R/open_spending.ds.R

In okgreece/DescriptiveStats.OBeu: Descriptive Statistics 'OpenBudgets.eu'

#' @title
#' Read and Calculate the Basic Information for Basic Descriptive Tasks from Open Spending and Rudolf APIs.
#'
#' @description
#' Extract and analyze the input data provided from Open Spending API of OpenBudgets.eu, using the ds.analysis function.
#'
#' @usage open_spending.ds(json_data, dimensions = NULL, amounts = NULL,
#' measured.dimensions = NULL, coef.outl = 1.5, box.outliers = TRUE,
#' box.wdth = 0.15, cor.method = "pearson", freq.select = NULL)
#'
#' @param json_data The json string, URL or file from Open Spending API
#' @param dimensions The dimensions of the input data
#' @param amounts The measures of the input data
#' @param measured.dimensions The dimensions to which correspond amount/numeric variables
#' @param coef.outl Determines the length of the "whiskers" plot.
#' If it is equal to zero no outliers will be returned.
#' @param box.outliers If TRUE the outliers will be computed at the selected "coef.outl" level
#' (default is 1.5 times the Interquartile Range).
#' @param box.wdth The width level is determined 0.15 times the square root of the size of the input data.
#' @param cor.method The correlation coefficient method to compute: "pearson" (default),
#' "kendall" or "spearman".
#' @param freq.select One or more nominal variables to calculate their corresponding frequencies.
#'
#' @details
#' This function is used to read data in json format from Open Spending and Rudolf APIs., in order to implement
#' some basic descriptive tasks through \code{\link{ds.analysis}} function.
#'
#' @return A json string with the resulted parameters of the \code{\link{ds.analysis}} function.
#'
#' @author Kleanthis Koupidis
#'
#' @seealso \code{\link{ds.analysis}}
#'
#' @examples
#' # OpenBudgets.eu Dataset Example:
#' # open_spending.ds(json_data = Wuppertal_openspending,
#' #    dimensions ="functional_classification_3.Produktgruppe|date_2.Year",
#' #    amounts = "Amount")
#' @rdname open_spending.ds
#'
#' @export
#'

open_spending.ds <- function(json_data, dimensions = NULL, amounts = NULL,
                             measured.dimensions = NULL, coef.outl = 1.5, box.outliers = TRUE,
                             box.wdth = 0.15, cor.method = "pearson", freq.select = NULL) {
  linkexist <- RCurl::url.exists(json_data)
  if (linkexist) {
    # json_data = RCurl::getURL(json_data)#, ssl.verifyhost=FALSE )
  } else if (!linkexist) stop("Not valid json data input")

  dt <- jsonlite::fromJSON(json_data)
  components <- c("data", "cells")
  select.comp <- match.arg(components, names(dt), several.ok = TRUE)
  dt <- as.data.frame(dt[select.comp])
  amounts <- unlist(strsplit(amounts, "\\|"))
  dimensions <- unlist(strsplit(dimensions, "\\|"))

  if (select.comp == "data") {
    names(dt) <- gsub("data.", "", names(dt))
    variables <- c(dimensions, amounts)
    dt2 <- dt[variables]
    dt2[dimensions] <- sapply(dt2[dimensions], as.character)
  } else {
    names(dt) <- gsub("cells.", "", names(dt))
    melt <- reshape::melt.data.frame(dt)
    formula <- paste(dimensions, measured.dimensions, sep = "~")
    dt2 <- reshape::cast(melt, formula, sum,
      subset = melt$variable == amounts
    )
  }

  dt2 <- stats::na.omit(dt2)
  ds.result <- ds.analysis(dt2,
    c.out = coef.outl, outliers = box.outliers,
    box.width = box.wdth, corr.method = cor.method,
    fr.select = freq.select
  )

  ds.results <- jsonlite::toJSON(ds.result)

  return(ds.results)
}