map for it
montgomery_county_gc <- geocode("Montgomery County, MD")

# Now, get the actual map
montgomery_county_map <- get_map(location = c(montgomery_county_gc$lon, montgomery_county_gc$lat))

# Showing shades of colors using an abs function
alldata$circadian <- abs(alldata$hour_of_day - 12)
# Using default colors
ggmap(montgomery_county_map) + geom_point(data=sampledata, aes(longitude, latitude, color=circadian))

srinikumar

# Get Montgomery County geocode first to get the map for it 
45 montgomery_county_gc <- geocode("Montgomery County, MD") 
46 
 
47 # Now, get the actual map 
48 montgomery_county_map <- get_map(location = c(montgomery_county_gc$lon, montgomery_county_gc$lat)) 
49 
 
50 
 

srinikumar

execute sp_execute_external_script
  @language = N'R', 
  @script = N' 
  OutputDataSet <- InputDataSet;
  inputData <- InputDataSet;
  names(inputData) <- tolower(names(inputData));
  resultTree <- violation_count_tree <- rxDTree(violations ~ commercial_vehicle + alcohol + work_zone + state + 
                    vehicletype + year + make + model + color + race + gender + 
                    driver_city + driver_state + drivers_license_state + 
                    month_of_year + day_of_week + hour_of_day, 

srinikumar

# The ctree from the party library did not finish at all!
cran_violation_count_tree <- ctree(data=rxViolations, formula=violations ~ commercial_vehicle + alcohol + work_zone + state + 
                                     vehicletype + year + make + model + color + race + gender + 
                                     driver_city + driver_state + drivers_license_state + 
                                     month_of_year + day_of_week + hour_of_day)


# I was pleasantly surprised that the rxDTree function finished in slightly less than 60 seconds
violation_count_tree <- rxDTree(violations ~ commercial_vehicle + alcohol + work_zone + state + 
                    vehicletype + year + make + model + color + race + gender + 

srinikumar

bymonth <- sqldf("select num_month_of_year, sum(frequency) as frequency from res group by num_month_of_year")

srinikumar

 res <- sqlQuery(conn, "select [Date Of Stop] as date_of_stop, num_month_of_year, num_day_of_week, hour_of_day, count([Date Of Stop]) as frequency from Montgomery_County_MD_Traffic_Violations group by [Date Of Stop], num_month_of_year, num_day_of_week, hour_of_day order by [Date Of Stop]")

srinikumar

 res <- sqlQuery(conn, "select [Date Of Stop] as date_of_stop, num_month_of_year, num_day_of_week, hour_of_day, count([Date Of Stop]) as frequency from Montgomery_County_MD_Traffic_Violations group by [Date Of Stop], num_month_of_year, num_day_of_week, hour_of_day order by [Date Of Stop]")