R/standardising.R

In amarshall1/prescribeR: Quantify Exposure Based On Routine Prescribing Data

#' Standardise Input Data Field Names and Formats
#'
#' Adjusts the field names and data formats of prescribing and events data to
#' create a data frame in a format suited for use with the other functions in
#' this package - mostly called internally by the other functions in this
#' package, but can be used directly  by the user to reduce the number of
#' arguments which need to be passed to other functions as these standardised
#' values are inluded as the default values in their code.
#'
#' @param df a data frame containing the data of interest
#' @param patient_id_col a string, the name of the column in \code{df}
#'   containing the patient IDs
#' @param drug_id_col a string, the name of the column in \code{df} containing
#'   the drug IDs
#' @param presc_date_col a string, the name of the column in \code{df}
#'   containing the prescption date
#' @param dd_disp_col a string, the name of the column in \code{df} containing
#'   the number of daily doses dispensed
#' @param qty_disp_col a string, the name of the column in \code{df} containing
#'   the quantity of drug dispensed
#' @param qty_per_day_col a string, the name of the column in \code{df}
#'   containing the quantity of drug to be taken per day
#' @param ev_date_1_col a string, the name of the column containing the first
#'   event dates
#' @param ev_date_2_col a string, the name of the column containing the second
#'   event dates if present
#' @param date_format a string, the format of the dates in \code{df}
#'
#' @return a data frame with standardised field names and formats
#'
#' @export
#'
#' @importFrom magrittr %>%
#'
#' @examples
#' tidy_synth <- tidy_presc(df = synth_presc, drug_id_col = "approved_name",
#'  dd_disp_col = "ddd_dispensed", qty_disp_col = "qty_dispensed",
#'  date_format = "%Y-%m-%d")
#' tidy_synth <- tidy_presc(synth_events, patient_id = "patient_id",
#' ev_date_1 = "start_date")
#'

tidy_presc <- function(df,
                       patient_id_col = NULL,
                       drug_id_col = NULL,
                       presc_date_col = NULL,
                       dd_disp_col = NULL,
                       qty_disp_col = NULL,
                       qty_per_day_col = NULL,
                       ev_date_1_col = NULL,
                       ev_date_2_col = NULL,
                       date_format) {
  df1 <- df
  if (!is.null(patient_id_col)) {
    df1 <- df1 %>%
      dplyr::rename(patient_id = patient_id_col)
    df1$patient_id <- as.character(df1$patient_id)
  }
  if (!is.null(drug_id_col)) {
    df1 <- df1 %>%
      dplyr::rename(drug_id = drug_id_col)
    df1$drug_id <- as.character(df1$drug_id)
  }
  if (!is.null(presc_date_col)) {
    df1 <- df1 %>%
      dplyr::rename(presc_date_x = presc_date_col)
    df1$presc_date_x <-
      as.Date(df1$presc_date_x, format = date_format)
  }
  if (!is.null(dd_disp_col)) {
    df1 <- df1 %>%
      dplyr::rename(dd_disp = dd_disp_col)
    df1$dd_disp <- as.numeric(df1$dd_disp)
  }
  if (!is.null(qty_disp_col)) {
    df1 <- df1 %>%
      dplyr::rename(qty_disp = qty_disp_col)
    df1$qty_disp <- as.numeric(df1$qty_disp)
  }
  if (!is.null(qty_per_day_col)) {
    df1 <- df1 %>%
      dplyr::rename(qty_per_day = qty_per_day_col)
    df1$qty_per_day <- as.numeric(df1$qty_per_day)
  }
  if (!is.null(ev_date_1_col)) {
    df1 <- df1 %>%
      dplyr::rename(ev_date_1 = ev_date_1_col)
    df1$ev_date_1 <- as.Date(df1$ev_date_1, format = date_format)
  }
  if (!is.null(ev_date_2_col)) {
    df1 <- df1 %>%
      dplyr::rename(ev_date_2 = ev_date_2_col)
    df1$ev_date_2 <- as.Date(df1$ev_date_2, format = date_format)
  }
  return(df1)
}