library(ggplot2)
library(viridis)
library(ggthemes)
+library(animation) # for saveGIF
# Obtain the tax dataset if not available yet
if(!file.exists("data/taxstats2015individual06ataxablestatusstateterritorypostcode.csv"))
# 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 = "") +
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)