focused_metadata <- function(metadata, vars_in_data) {
  # metadata <- m$data
  # vars_in_data <- names(d$data)

  fields <-
    metadata[!metadata$field_type %in% c("descriptive", "checkbox") &
               metadata$field_name %in% vars_in_data,
             "field_name"]

  # Process checkbox fields
  if (any(metadata$field_type == "checkbox")) {

    # Getting base field names from checkbox fields
    vars_check <- gsub(pattern = "___(\\d+)",replacement = "", vars_in_data)

    # Processing
    checkbox_basenames <-
      metadata[metadata$field_type == "checkbox" &
                 metadata$field_name %in% vars_check,
               "field_name"]

    fields <- rbind(fields, checkbox_basenames)

  }

  # Process instrument status fields
  form_names <- unique(metadata$form_name[metadata$field_name %in% fields$field_name])

  form_complete_fields <- data.frame(
    field_name = paste0(form_names, "_complete"),
    stringsAsFactors = FALSE
  )

  fields <- rbind(fields, form_complete_fields)

  # Process survey timestamps
  timestamps <-
    intersect(vars_in_data, paste0(form_names, "_timestamp"))
  if (length(timestamps)) {
    timestamp_fields <- data.frame(
      field_name = timestamps,
      stringsAsFactors = FALSE
    )

    fields <- rbind(fields, timestamp_fields)

  }

  # Process ".*\\.factor" fields supplied by REDCap's export data R script
  if (any(grepl("\\.factor$", vars_in_data))) {
    factor_fields <-
      do.call("rbind",
              apply(fields,
                    1,
                    function(x, y) {
                      field_indices <- grepl(paste0("^", x[1], "\\.factor$"), y)
                      if (any(field_indices))
                        data.frame(
                          field_name = y[field_indices],
                          form_name = x[2],
                          stringsAsFactors = FALSE,
                          row.names = NULL
                        )
                    },
                    y = vars_in_data))

    fields <- rbind(fields, factor_fields)

  }

  metadata[metadata$field_name %in% fields$field_name,]

}

match_fields_to_form <- function(metadata, vars_in_data) {
  fields <- metadata[!metadata$field_type %in% c("descriptive", "checkbox"),
                     c("field_name", "form_name")]

  # Process instrument status fields
  form_names <- unique(metadata$form_name)
  form_complete_fields <- data.frame(
    field_name = paste0(form_names, "_complete"),
    form_name = form_names,
    stringsAsFactors = FALSE
  )

  fields <- rbind(fields, form_complete_fields)

  # Process survey timestamps
  timestamps <-
    intersect(vars_in_data, paste0(form_names, "_timestamp"))
  if (length(timestamps)) {
    timestamp_fields <- data.frame(
      field_name = timestamps,
      form_name = sub("_timestamp$", "", timestamps),
      stringsAsFactors = FALSE
    )

    fields <- rbind(fields, timestamp_fields)

  }

  # Process checkbox fields
  if (any(metadata$field_type == "checkbox")) {
    checkbox_basenames <- metadata[metadata$field_type == "checkbox",
                                   c("field_name", "form_name")]

    checkbox_fields <-
      do.call("rbind",
              apply(checkbox_basenames,
                    1,
                    function(x, y)
                      data.frame(
                        field_name = y[grepl(paste0("^", x[1], "___((?!\\.factor).)+$"), y, perl = TRUE)],
                        form_name = x[2],
                        stringsAsFactors = FALSE,
                        row.names = NULL
                      ),
                    y = vars_in_data))

    fields <- rbind(fields, checkbox_fields)

  }

  # Process ".*\\.factor" fields supplied by REDCap's export data R script
  if (any(grepl("\\.factor$", vars_in_data))) {
    factor_fields <-
      do.call("rbind",
              apply(fields,
                    1,
                    function(x, y) {
                      field_indices <- grepl(paste0("^", x[1], "\\.factor$"), y)
                      if (any(field_indices))
                        data.frame(
                          field_name = y[field_indices],
                          form_name = x[2],
                          stringsAsFactors = FALSE,
                          row.names = NULL
                        )
                    },
                    y = vars_in_data))

    fields <- rbind(fields, factor_fields)

  }

  fields

}


split_non_repeating_forms <-
  function(table, universal_fields, fields) {
    forms <- unique(fields[[2]])

    x <- lapply(forms,
                function (x) {
                  table[names(table) %in% union(universal_fields, fields[fields[, 2] == x, 1])]
                })

    structure(x, names = forms)

  }