Remove provisional fns

Author: pratikunterwegs

NAMESPACE

@@ -3,7 +3,6 @@
 export(basic_burden_sanity)
 export(check_demography_alignment)
 export(file_dict_colnames)
-export(impact_check)
 export(plot_age_patterns)
 export(plot_compare_demography)
 export(plot_coverage_set)
@@ -18,11 +17,9 @@ export(prep_plot_fvp)
 export(prep_plot_global_burden)
 export(theme_vimc)
 export(theme_vimc_noxaxis)
-export(transform_coverage_fvps)
 export(validate_complete_incoming_files)
 export(validate_file_dict_template)
 export(validate_template_alignment)
-importFrom(dplyr,.data)
 importFrom(ggplot2,'%+replace%')
 importFrom(ggplot2,aes)
 importFrom(ggplot2,facet_grid)

R/burden_diagnositics.R

@@ -203,7 +203,7 @@ validate_complete_incoming_files <- function(
 
 #' Check incoming burden set against template
 #'
-#' @description
+#' @description Identify extra and missing columns and rows in burden data.
 #'
 #' @param burden_set A `<data.frame>` of modeller-provided burden-set data.
 #'
@@ -389,159 +389,3 @@ basic_burden_sanity <- function(burden) {
 
   mes
 }
-
-#' @title
-#'
-#' @description
-#' A short description...
-#'
-#' @param coverage
-#'
-#' @param wpp
-#'
-#' @return
-#'
-#' @examples
-#' # example code
-#'
-#' @importFrom dplyr .data
-#'
-#' @keywords diagnostics
-#'
-#' @export
-transform_coverage_fvps <- function(coverage, wpp) {
-  # TODO: checks on coverage
-  # TODO: checks on wpp
-
-  cols_to_select <- c("age_from", "age_to", "gender")
-  todo_list <- dplyr::select(
-    coverage,
-    {{ cols_to_select }}
-  )
-  todo_list <- dplyr::distinct(todo_list)
-  todo_list <- dplyr::mutate(
-    todo_list,
-    job = seq_along(.data$gender)
-  )
-
-  # TODO: THIS NEEDS TO BE CLEANED UP
-  # TODO: clarify structure of `coverage` and mapping of gender to age
-  pop_all <- list()
-  for (i in seq_along(todo_list$age_from)) {
-    x <- dplyr::filter(
-      wpp,
-      dplyr::between(.data$age, todo_list$age_from[i], todo_list$age_to[i]),
-      .data$gender == todo_list$gender[i]
-    )
-    x <- dplyr::summarise(
-      x,
-      target_wpp = sum(.data$value),
-      .by = c("country", "year")
-    )
-    x <- dplyr::mutate(
-      x,
-      job = todo_list$job[i]
-    )
-
-    pop_all[[i]] <- x
-  }
-  pop_all <- dplyr::bind_rows(pop_all)
-
-  # TODO: add comments or explain in fn docs
-  d <- dplyr::left_join(
-    coverage,
-    pop_all,
-    by = c("country", "year")
-  )
-  d <- dplyr::mutate(
-    d,
-    target = dplyr::coalesce(
-      .data$target,
-      .data$target_wpp # replace NAs in target with target_wpp
-    ),
-    fvps = .data$target * .data$coverage,
-    fvps_adjusted = pmin(
-      .data$target_wpp,
-      .data$fvps
-    ),
-    target_adjusted = pmin(
-      .data$target_wpp,
-      .data$target
-    ),
-    coverage_adjusted = .data$fvps_adjusted / .data$target_adjusted
-  )
-  d[["target_wpp"]] <- NULL
-
-  d
-}
-
-# TODO: fill out fn docs
-#' @title
-#'
-#' @description
-#'
-#' @param burden
-#'
-#' @param scenario_order
-#'
-#' @return
-#'
-#' @importFrom dplyr .data
-#'
-#' @examples
-#'
-#' @keywords diagnostics
-#'
-#' @export
-impact_check <- function(burden, scenario_order) {
-  # TODO: input checks
-  scenario_cols <- c("scenario", "scenario_order")
-  scenario_order <- dplyr::select(scenario_order, {{ scenario_cols }})
-
-  d <- dplyr::summarise(
-    burden,
-    millions = sum(.data$value) / 1e6,
-    .by = c("scenario", "burden_outcome"),
-    .groups = "drop" # probably unnecessary as grouping is temporary
-  )
-
-  d <- dplyr::left_join(
-    d,
-    scenario_order,
-    by = "scenario"
-  )
-
-  d <- dplyr::mutate(
-    d,
-    scenario_order = glue::glue("{.data$scenario_order}:{.data$scenario}")
-  )
-
-  d$scenario <- NULL
-
-  d <- tidyr::pivot_wider(
-    d,
-    names_from = "scenario_order",
-    values_from = "million"
-  )
-
-  # TODO: CLEAN THIS UP
-  for (i in 2:nrow(scenario_order)) {
-    for (j in 1:(i - 1)) {
-      if (any(d[i + 1] > d[j + 1])) {
-        cat(sprintf(
-          "&nbsp;&nbsp;&nbsp;&nbsp;**Warning**: provided less disease burden in lower coverage scenario (%s) compared to higher coverage scenario (%s).</span>",
-          names(d)[j + 1],
-          names(d)[i + 1]
-        ))
-        cat("<br>")
-      } else {
-        cat(sprintf(
-          "&nbsp;&nbsp;&nbsp;&nbsp;**PASS**: Provided higher disease burden in scenarios with fewer FVPs.</span>"
-        ))
-        cat("<br>")
-      }
-    }
-  }
-
-  d
-}