R/gmi.R

In irinagain/iglu: Interpreting Glucose Data from Continuous Glucose Monitors

#' Calculate GMI
#'
#' @description
#' The function gmi produces GMI values in a tibble object.
#'
#' @usage
#' gmi(data)
#'
#' @param data DataFrame object with column names "id", "time", and "gl",
#' or numeric vector of glucose values.
#'
#' @return If a data.frame object is passed, then a tibble object with
#' two columns: subject id and corresponding GMI is returned. If a vector of glucose
#' values is passed, then a tibble object with just the GMI value is returned.
#' as.numeric() can be wrapped around the latter to output just a numeric value.
#'
#' @export
#'
#' @details
#' A tibble object with 1 row for each subject, a column for subject id and
#' a column for GMI values is returned. NA glucose values are
#' omitted from the calculation of the GMI.
#'
#' GMI score is calculated by \eqn{3.31 + (.02392*mean(G))}
#' where G is the vector of Glucose Measurements (mg/dL).
#'
#' @references
#' Bergenstal (2018) Glucose Management Indicator (GMI): A New Term for
#' Estimating A1C From Continuous Glucose Monitoring
#' \emph{Hormone and Metabolic Research} \strong{41} .2275-2280,
#' \doi{10.2337/dc18-1581}.
#'
#' @examples
#'
#' data(example_data_1_subject)
#' gmi(example_data_1_subject)
#'
#' data(example_data_5_subject)
#' gmi(example_data_5_subject)
#'

gmi <- function(data){
  gl = id = NULL
  rm(list = c("gl", "id"))
  data = check_data_columns(data)
  is_vector = attr(data, "is_vector")

  out = data %>%
    dplyr::group_by(id) %>%
    dplyr::summarise(
      GMI = 3.31 + (.02392*mean(gl, na.rm = TRUE) )
    )
  if (is_vector) {
    out$id = NULL
  }
  return(out)
}