ready_clin <- function(data) {
  data |>
    correct_na() |>
    dplyr::mutate(
      reg_smoker = dplyr::case_match(
        reg_rygning, "1" ~ TRUE,
        c("2", "3", "4") ~ FALSE,
        "9" ~ NA
      ),
      reg_cohabiting = dplyr::case_match(
        reg_civil, "1" ~ TRUE,
        c("2", "3") ~ FALSE,
        "9" ~ NA
      ),
      # Living alone defined as not together with somebody
      reg_alone = dplyr::case_match(
        reg_civil, "1" ~ FALSE,
        c("2", "3") ~ TRUE,
        "9" ~ NA
      ),
      reg_more_alc = dplyr::case_match(
        reg_alkohol, "1" ~ FALSE,
        "2" ~ TRUE,
        "9" ~ NA
      ),
      reg_female = sex == "Kvinde",
      dplyr::across(
        .cols = c(
          "reg_hyperten",
          "reg_diabetes",
          "reg_atriefli",
          "reg_perifer_arteriel",
          "reg_tidl_tci",
          "reg_ami"
        ),
        ~ dplyr::case_match(
          .x, "1" ~ TRUE,
          "2" ~ FALSE,
          "9" ~ NA
        )
      ),
      dplyr::across(
        .cols = c(
          "reg_trombolyse",
          "reg_trombektomi"
        ),
        ~ dplyr::case_match(
          .x, "1" ~ TRUE,
          c("3", "4") ~ FALSE,
          "9" ~ NA
        )
      ),
      reg_any_perf = reg_trombolyse | reg_trombektomi
    ) |>
    bmi_calc()
}

#' Formatting the complete data set
#'
#' @param data the merged raw data set
#'
#' @return tibble
#' @examples
#' ds <- targets::tar_read(df_all_data) |>
#'   data_formatting() |>
#'   subset_df("mdi")
#' ds |> skimr::skim()
#' ds |> View()
data_formatting <- function(data) {
  to_logical <- grep(match_str_grepl(c("missings", "incompletes")), names(data))

  suppressWarnings(
    data |>
      correct_na() |>
      dplyr::mutate(dplyr::across(all_of(to_logical), ~ .x == "TRUE")) |>
      dplyr::mutate(
        # This uses the work-corrected score
        # pase_0 = dplyr::if_else(pase_score_missings_w_0 | is.na(talos_pase10_0), NA, pase_score_sum_w_0),
        # pase_4 = dplyr::if_else(pase_score_missings_w_4 | is.na(talos_pase10_4), NA, pase_score_sum_w_4),
        # Below is the plain PASE scor used according to the manual used with TALOS
        pase_0 = dplyr::if_else(pase_score_missings_0, NA, pase_score_sum_0),
        pase_4 = dplyr::if_else(pase_score_missings_4, NA, pase_score_sum_4),
        who_0 = as.numeric(talos_who07_0),
        who_4 = as.numeric(talos_who07_4),
        mrs_0 = factor(substr(talos_mrs01_0, 1, 1), ordered = FALSE),
        mrs_0_above0 = (as.numeric(mrs_0) - 1) > 0,
        mrs_4 = factor(substr(talos_mrs01_4, 1, 1), ordered = FALSE),
        mrs_4_above1 = (as.numeric(mrs_4) - 1) > 1,
        mdi_4 = as.numeric(talos_mdi12_4),
        mfi_gen_4 = as.numeric(talos_mfi_gen_4),
        nihss_0 = as.numeric(talos_nihss16_0),
        # soc_status = dst_SOC_STATUS_KODE.latest,
        # fam_indk = cut(dst_FAMAEKVIVADISP_13.5.mean,
        #                breaks = quantile(dst_FAMAEKVIVADISP_13.5.mean, probs = seq(0, 1, 1 / 3), na.rm = TRUE),
        #                ordered_results = FALSE,
        #                labels = c("low", "medium", "high"),
        #                include.lowest = TRUE
        # ),
        # fam_indk_bin = cut(dst_FAMAEKVIVADISP_13.5.mean,
        #                    breaks = quantile(dst_FAMAEKVIVADISP_13.5.mean, probs = seq(0, 1, 1 / 2), na.rm = TRUE),
        #                    ordered_results = FALSE,
        #                    labels = c("low", "high"),
        #                    include.lowest = TRUE
        # ),
        # fam_indk_hl=forcats::fct_rev(fam_indk),
        # fam_indk_high = dplyr::if_else(fam_indk_bin=="high",TRUE,FALSE),
        # fam_indk_low = dplyr::if_else(fam_indk_bin=="low",TRUE,FALSE),
        # edu_level = factor(dst_ISCED_lvl, ordered = FALSE, levels = c("low", "medium", "high")),
        # edu_high = dplyr::if_else(dst_ISCED_bin=="high",TRUE,FALSE),
        # edu_low = dplyr::if_else(dst_ISCED_lvl=="low",TRUE,FALSE),
        # edu_level_hl=forcats::fct_rev(edu_level),
        rtreat_placebo = rtreat == "Placebo"
      ) #|>
    # group_soc_status() |>
    # define_status_time()
  )
}

#' Store of variable names for data sub-setting
#'
#' @return list
#' @examples
#' define_variables()
define_variables <- function() {
  list(
    clin = c(
      "age",
      "reg_female",
      "nihss_0",
      "reg_trombolyse",
      "reg_trombektomi",
      # "rtreat",
      "rtreat_placebo"
    ),
    lifestyle = c(
      "pase_0",
      "pase_4",
      "reg_alone",
      "reg_bmi",
      # "reg_hojde",
      # "reg_vaegt_alt",
      "reg_smoker",
      "reg_more_alc",
      "reg_hyperten",
      "reg_diabetes",
      "reg_tidl_tci",
      "reg_atriefli",
      "reg_ami",
      "reg_perifer_arteriel"
    ),
    ses = c(
      # "soc_status",
      # "soc_status_work",
      # "soc_status_nowork",
      # "fam_indk",
      # "fam_indk_hl",
      # "fam_indk_high",
      # "fam_indk_low",
      # "edu_level",
      # "edu_high",
      # "edu_low",
      # "edu_level_hl"
    ),
    assess.events = c(
      "who_4",
      "mdi_4",
      "mrs_4_above1",
      "mfi_gen_4",
      "time",
      "status",
      "event.include"
    ),
    assess.pred = c(
      "who_0",
      "mrs_0_above0"
    ),
    extra = c(
      "soc_status",
      "pase_0",
      "pase_4"
    )
  )
}


#' Wrapper to print summary table with extended info
#'
#' @param data formatted and subset data set
#' @param by.var stratify by
#'
#' @return
#' @examples
print_table_summary <- function(data, by.var = "pase_change", adds = TRUE, missing="ifany") {
  out <- data |>
    pragmatic_imputation(vec=c("reg_trombolyse", "reg_trombektomi","reg_any_perf")) |> 
    # pase_cutter(drop.pase = TRUE) |>
    gtsummary::tbl_summary(
      missing = missing,
      by = tidyselect::all_of(by.var) # ,
      # value = list(where(is.logical) ~ TRUE),
      # type = list(gtsummary::all_continuous() ~ "continuous2"),
      # statistic = list(gtsummary::all_continuous() ~ c(
      #   # "{N_nonmiss} ({p_nonmiss}%)",
      #   "{median} ({p25}, {p75})",
      #   # "{min}, {max}",
      #   "{mean} ({sd})"#,
      #   # "{N_miss} ({p_miss}%)"
      # )#,
      # # gtsummary::all_categorical() ~ c(
      # #   "{N_obs} ({p_nonmiss}%)"#,
      # #   # "{N_miss} ({p_miss})"
      # # )
      # )
    ) #|>
    # gtsummary::add_n()

  if (adds) {
    out |>
      gtsummary::add_overall() #|>
    # gtsummary::add_p()
  } else {
    out
  }
}

#' Title
#'
#' @param data
#'
#' @return
#' @export
#'
#' @examples
#' targets::tar_read(df_all_data_formatted) |> View()
#' targets::tar_read(df_pred_data) |>
#'   dplyr::transmute(stRoke::quantile_cut(pase_0, 4, group.names = 1:4), soc_status_work, fam_indk, edu_level) |>
#'   summary_tblone()
summary_tblone <- function(data, by = names(data)[1], adds = FALSE, missing="ifany") {
  # data <- targets::tar_read(df_pred_data)
  data |>
    labelling_data() |>
    # prediction_ready() |>
    # dplyr::select(-reg_bmi) |>
    # pase_cutter(drop.pase = TRUE) |>
    # dplyr::filter(!is.na(pase_change)) |>
    # dplyr::mutate(pase_change=forcats::fct_rev(pase_change)) |>
    print_table_summary(by.var = by, adds = adds, missing=missing)
}

#' Creating a truthful stratified table for predictions
#'
#' @param data data frame
#'
#' @return list
#' @export
#'
#' @examples
#' targets::tar_read(df_pred_data) |> true_pred_sum_plot()
#' data <- pred_data
true_pred_sum_plot <- function(data,by="pase_change") {
  true_sum <- data |>
    pase_cutter(drop.pase = TRUE) |>
    dplyr::mutate(pase_change = forcats::fct_rev(pase_change))
    

  list( # true_sum,
    true_sum |>
      (function(.x) {
        split(.x, .x$pase_change %in% c("Persistently low", "Increase"))
      })() |>
      purrr::map(function(.y) {
        .y |> dplyr::mutate(pase_change = factor(pase_change))
      })
  ) |>
    purrr::list_flatten() |>
    purrr::map(summary_tblone, by = by) |>
    purrr::map(micRo::mask_micro_summary) |>
    gtsummary::tbl_merge()
}