From a4d86b2c1b034830351c7d5d9e72a1acb2d36a29 Mon Sep 17 00:00:00 2001 From: Frederik Vanrenterghem Date: Thu, 2 Nov 2017 22:10:21 +0800 Subject: [PATCH] Style the graph. --- AU-taxstats.R | 17 ++++++++++++++--- 1 file changed, 14 insertions(+), 3 deletions(-) diff --git a/AU-taxstats.R b/AU-taxstats.R index 728c943..76f9dcf 100644 --- a/AU-taxstats.R +++ b/AU-taxstats.R @@ -4,6 +4,7 @@ install.packages("devtools") devtools::install_github("tidyverse/ggplot2") # needed for geom_sf library(ggplot2) library(viridis) +library(ggthemes) # Obtain the tax dataset if not available yet if(!file.exists("data/taxstats2015individual06ataxablestatusstateterritorypostcode.csv")) @@ -50,12 +51,22 @@ taxstats.POA$`Total.Income.or.Loss.no.`[is.na(taxstats.POA$`Total.Income.or.Loss # 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$TotalIncomeEarners +sa3$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") )) + geom_sf(aes(fill = incomeperearningcapita, color = incomeperearningcapita)) + - scale_fill_viridis("incomeperearningcapita") + - scale_color_viridis("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", + 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()) + -- 2.39.5