#'
#' @title Gets the length of a vector or list
#' @description This function is similar to R function \code{length}.
#' @details The function returns the pooled length or the length of the a vector or a list for
#' each study.
#' @param x a string character, the name of a vector
#' @param type a character which represents the type of analysis to carry out.
#' If \code{type} is set to 'combine', a global variance is calculated
#' if \code{type} is set to 'split', the variance is calculated separately for each study.
#' @param checks a boolean, if TRUE (default) checks that verify elements on the server side
#' such checks lengthen the run-time so the default is FALSE and one can switch these checks
#' on (set to TRUE) when faced with some error(s).
#' @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{dataframe}, from opal datasources.
#' @return a numeric, the number of elements of the input vector or list.
#' @author Gaye, A.
#' @export
#' @examples {
#'
#' # load that contains the login details
#' data(logindata)
#'
#' # login and assign all the variables stored on the server side
#' opals <- datashield.login(logins=logindata,assign=TRUE)
#'
#' # Example 1: Get the total number of observations across all the studies for the variable 'LAB_TSC' - default behaviour
#' ds.length(x='D$LAB_TSC')
#'
#' # Example 2: Get the number of observations on each study, for the variable 'LAB_TSC'
#' ds.length(x='D$LAB_TSC', type='split')
#'
#' # clear the Datashield R sessions and logout
#' datashield.logout(opals)
#'
#' }
#'
ds.length = function(x=NULL, type='combine', checks=FALSE, datasources=NULL){
# if no opal login details are provided look for 'opal' objects in the environment
if(is.null(datasources)){
datasources <- findLoginObjects()
}
if(is.null(x)){
stop("Please provide the name of the input vector!", call.=FALSE)
}
# the input variable might be given as column table (i.e. D$x)
# or just as a vector not attached to a table (i.e. x)
# we have to make sure the function deals with each case
xnames <- extract(x)
varname <- xnames$elements
obj2lookfor <- xnames$holders
if(checks){
# check if the input object(s) is(are) defined in all the studies
if(is.na(obj2lookfor)){
defined <- isDefined(datasources, varname)
}else{
defined <- isDefined(datasources, obj2lookfor)
}
# call the internal function that checks the input object is of the same class in all studies.
typ <- checkClass(datasources, x)
# the input object must be a vector
if(typ != 'character' & typ != 'factor' & typ != 'integer' & typ != 'logical' & typ != 'numeric' & typ != 'list'){
stop("The input object must be a character, factor, integer, logical or numeric vector or a list.", call.=FALSE)
}
}
cally <- paste0("length(", x, ")")
lengths <- datashield.aggregate(datasources, as.symbol(cally))
if(type=="combine"){
pooled.length <- sum(unlist(lengths))
return(list("total.number.of.observations"=round(pooled.length,4)))
}else{
if(type=="split"){
return(lengths)
}else{
stop('Function argument "type" has to be either "combine" or "split"')
}
}
}
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