MattCowgill

license: mit

MattCowgill

library(readrba)
library(tidyverse)

theme_anz <- function(...) {
  theme_minimal(...) +
  theme(panel.grid.major.x = element_blank(),
        panel.grid.minor = element_blank(),
        legend.position = "bottom",
        legend.direction = "horizontal",
        legend.title = element_blank(),

MattCowgill

library(readxl)
library(tidyverse)
library(lubridate)
library(janitor)

rents_url <- "https://www.dffh.vic.gov.au/moving-annual-rents-suburb-june-quarter-2021-excel"
rents_loc <- tempfile(fileext = ".xlsx")

download.file(url = rents_url,
              destfile = rents_loc,

MattCowgill

library(tidyverse)
library(readabs)
library(readxl)
library(lubridate)

hes_file <- download_abs_data_cube("household-expenditure-survey-australia-summary-results",
                                   "do001")

hes_raw <- read_excel(hes_file, sheet = "Table 1.1", range = "A33:H47",
                      col_names = c("cat", "1984", "1988–89", "1993–94", "1998–99", "2003–04", "2009–10", "2015–16"))

MattCowgill

library(OECD)
library(tidyverse)
library(lubridate)
library(countrycode)

oecd_cpi_raw <- get_dataset("PRICES_CPI",
                            "AUS+CAN+JPN+GBR+USA+EA19.CPALTT01.GY.Q+M")

oecd_cpi <- oecd_cpi_raw |> 
  janitor::clean_names() |> 

MattCowgill

library(tidyverse)
library(readrba)

forecasts <- rba_forecasts()

latest_two <- forecasts |>
  filter(forecast_date %in% c(max(forecast_date),
                              max(forecast_date) - months(3)))

latest_two |>

MattCowgill

library(tidyverse)
library(readabs)

vac_raw <- read_abs_series("A85389466F")

ur_raw <- read_abs_series("A84423050A")

ur <- ur_raw |> 
  mutate(value = slider::slide_mean(value, before = 2L)) |> 
  select(date, ur = value)

MattCowgill

# This code provides a way to download table(s) from the Estimate Dwelling Stock
# release, which has not (yet) been added to the ABS Time Series Directory
# and therefore cannot be loaded by readabs::read_abs()

get_dwell_table <- function(table_no = 1, path = tempdir()) {
  table_no <- match.arg(as.character(table_no), as.character(1:9))
  filename <- paste0("87010", table_no, ".xlsx")
  file <- readabs::download_abs_data_cube("estimated-dwelling-stock",
                                  filename,
                                  path = path)

MattCowgill

c("JUN-16", "SEP-16", "DEC-16", "MAR-17", "JUN-17", "SEP-17", "DEC-17", "MAR-18", "JUN-18", "SEP-18", "DEC-18", "MAR-19", "JUN-19", "SEP-19", "DEC-19", "MAR-20", "JUN-20", "SEP-20", "DEC-20", "MAR-21", "JUN-21", "SEP-21", "DEC-21", "MAR-22", "JUN-22", "JUN-16", "SEP-16", "DEC-16", "MAR-17", "JUN-17", "SEP-17", "DEC-17", "MAR-18", "JUN-18", "SEP-18", "DEC-18", "MAR-19", "JUN-19", "SEP-19", "DEC-19", "MAR-20", "JUN-20", "SEP-20", "DEC-20", "MAR-21", "JUN-21", "SEP-21", "DEC-21", "MAR-22", "JUN-22", 
"JUN-16", "SEP-16", "DEC-16", "MAR-17", "JUN-17", "SEP-17", "DEC-17", "MAR-18", "JUN-18", "SEP-18", "DEC-18", "MAR-19", "JUN-19", "SEP-19", "DEC-19", "MAR-20", "JUN-20", "SEP-20", "DEC-20", "MAR-21", "JUN-21", "SEP-21", "DEC-21", "MAR-22", "JUN-22", "JUN-16", "SEP-16", "DEC-16", "MAR-17", "JUN-17", "SEP-17", "DEC-17", "MAR-18", "JUN-18", "SEP-18", "DEC-18", "MAR-19", "JUN-19", "SEP-19", "DEC-19", "MAR-20", "JUN-20", "SEP-20", "DEC-20", "MAR-21", "JUN-21", "SEP-21", "DEC-21", "MAR-22", "JUN-22", "JUN-16", 
"SEP-16", 

MattCowgill

library(readabs)

path <- file.path(tempdir(), "abs_dwellings")
dir.create(path)

dwellings_sa2_zip <- download_abs_data_cube("estimated-dwelling-stock",
                                        "zip",
                                        path = path)

zip::unzip(dwellings_sa2_zip,