read_awe {readabs}

read_awe

Description

Convenience function to obtain wage levels from ABS 6302.0, Average Weekly Earnings, Australia.

Usage

read_awe(
  wage_measure = c("awote", "ftawe", "awe"),
  sex = c("persons", "males", "females"),
  sector = c("total", "private", "public"),
  state = c("all", "nsw", "vic", "qld", "sa", "wa", "tas", "nt", "act"),
  na.rm = FALSE,
  path = Sys.getenv("R_READABS_PATH", unset = tempdir()),
  show_progress_bars = FALSE,
  check_local = FALSE
)

Arguments

wage_measure

Character of length 1. Must be one of:

awote

Average weekly ordinary time earnings; also known as Full-time adult ordinary time earnings

ftawe

Full-time adult total earnings

awe

Average weekly total earnings of all employees

sex

Character of length 1. Must be one of: persons, males, or females.

sector

Character of length 1. Must be one of: total, private, or public. Note that you cannot get sector-by-state data; if state is not all then sector must be total.

state

Character of length 1. Must be one of: all, nsw, vic, qld, sa, wa, nt, or act. Note that you cannot get sector-by-state data; if sector is not total then state must be all.

na.rm

Logical. FALSE by default. If FALSE, a consistent quarterly series is returned, with NA values for quarters in which there is no data. If TRUE, only dates with data are included in the returned data frame.

path

See ?read_abs

show_progress_bars

See ?read_abs

check_local

See ?read_abs

Details

The latest AWE data is available using read_abs(cat_no = "6302.0", tables = 2). However, this time series only goes back to 2012, when the ABS switched from quarterly to biannual collection and release of the AWE data. The read_awe() function assembles on time series back to November 1983 quarter; it is quarterly to 2012 and biannual from then. Note that the data returned with this function is consistently quarterly; any quarters for which there are no observations are recorded as NA unless na.rm = TRUE.

Value

A tbl_df with four columns: date, sex, wage_measure and value. The data is nominal and seasonally adjusted.

Examples

## Not run: 
read_awe("awote", "persons")

## End(Not run)