#' Download REDCap data
#'
#' Wrapper function for using REDCapR::redcap_read and REDCapRITS::REDCap_split
#' including some clean-up. Works with longitudinal projects with repeating
#' instruments.
#' @param uri REDCap database uri
#' @param token API token
#' @param records records to download
#' @param fields fields to download
#' @param events events to download
#' @param forms forms to download
#' @param raw_or_label raw or label tags
#' @param generics vector of auto-generated generic variable names to
#' ignore when discarding empty rows
#' @param ... extra parameters for internal REDCapR::redcap_read
#'
#' @return list of instruments
#' @importFrom REDCapR redcap_metadata_read redcap_read
#' @export
#'
#' @examples
#' # Examples will be provided later
read_redcap_tables <- function(uri,
                               token,
                               records = NULL,
                               fields = NULL,
                               events = NULL,
                               forms = NULL,
                               raw_or_label = "label",
                               generics = c(
                                 "record_id",
                                 "redcap_event_name",
                                 "redcap_repeat_instrument",
                                 "redcap_repeat_instance"
                               ),
                               ...) {
  # Notes to self: Based on the metadata, this functionality could be
  # introduced without using the REDCapRITS package.. To be tried..
  #
  # This does not handle repeated instruments!! This should be implemented.

  d <- REDCapR::redcap_read(
    redcap_uri = uri,
    token = token,
    fields = fields,
    events = events,
    forms = forms,
    records = records,
    raw_or_label = raw_or_label,
    ...
  )

  m <-
    REDCapR::redcap_metadata_read (redcap_uri = uri, token = token)

  l <- REDCap_split(d$data,
                    m$data[m$data$field_name %in% names(d$data), ],
                    forms = "all")

  lapply(l, function(i) {
    if (ncol(i) > 2) {
      s <- data.frame(i[, !colnames(i) %in% generics])
      i[!apply(is.na(s), MARGIN = 1, FUN = all), ]
    } else {
      i
    }
  })

}