diff --git a/R/read_erp.R b/R/read_erp.R
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+#' Download a tidy tibble containing the Estimated Residential Population from the ABS
+#'
+#' \code{read_erp()} uses the \code{read_abs()} function to download, import,
+#' and tidy the Estimated Residential Population from the ABS. It allows the user
+#' to specify age, sex and states/territories of interest. It returns a tibble
+#' containing five columns: the date, the age range, sex and states that the ERP
+#' corresponds to. This makes joining the ERP to another dataframe easy.
+#'
+#' @param age_range numeric; default is "0:100". A vector containing ages in single
+#' years for which an ERP is sought. The ABS top-code ages at 100.
+#'
+#' @param sex character; default is "Persons". Other values are "Male" and
+#' "Female".
+#'
+#' @param states character; default is "Australia". Other values are the full
+#' or abbreviated names of the states and self-governing territories.
+#'
+#' @param path character; default is "data/ABS". Only used if
+#' retain_files is set to TRUE. Local directory in which to save
+#' downloaded ABS time series spreadsheets.
+#'
+#' @param show_progress_bars logical; TRUE by default. If set to FALSE, progress
+#' bars will not be shown when ABS spreadsheets are downloading.
+#'
+#' @param check_local logical; FALSE by default. See \code{?read_abs}.
+#'
+#' @param retain_files logical; FALSE by default. When TRUE, the spreadsheets
+#' downloaded from the ABS website will be saved in the
+#' directory specified with 'path'.
+#'
+#' @examples
+#'
+#' # Create a tibble called 'erp' that contains the ERP index
+#' # numbers for 30 June each year for Australia.
+#' \donttest{
+#' erp <- read_erp()
+#' }
+#'
+#' @importFrom dplyr filter select tibble mutate group_by summarise arrange
+#' @importFrom tools toTitleCase
+#'
+#' @export
+
+read_erp <- function(age_range = 0:100,
+                     sex = c("Persons", "Male", "Female"),
+                     states = c("Australia", "New South Wales", "Victoria", "Queensland",
+                               "South Australia", "Western Australia", "Tasmania",
+                               "Northern Territory", "Australian Capital Territory"),
+    path = Sys.getenv("R_READABS_PATH", unset = tempdir()),
+                     show_progress_bars = TRUE,
+                     check_local = FALSE,
+                     retain_files = FALSE) {
+  if (!is.numeric(age_range)) {
+    stop("The age_range argument must be a numeric vector.")
+  }
+
+  # Check if all elements are integers
+  if (any(age_range != as.integer(age_range))) {
+    stop("All elements in age_range must be integers.")
+  }
+
+  # Check if all elements are between 0 and 100
+  if (any(age_range < 0 | age_range > 100)) {
+    stop("All elements in age_range must be between 0 and 100.")
+  }
+
+  # Restrict the 'sex' argument to the valid choices
+  sex_options <- c("Persons", "Male", "Female")
+
+  # Check if the input is a subset of the valid options
+  if (!all(sex %in% sex_options)) {
+    invalid_values <- sex[!sex %in% sex_options]
+    stop("Invalid value(s): ", paste(invalid_values, collapse = ", "),
+         ". Allowed values are: Persons, Male, Female.")
+  }
+
+  # Restrict the states argument to valid choices but include abbreviations
+  # Always return the full name if an abbreviation has been used.
+  stes <- unname(vapply(states, validate_state, FUN.VALUE = character(1)))
+
+  if (!is.logical(retain_files)) {
+    stop("`retain_files` must be either `TRUE` or `FALSE`.")
+  }
+
+  if (!is.logical(show_progress_bars)) {
+    stop("`show_progress_bars` must be either `TRUE` or `FALSE`")
+  }
+
+  if (retain_files == TRUE & !is.character(path)) {
+    stop(
+      "If `retain_files` is `TRUE`,",
+      " you must specify a valid file path in `path`."
+    )
+  }
+
+  ste_tbls <- state_lookup$tbl_n[state_lookup$full_name %in% stes]
+
+  erp_raw <- read_abs(
+    cat_no = "3101.0",
+    tables = ste_tbls,
+    retain_files = retain_files,
+    check_local = check_local,
+    show_progress_bars = show_progress_bars,
+    path = path
+  )
+
+
+  x <- erp_raw %>%
+    dplyr::mutate(
+      age = gsub("[^0-9]", "", series),
+      series_sex = gsub(".*;\\s*(Male|Female|Persons)\\s*;.*", "\\1", series),
+      state = trimws(gsub(".*,(\\s*[^,]+)$", "\\1", table_title))
+    ) %>%
+    filter(age %in% age_range,
+           series_sex %in% sex) %>%
+    group_by(date, series_sex, state) %>%
+    summarise(erp = sum(value)) %>%
+    arrange(state, series_sex, date) %>%
+    select(date, state, sex=series_sex, erp)
+
+  x
+}
+
+# Lookup table of state/territory names, abbreviations and ABS ERP table numbers
+state_lookup <- tibble(full_name=c("Australia", "New South Wales", "Victoria", "Queensland",
+                                   "South Australia", "Western Australia", "Tasmania",
+                                   "Northern Territory", "Australian Capital Territory"),
+                       abbrev=c("Aus", "NSW", "Vic", "Qld", "SA", "WA", "Tas", "NT", "ACT"),
+                       tbl_n=c(59, 51:58))
+
+# Ensure that user specified state/territory names are acceptable. Allow full
+# names or abbreviations. Throw an error if they are incorrect.
+# Always return the full name.
+validate_state <- function(state) {
+  # Define valid states and their abbreviations
+  valid_states <- state_lookup$full_name
+  valid_abbreviations <- state_lookup$abbrev
+
+  # Allow trailing periods in abbreviations
+  valid_abbreviations_with_period <- paste0(valid_abbreviations, ".")
+
+  # Combine all valid inputs
+  all_valid_inputs <- c(valid_states, valid_abbreviations, valid_abbreviations_with_period)
+
+  # Create a lookup table (map all variants to full state names)
+  state_map <- setNames(rep(valid_states, times = 3),
+                        c(valid_states, valid_abbreviations, valid_abbreviations_with_period))
+
+  # Standardize input to lowercase and remove trailing periods in case
+  # people do things like "Vic." or "Tas."
+  state_cleaned <- gsub("\\.$", "", state)  # Remove trailing period
+  state_cleaned <- tolower(state_cleaned)   # Convert to lowercase
+
+  # Create a cleaned version of the map for case-insensitive matching
+  names(state_map) <- tolower(names(state_map))
+
+  # Check if the input matches a valid state or abbreviation
+  if (!state_cleaned %in% names(state_map)) {
+    stop("Invalid state. Please use a valid state name or abbreviation.")
+  }
+
+  # Return the standard full state name
+  return(unname(tools::toTitleCase(state_map[state_cleaned])))
+}