#' Generate dataset containing the metrics
#'
#' Detecting steps using a training and testing datasets
#'
#' @param pathOfTheFolder path to the source of the raw data.
#' @param feature Which feature to calculate, should be passed as a string or a vector of strings.
#' @param step indicates the step, we used {up, down and flat} as {0,1,2} respectively.
#' @param sep the field separator character - default ";".
#' @return a data frame
#' @export
generateDataSet <- function(pathOfTheFolder, features, step, sep = ";"){
rms <- function(num) sqrt(sum(num^2)/length(num));
sessions <- list.files(pathOfTheFolder, full.names = T)
all <- NULL
for (i in sessions){
extractStepsCommand <- paste("extractStepCycle('",i,"/0'",")", sep="")
steps <- eval(parse(text=extractStepsCommand))
mergeAllRawDataCommand <- paste("mergeAllRawData('",i,"/0'",",", step, ")", sep="")
mergedData <- eval(parse(text=mergeAllRawDataCommand))
z<- generateFullFeatures(mergedData, steps, features, step)
all <-rbind(all, z)
}
return(all)
}
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