## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
options("tibble.print_min" = 5L, "tibble.print_max" = 5L)
library(cohortBuilder)

## ----eval = FALSE-------------------------------------------------------------
# set_source.tblist <- function(dtconn, primary_keys = NULL, binding_keys = NULL,
#                               source_code = NULL, description = NULL, ...) {
#   Source$new(
#     dtconn, primary_keys = primary_keys, binding_keys = binding_keys,
#     source_code = source_code, description = description,
#     ...
#   )
# }

## ----eval = FALSE-------------------------------------------------------------
# .init_step.tblist <- function(source, ...) {
#   source$dtconn
# }

## ----eval = FALSE-------------------------------------------------------------
# .init_step.db <- function(source) {
#   purrr::map(
#     stats::setNames(source$dtconn$tables, source$dtconn$tables),
#     function(table) {
#       tbl_conn <- dplyr::tbl(
#         source$dtconn$connection,
#         dbplyr::in_schema(source$dtconn$schema, table)
#       )
#       attr(tbl_conn, "tbl_name") <- table
#       tbl_conn
#     }
#   )
# }

## ----eval = FALSE-------------------------------------------------------------
# .pre_filtering.tblist <- function(source, data_object, step_id) {
#   for (dataset in names(data_object)) {
#     attr(data_object[[dataset]], "filtered") <- FALSE
#   }
#   return(data_object)
# }

## ----eval = FALSE-------------------------------------------------------------
# .pre_filtering.db <- function(source, data_object, step_id) {
#   purrr::map(
#     stats::setNames(source$dtconn$tables, source$dtconn$tables),
#     function(table) {
#       table_name <- tmp_table_name(table, step_id)
#       DBI::dbRemoveTable(source$dtconn$conn, table_name, temporary = TRUE, fail_if_missing = FALSE)
#       attr(data_object[[table]], "filtered") <- FALSE
#       return(data_object[[table]])
#     }
#   )
# }

## ----eval = FALSE-------------------------------------------------------------
# .collect_data.tblist <- function(source, data_object) {
#   data_object
# }

## ----eval = FALSE-------------------------------------------------------------
# .collect_data.db <- function(source, data_object) {
#   purrr::map(
#     stats::setNames(source$dtconn$tables, source$dtconn$tables),
#     ~dplyr::collect(data_object[[.x]])
#   )
# }

## ----eval = FALSE-------------------------------------------------------------
# .get_stats.tblist <- function(source, data_object) {
#   dataset_names <- names(source$dtconn)
#   dataset_names |>
#     purrr::map(
#       ~ list(n_rows = nrow(data_object[[.x]]))
#     ) |>
#     stats::setNames(dataset_names)
# }

## ----eval = FALSE-------------------------------------------------------------
# .get_stats.db <- function(source, data_object) {
#   dataset_names <- source$dtconn$tables
#   dataset_names |>
#     purrr::map(
#       ~ list(
#         n_rows = data_object[[.x]] |>
#           dplyr::summarise(n = n()) |>
#           dplyr::collect() |>
#           dplyr::pull(n) |>
#           as.integer()
#       )
#     ) |>
#     stats::setNames(dataset_names)
# }

## ----eval = FALSE-------------------------------------------------------------
# .run_binding.tblist <- function(source, binding_key, data_object_pre, data_object_post, ...) {
#   binding_dataset <- binding_key$update$dataset
#   dependent_datasets <- names(binding_key$data_keys)
#   active_datasets <- data_object_post |>
#     purrr::keep(~ attr(., "filtered")) |>
#     names()
# 
#   if (!any(dependent_datasets %in% active_datasets)) {
#     return(data_object_post)
#   }
# 
#   key_values <- NULL
#   common_key_names <- paste0("key_", seq_along(binding_key$data_keys[[1L]]$key))
#   for (dependent_dataset in dependent_datasets) {
#     key_names <- binding_key$data_keys[[dependent_dataset]]$key
#     tmp_key_values <- collapse::funique(data_object_post[[dependent_dataset]][, key_names, drop = FALSE]) |>
#       stats::setNames(common_key_names)
#     if (is.null(key_values)) {
#       key_values <- tmp_key_values
#     } else {
#       key_values <- dplyr::inner_join(key_values, tmp_key_values, by = common_key_names)
#     }
#   }
# 
#   data_object_post[[binding_dataset]] <- dplyr::inner_join(
#     switch(
#       as.character(binding_key$post),
#       "FALSE" = data_object_pre[[binding_dataset]],
#       "TRUE" = data_object_post[[binding_dataset]]
#     ),
#     key_values,
#     by = stats::setNames(common_key_names, binding_key$update$key)
#   )
#   if (binding_key$activate) {
#     attr(data_object_post[[binding_dataset]], "filtered") <- TRUE
#   }
# 
#   return(data_object_post)
# }

## ----eval = FALSE-------------------------------------------------------------
# .get_attrition_count.tblist <- function(source, data_stats, dataset, ...) {
#   data_stats |>
#     purrr::map_int(~.[[dataset]][["n_rows"]])
# }

## ----eval = FALSE-------------------------------------------------------------
# .get_attrition_label.tblist <- function(source, step_id, step_filters, dataset, ...) {
#   pkey <- source$primary_keys
#   binding_keys <- source$binding_keys
#   if (step_id == "0") {
#     if (is.null(pkey)) {
#       return(dataset)
#     } else {
#       dataset_pkey <- .get_item(pkey, "dataset", dataset)[1L][[1L]]$key
#       if (is.null(dataset_pkey)) return(dataset)
#       return(glue::glue("{dataset}\n primary key: {paste(dataset_pkey, collapse = ', ')}"))
#     }
#   }
#   filters_section <- step_filters |>
#     purrr::keep(~.$dataset == dataset) |>
#     purrr::map(~get_attrition_filter_label(.$name, .$value_name, .$value)) |>
#     paste(collapse = "\n")
#   bind_keys_section <- ""
#   if (!is.null(binding_keys)) {
#     dependent_datasets <- .get_item(
#       binding_keys, attribute = "update", value = dataset,
#       operator = function(value, target) {
#         value == target$dataset
#       }
#     ) |>
#       purrr::map(~names(.[["data_keys"]])) |>
#       unlist() |>
#       collapse::funique()
#     if (length(dependent_datasets) > 0L) {
#       bind_keys_section <- glue::glue(
#         "\nData linked with external datasets: {paste(dependent_datasets, collapse = ', ')}",
#         .trim = FALSE
#       )
#     }
#   }
#   gsub(
#     "\n$",
#     "",
#     glue::glue("Step: {step_id}\n{filters_section}{bind_keys_section}")
#   )
# }

