geom_sf(aes(fill = incomeperearningcapita, color = incomeperearningcapita)) +
scale_fill_viridis("incomeperearningcapita") +
scale_color_viridis("incomeperearningcapita")
+
+# Let's try by SA3
+if(!file.exists("data/1270055001_sa3_2016_aust_shape.zip"))
+ download.file(url = "http://www.abs.gov.au/AUSSTATS/subscriber.nsf/log?openagent&1270055001_sa3_2016_aust_shape.zip&1270.0.55.001&Data%20Cubes&43942523105745CBCA257FED0013DB07&0&July%202016&12.07.2016&Latest", destfile = "data/1270055001_sa3_2016_aust_shape.zip")
+
+if(!file.exists("data/SA3_2016_AUST.shp"))
+ unzip(zipfile = "data/1270055001_sa3_2016_aust_shape.zip", exdir = "data/")
+
+sa3 <- st_read(dsn = "data/", layer = "SA3_2016_AUST", stringsAsFactors = FALSE)
+taxstats.sa3 <- merge(x = taxstats, y = sa3, by.x = "Postcode", by.y = "POA_CODE16", all.y = TRUE)
+
+# Create a matrix of intersecting postal codes and SA3's
+
+POA_SAs <- st_intersects(x=sa3, y=POA, sparse=FALSE)
+taxstats.POA$`Total.Income.or.Loss..`[is.na(taxstats.POA$`Total.Income.or.Loss..`)] <- 0
+taxstats.POA$`Total.Income.or.Loss.no.`[is.na(taxstats.POA$`Total.Income.or.Loss.no.`)] <- 0
+# Perform matrix multiplication to obtain the income metrix per SA3
+# Total income will be incorrect, as the POAs intersect with multiple SA3s
+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
+
+# 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")