library(data.table)
library(hutils)
library(magrittr)
library(ggplot2)
library(grattan) # needs to be before require_taxstats.
has_taxstats <-
require_taxstats() &&
require_taxstats1516()s1516 <-
as.data.table(sample_file_1516_synth) %>%
setkey(Taxable_Income) %>%
mutate_ntile("Taxable_Income", n = 100L) %>%
mutate_ntile("Taxable_Income", n = 10L)
s1516[, tax := income_tax(Taxable_Income, "2015-16", .dots.ATO = .SD)]s1516[, taxNoNG := income_tax(Taxable_Income - pminC(Net_rent_amt, 0),
fy.year = "2015-16",
.dots.ATO = .SD)]
s1516[, benefit_due_NG := taxNoNG - tax]benefitsNG_by_Decile <-
s1516[, .(avgBenefitNG = mean(benefit_due_NG)), keyby = "Taxable_IncomeDecile"]
copy(benefitsNG_by_Decile) %>%
setnames("Taxable_IncomeDecile", "Taxable Income Decile") %>%
.[, Share := paste0(round(100 * avgBenefitNG / sum(avgBenefitNG)), "%")] %>%
.[, avgBenefitNG := round(avgBenefitNG, -2)] %>%
setnames("avgBenefitNG", "Average benefit from NG / $") %>%
kable(align = "rrr")s1516[, .(avgTaxableIncome = mean(Taxable_Income)), keyby = .(age_range, Gender)] %>%
.[age_range_decoder, on = "age_range"] %>%
.[, Sex := if_else(Gender == 1, "Female", "Male")] %>%
ggplot(aes(x = age_range_description,
y = avgTaxableIncome,
group = Sex,
color = Sex)) +
geom_line() +
scale_y_continuous(labels = function(x) paste0("$", prettyNum(x, big.mark = ",")))