Only look at Melbourne.

diff --git a/AU-taxstats.R b/AU-taxstats.R
index e96c7a62a6a881c7331c596dcea96774d99b70e9..728c943c07940bd9c7a0770ece8a28d48b219774 100644 (file)
--- a/AU-taxstats.R
+++ b/AU-taxstats.R
@@ -51,7 +51,11 @@ taxstats.POA$`Total.Income.or.Loss.no.`[is.na(taxstats.POA$`Total.Income.or.Loss
 sa3$TotalIncome <- as.vector(POA_SAs %*% as.matrix(taxstats.POA$`Total.Income.or.Loss..`))
 sa3$TotalIncomeEarners <- as.vector(POA_SAs %*% as.matrix(taxstats.POA$`Total.Income.or.Loss.no.`))
 sa3$incomeperearningcapita <- sa3$TotalIncome / sa3$TotalIncomeEarners
 sa3$TotalIncome <- as.vector(POA_SAs %*% as.matrix(taxstats.POA$`Total.Income.or.Loss..`))
 sa3$TotalIncomeEarners <- as.vector(POA_SAs %*% as.matrix(taxstats.POA$`Total.Income.or.Loss.no.`))
 sa3$incomeperearningcapita <- sa3$TotalIncome / sa3$TotalIncomeEarners

-ggplot(sa3) +
+
+# As SA3s are still to narrow around cities compared to in the country,
+# let's simply look at Melbourne
+
+ggplot(dplyr::filter(sa3, data.table::`%like%`(GCC_NAME16, "Melbourne") )) +

   geom_sf(aes(fill = incomeperearningcapita, color = incomeperearningcapita)) +
   scale_fill_viridis("incomeperearningcapita") +
   scale_color_viridis("incomeperearningcapita")
   geom_sf(aes(fill = incomeperearningcapita, color = incomeperearningcapita)) +
   scale_fill_viridis("incomeperearningcapita") +
   scale_color_viridis("incomeperearningcapita")