X-Git-Url: http://git.vanrenterghem.biz/R/project-au-taxstats.git/blobdiff_plain/a4d86b2c1b034830351c7d5d9e72a1acb2d36a29..83ec806f511257df27151b397a132b3d9d061c84:/AU-taxstats.R

diff --git a/AU-taxstats.R b/AU-taxstats.R
index 76f9dcf..92577c6 100644
--- a/AU-taxstats.R
+++ b/AU-taxstats.R
@@ -5,6 +5,7 @@ devtools::install_github("tidyverse/ggplot2") # needed for geom_sf
 library(ggplot2)
 library(viridis)
 library(ggthemes)
+library(animation) # for saveGIF
 
 # Obtain the tax dataset if not available yet
 if(!file.exists("data/taxstats2015individual06ataxablestatusstateterritorypostcode.csv")) 
@@ -40,7 +41,6 @@ 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
 
@@ -56,17 +56,29 @@ sa3$incomeperearningcapita <- (sa3$TotalIncome / sa3$TotalIncomeEarners)/1000
 # 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") )) +
+cities = c("Perth","Melbourne","Sydney","Adelaide","Brisbane")
+
+# Create a plot for each of these cities. This is wrapped in a function
+# for use by saveGIF
+
+plots <- function() {lapply(cities, function(x){
+ plot <- ggplot(dplyr::filter(sa3, data.table::`%like%`(GCC_NAME16, x) )) +
   geom_sf(aes(fill = incomeperearningcapita, color = incomeperearningcapita)) +
-  scale_fill_viridis(name = "") +
-  scale_color_viridis(name = "") +
+  scale_fill_viridis(name = "",
+                     limits = c(min(sa3$incomeperearningcapita, na.rm = TRUE),max(sa3$incomeperearningcapita, na.rm = TRUE))) +
+  scale_color_viridis(name = "",
+                                limits = c(min(sa3$incomeperearningcapita, na.rm = TRUE),max(sa3$incomeperearningcapita, na.rm = TRUE))) +
   coord_sf(datum = NA) + # Work around https://github.com/tidyverse/ggplot2/issues/2071 to remove gridlines
-  labs(title = "Melbourne \nincome distribution",
+  labs(title = paste0(x," \nincome distribution"),
        subtitle = "2014/15, in 1000s AUD",
        caption = "\nSource: Australian Taxation Office") +
   theme_economist() +
   theme(legend.position = "bottom",
         legend.text = element_text(angle = 45, hjust = 1, size = 8),
         axis.text = element_blank(),
-        axis.ticks = element_blank()) 
-  
+        axis.ticks = element_blank())
+ print(plot)
+})
+}
+
+saveGIF(plots(),movie.name = "AUCitiesIncomeDistribution.gif", interval = 2, loop = 2)