R/ds.min.R

In paularaissa/distStatsClient: Distributed Multiple Imputations

#-------------------------------------- HEADER --------------------------------------------#
#' @title Minimum
#' @description Computes the minimum value among data nodes or for each data node.
#' @details Return the minimum of all the values present in their arguments, as integer if all are logical or integer,
#' as double if all are numeric, and character otherwise.
#'
#' The minimum of a numeric empty set is +Inf which ensures transitivity.
#' For numeric x ds.min(x) == +Inf whenever ds.length(x) == 0 (after removing missing values if requested).
#'
#' By definition the min of a numeric vector containing an NaN is NaN, except that the min of any vector containing
#' an NA is NA even if it also contains an NaN. Note that ds.min(NA, Inf) == NA even though the minimum would be Inf whatever
#' the missing value actually is.
#'
#' Character versions are sorted lexicographically. The min of an empty character vector is defined to be character NA.
#'
#' It is a wrapper for the server side function \code{\link{getMin}}.
#' @param x a character, the name of study variable.
#' @param datasources a list of opal object(s) obtained after login in to opal servers;
#' these objects hold also the data assign to R, as \code{data frame}, from opal datasources.
#' @return return a numeric value.
#' @author Paula Raissa Costa e Silva
#' @section Dependencies:
#' \code{\link{getMin}}
#' @export
#' @examples {
#' ds.min('D$birth_weight')
#' }

ds.min <- function(x=NULL, datasources=NULL) {

  if(is.null(x)){
    stop("Please provide the name of the input vector!", call. = FALSE)
  }

  if(is.null(datasources)){
    datasources <- findLoginObjects()
  }
  xnames <- extract(x)
  varname <- xnames$elements
  obj2lookfor <- xnames$holders
  if(is.na(obj2lookfor)) {
    defined <- isDefined(datasources, varname)
  } else{
    defined <- isDefined(datasources, obj2lookfor)
  }
  #number of studies
  num.sources <- length(datasources)
  for(i in 1:num.sources){
    cally <- call("getMin", x)
    result.min <- opal::datashield.aggregate(datasources, cally)
  }

  aux <- c()
  result.min <- list()
  for(value in result.min){
    aux <- rbind(aux, value)
  }
  min <- min(aux)

  return(min)

}