R/nl_pat_fam_prices.R

In tatooheene: Technology Appraisal Toolbox for Health Economic Evaluations in the Netherlands

#' A function to download the Patient & Family Reference prices of the Dutch Costing Manual for one or multiple years
#' @description
#' `r lifecycle::badge("experimental")`
#' This function downloads the Patient & Family Reference prices of the Dutch Costing Manual for one or multiple years. The prices are available in Euro (EUR) or International Dollar (INT$).
#'
#' @param year The year of which the reference price should be downloaded, multiple years are possible, default is the whole dataset (year = "all")
#' @param category The category of prices that should be included (one or more categories), default is including all categories
#' @param unit The reference price that should be included (one or multiple reference prices),  default is including the whole data frame
#' @param currency The currency of the output of the prices. A decision can be made between EUR and INT$, the default is EUR.
#' @return A dataframe or value with the Patient & Family Reference price(s) of the Dutch Costing Manual for the specified years
#' @examples
#' # Example usage of the nl_pat_fam_prices function
#' # Calculate for 2023 with the category Transportation and the unit Car, cost per kilometer in EURO
#' nl_pat_fam_prices(year = "2022", category = "Transportation", unit = "Car, cost per kilometer")
#'
#' # Calculate for year 2022 and 2023 the unit Car, cost per kilometer in EURO
#' nl_pat_fam_prices(year = "all", unit = "Car, cost per kilometer")
#'
#' # Calculate for the year 2022 with the category Transportation in INT$
#' nl_pat_fam_prices(year = "2022", category = "Transportation", currency = "INT$")
#'
#' @keywords Generic, Costing Manual, Dutch Reference Prices, Patient & Family Prices
#' @export nl_pat_fam_prices

nl_pat_fam_prices <- function(
    year = "all",
    category = "all",
    unit = "all",
    currency = c("EUR", "INT$")){

  # Read dataframe
  df <- tatooheene::df_rp_patient

  # Input validation
  # Input validation
  year <- match.arg(year, c("all",
                            suppressWarnings(na.omit(as.numeric(colnames(df))
                            ))))

  possible_cat <- c("all", unique(df$Category))
  category <- match.arg(category, possible_cat)

  currency <- match.arg(currency)

  possible_unit <- c("all", unique(df$Unit))
  unit <- match.arg(unit, possible_unit)

  #If currency is INT$, change output
  if(currency == "INT$"){
    df_ppp <- tatooheene::nl_ppp()
    df_ppp <- df_ppp |>
      dplyr::filter(as.numeric(Year) >= 2022) |>
      tidyr::pivot_wider(names_from = "Year", values_from = "PPP")

    df_ppp <- df_ppp[, rev(seq_len(ncol(df_ppp)))]

    common_years <- intersect(colnames(df), colnames(df_ppp))

    df[, common_years] <- sweep(df[, common_years], 2, as.numeric(df_ppp[, common_years]), `/`)
  }


  # If specified, filter based on category
  if(category != "all"){
    df <- df |>
      dplyr::filter(Category %in% category)
  }

  if(unit != "all"){
    df <- df |>
      dplyr::filter(Unit %in% unit)
  }

  if(year != "all"){
    df <-  df |>
      dplyr::select("Category", "Unit", dplyr::all_of(c(as.character(year))))
  }

  return(df)
}