## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = FALSE
)

## ----load---------------------------------------------------------------------
# library(hmrc)

## ----receipts-basic-----------------------------------------------------------
# # All 41 tax heads
# receipts <- get_tax_receipts()
# head(receipts)
# #>         date   tax_head          description receipts_gbp_m
# #>   2016-04-01 income_tax Income Tax (PAYE...          17423
# #>   2016-05-01 income_tax Income Tax (PAYE...          11847

## ----list-heads---------------------------------------------------------------
# list_tax_heads()

## ----receipts-filter----------------------------------------------------------
# big_three <- get_tax_receipts(
#   tax   = c("income_tax", "vat", "nics_total"),
#   start = "2020-01"
# )

## ----receipts-plot, fig.width = 7, fig.height = 4-----------------------------
# library(ggplot2)
# 
# ggplot(big_three, aes(x = date, y = receipts_gbp_m / 1000, colour = description)) +
#   geom_line(linewidth = 0.8) +
#   scale_y_continuous(labels = scales::label_comma(suffix = "bn")) +
#   labs(
#     title   = "UK monthly tax receipts",
#     x       = NULL,
#     y       = "GBP billions",
#     colour  = NULL,
#     caption = "Source: HMRC Tax Receipts and NICs bulletin"
#   ) +
#   theme_minimal(base_size = 12) +
#   theme(legend.position = "bottom")

## ----vat----------------------------------------------------------------------
# # Net VAT: total minus repayments
# vat <- get_vat(measure = c("total", "repayments"), start = "2015-01")
# 
# # Repayments are recorded as negative (money flowing out of HMRC)
# head(vat[vat$measure == "repayments", c("date", "receipts_gbp_m")])

## ----fuel---------------------------------------------------------------------
# fuel <- get_fuel_duties(fuel = "total", start = "2010-01")
# 
# # Annual totals
# fuel$year <- format(fuel$date, "%Y")
# aggregate(receipts_gbp_m ~ year, data = fuel, FUN = sum)

## ----tobacco------------------------------------------------------------------
# tobacco <- get_tobacco_duties(product = c("cigarettes", "hand_rolling"),
#                               start   = "2015-01")

## ----ct-----------------------------------------------------------------------
# ct <- get_corporation_tax()
# ct[ct$type == "total_ct", c("tax_year", "receipts_gbp_m")]

## ----stamp--------------------------------------------------------------------
# sd <- get_stamp_duty(type = "sdlt_total")
# tail(sd[, c("tax_year", "receipts_gbp_m")], 5)

## ----rd-----------------------------------------------------------------------
# # Cost of R&D credits — SME vs RDEC
# rd <- get_rd_credits(measure = "amount_gbp_m")
# rd[rd$tax_year == "2023-24", c("scheme", "description", "value")]

## ----taxgap-------------------------------------------------------------------
# gap <- get_tax_gap()
# 
# # Sort by absolute gap
# gap[order(-gap$gap_gbp_bn),
#     c("tax", "component", "gap_gbp_bn", "uncertainty")]

## ----income-tax---------------------------------------------------------------
# it <- get_income_tax_stats(tax_year = "2023-24")
# it[, c("income_range", "taxpayers_thousands", "tax_liability_gbp_m", "average_rate_pct")]

## ----property-----------------------------------------------------------------
# prop <- get_property_transactions(
#   type   = "residential",
#   nation = "uk",
#   start  = "2018-01"
# )

## ----property-plot, fig.width = 7, fig.height = 4-----------------------------
# ggplot(prop, aes(x = date, y = transactions / 1000)) +
#   geom_line(colour = "#3B82F6", linewidth = 0.8) +
#   scale_y_continuous(labels = scales::label_comma(suffix = "k")) +
#   labs(
#     title   = "UK residential property transactions",
#     x       = NULL,
#     y       = "Transactions (thousands)",
#     caption = "Source: HMRC Monthly Property Transactions bulletin"
#   ) +
#   theme_minimal(base_size = 12)

## ----cache--------------------------------------------------------------------
# # Remove files older than 30 days
# clear_cache(max_age_days = 30)
# 
# # Remove everything and start fresh
# clear_cache()