R/extract.R

Defines functions .has_info_in_record extract_info extract_completes

Documented in extract_completes extract_info .has_info_in_record

#' Extract Completion Status
#'
#' Extract the completion status for each participant and each instrument in
#' the given raw report data. This isn't terribly useful, but could be helpful
#' when trying to exclude any data entry that isn't finished.
#'
#' @param report_data Raw report data from get_report or importing from .csv
#' @param record_id_col column name for the record id, defaults to the lab's record_id
#'
#' @return A tibble containing the completion status value for each instrument
#' for every participant in the given report
#' @export
extract_completes <- function(report_data, record_id_col = "record_id") {
  complete_regex <- paste0(record_id_col, "|(_instance|_complete$)")
  complete_vars <- report_data[grep(complete_regex, colnames(report_data))]

  dplyr::filter(
    tidyr::pivot_longer(complete_vars,
                        cols = tidyselect::contains("_complete"),
                        names_to = "instrument_complete"
    ),
    !is.na(value)
  )
}



#' Extract Info Variables from Instrument
#'
#' Extract the information from the top of each instrument such as age & tsonset.
#' This is valuable to filter which records you actually want, then you
#' can filter each instrument via joining operations
#'
#' @param report_data Dataframe, the raw report data from get_report or importing from .csv
#' @param lookup Dataframe, instrument Name-Prefix lookup table from generate_table or read in from a .csv
#' @param make_uid Logical, should a unique id from the record id, instrument, and instance number be made?
#' @param record_id_col String, name of the record id column, defaults to record_id
#' @param filter_by String, name of the column to filter out NAs, defaults to user
#'
#' @return A dataframe with all of the "info" data from each instrument
#' @export
#' @importFrom rlang `!!` `!!!`
extract_info <- function(report_data,
                         lookup = anrlab::anrlab_instruments,
                         make_uid = TRUE,
                         record_id_col = "record_id",
                         filter_by = "user") {
  # get only the record id, instance number, and info columns
  info_regex <- paste0(record_id_col, "|(_instance$)|(_info_)")
  info_cols <- colnames(report_data)[grep(info_regex, colnames(report_data))]
  info_vars <- report_data[info_cols]

  # shorten column names for readability
  info_cols <- gsub("_info_", "_", info_cols)
  colnames(info_vars) <- info_cols

  # pivot the instrument information. There will be a lot of NAs so we filter
  # out any that don't have age information since it's an automatic calculation
  # that's only computed when that instrument is added to a record
  pivot_cols <- info_cols[grep(info_regex, info_cols, invert = T)]
  output <-
    tidyr::pivot_longer(
      info_vars,
      cols = tidyselect::all_of(pivot_cols),
      names_to = c("instrument_prefix", ".value"),
      names_pattern = "(.+)_(.+)$"
    )
  output <- output[.has_info_in_record(output, pivot_cols),]


  if (NA %in% output[["date"]]) {
    warning("There are missing dates, please verify dates are entered in redcap.")
  }

  output <- dplyr::inner_join(output,
                              lookup[,c('instrument_name','instrument_prefix')],
                              by = "instrument_prefix")
  if (make_uid) {
    output <- dplyr::mutate(output,
                            uid = paste(!!as.name(record_id_col),
                                        redcap_repeat_instance,
                                        instrument_prefix,
                                        sep = "_"
                            ),
                            .before = 1
    )
  }
  if ('age' %in% colnames(output)) {
    if (is.character(output[["age"]])) {
      output <- dplyr::mutate(output,
                              age = as.numeric(age),
                              tsonset = as.numeric(age),
                              date = as.Date(date)
      )
    }
  }

  dplyr::rename(output, redcap_repeat_instrument = instrument_name)
}

#' Check if record contains any info variables
#'
#' Given a dataframe that was pivoted using info variables stored in pivot_cols,
#' return only the rows that have at least 1 info variable filled out. Essentially,
#' check each row and return TRUE if any info column is not NA. This is necessary
#' because not all info variables will be included in every report.
#'
#' @param record_df Pivoted dataframe with extracted info variables
#' @param pivot_cols Which info columns the df was pivoted by
#'
#' @return Logical vector of which rows to keep
.has_info_in_record <- function(record_df, pivot_cols) {
  included_vars <- unique(gsub("^[^_]+_", "", pivot_cols))

  vapply(seq_len(nrow(record_df)),
         function(row_idx)
           any(
             vapply(included_vars,
                    function(col_idx)
                      !is.na(record_df[[row_idx,col_idx]]),
                    TRUE)
           ),
         TRUE
  )
}
tsostarics/anrlab documentation built on Dec. 13, 2021, 10:11 a.m.