ESA_Gender_Symposium_9-10.R

library(data.table)
library(magrittr)
library(grattan)
library(SampleFile1415)
library(taxstats)
library(hutils)
library(scales)

tax2014 <- copy(sample_file_1415) %>% apply_super_caps_and_div293()

nIndivsPreTaxContrGeq10K_by_decile <- 
  tax2014[, .(nIndivsPreTaxContrGeq10K = 50L * sum(concessional_contributions > 10e3),
              nIndivsVoluntaryContrGeq10K = 50L * sum(concessional_contributions - SG_contributions > 10e3)),
          keyby = .(TaxableIncomeDecile = factor(dplyr::ntile(Taxable_Income, 10L)),
                    Gender)] %>% 
  melt.data.table(measure.vars = patterns("10K")) %>%
  .[, Sex := if_else(as.logical(Gender), "Female", "Male")] %>%
  .[]

library(grattanCharts)
library(ggplot2)

grplot(nIndivsPreTaxContrGeq10K_by_decile,
       aes(x = TaxableIncomeDecile,
           y = value,
           fill = variable)) + 
  geom_col(position = "dodge") + 
  facet_wrap(~Sex) + 
  xlab("Taxable Income decile") + 
  scale_y_continuous(labels = comma)

tax201718 <- project(sample_file_1415, h = 3L) %>% apply_super_caps_and_div293()

sample_file_1415 %>%
  copy %>%
  .[, in_scope := MCS_Ttl_Acnt_Bal > 500e3] %>%
  project(h = 3L) %>% 
  apply_super_caps_and_div293() %>%
  .[, TaxableIncomeDecile := factor(dplyr::ntile(Taxable_Income, 10L))] %>%
  .[(in_scope)] %>%
  .[, .(N = floor(sum(WEIGHT * (concessional_contributions > 25e3)))),
    keyby = .(Sex = if_else(as.logical(Gender), "Female", "Male"),
              TaxableIncomeDecile)]