R/align_msd.R

In USAID-OHA-SI/tameDP: Import targets and PLHIV data from COP Target Setting Tool (formerly Data Pack)

#' Align MSD extract to disaggregates in Target Setting Tool
#'
#' This function pulls in a PSNUxIM MSD and datapack filepath to align the MSD extract
#' to the indicators and disaggregates in the datapack, as well as historic results and targets.
#' This function also addresses when OUs set targets at a higher level than PSNU for alignment.
#'
#' @param msd_path path to PSNUxIM extract
#'
#' @export
#'
#' @examples
#'  \dontrun{
#'    df_msd <- align_msd_disagg(msd_path = msd_path)
#' }
#'

align_msd_disagg <- function(msd_path) {

  #import MSD
  df_msd <- gophr::read_psd(msd_path)

  #semi join with MSD mapping to limit to MSD disaggs
  df_align <- df_msd %>%
    dplyr::semi_join(mer_historic_disagg_mapping_2024, by = c("indicator", "numeratordenom", "standardizeddisaggregate")) %>%
    gophr::clean_indicator()

  return(df_align)

}


#' #' Align MSD age bands to collapsed age bands in Target Setting Tool
#' #'
#' #' @param df dataframe from `align_msd_disagg` to pass through
#' #' @param collapse logical - if TRUE, summarizes across entire dataframe to collapse age bands.
#' #' If false, multiple observations with same age bands will occur. Note: if `collapse = TRUE`, processing speed will be long.
#' #'
#' #' @export
#' #'
#' #' @examples
#' #'  \dontrun{
#' #'    df_final <- align_ageband(df_align, collapse = FALSE)
#' #' }
#' #'
#' align_ageband <- function(df, collapse = FALSE) {
#'
#'
#'   #add collapsed age band crosswalk - collapsing takes a LONG time to run
#'   #VMMC 10-14 is NA
#'   #GEND_GBV is all NA
#'   #HTS, HTS_POS, HTS_INDEX, TB_ART,& TB_STAT are NA for <1
#'   #OVC_SERV, AGYW_PREV, PMTCT_EID, TB_PREV use ageasentered instead of collapsed age bands
#'
#'   df_final <- df %>%
#'     dplyr::left_join(tameDP::age_band_crosswalk, by = c("ageasentered" = "age_msd")) %>%
#'     dplyr::mutate(age_dp = ifelse(indicator == "VMMC_CIRC" & ageasentered == "10-14", NA, age_dp)) %>%
#'     dplyr::mutate(age_dp = ifelse(indicator == "GEND_GBV", NA, age_dp)) %>%
#'     dplyr::mutate(age_dp = ifelse(indicator %in% c("HTS_TST", "HTS_TST_POS", "HTS_INDEX",
#'                                                    "TB_ART", "TB_STAT", "TB_STAT_D") & ageasentered == "<01", NA, age_dp)) %>%
#'     dplyr::mutate(age_dp = ifelse(indicator %in% c("OVC_SERV", "AGYW_PREV", "PMTCT_EID", "TB_PREV", "TB_PREV_D", "TX_TB_D"), ageasentered, age_dp)) %>%
#'     dplyr::select(-ageasentered) %>%
#'     dplyr::select(-c(funding_agency, mech_code)) %>%
#'     dplyr::relocate(age_dp, .after = 9) %>%
#'     dplyr::relocate(dplyr::any_of(c("cumulative", "targets")), .after = 13) %>%
#'     #relocate(funding_agency, .after = 15) %>%
#'     dplyr::rename(ageasentered = age_dp)
#'
#'   if(collapse == TRUE) {
#'
#'     df_final <- df_final %>%
#'       dplyr::group_by(dplyr::across(-c(cumulative, targets))) %>%
#'       # group_by_all() %>%
#'       # group_by(indicator, fiscal_year, standardizeddisaggregate, age_dp) %>%
#'       dplyr::summarise(dplyr::across(c(cumulative, targets), sum, na.rm = TRUE), .groups = "drop")
#'
#'   }
#'
#'   return(df_final)
#'
#' }