R/getIDVars.R

In tabshiftr: Reshape Disorganised Messy Data

#' Extract identifying variables
#'
#' This function extracts the identifying variables from a table by applying a
#' schema description to it.
#' @param schema [\code{character(1)}]\cr the (validated) schema description of
#'   \code{input}.
#' @param input [\code{character(1)}]\cr table to reorganise.
#' @return a list per cluster with values of the identifying variables
#' @examples
#' input <- tabs2shift$clusters_nested
#' schema <- setCluster(id = "sublevel",
#'                      group = "territories", member = c(1, 1, 2),
#'                      left = 1, top = c(3, 8, 15)) %>%
#'   setIDVar(name = "territories", columns = 1, rows = c(2, 14)) %>%
#'   setIDVar(name = "sublevel", columns = 1, rows = c(3, 8, 15)) %>%
#'   setIDVar(name = "year", columns = 7) %>%
#'   setIDVar(name = "commodities", columns = 2) %>%
#'   setObsVar(name = "harvested", columns = 5) %>%
#'   setObsVar(name = "production", columns = 6)
#'
#' validateSchema(schema = schema, input = input) %>%
#'    getIDVars(input = input)
#' @importFrom checkmate assertTRUE
#' @importFrom tibble tibble
#' @importFrom purrr map set_names map_dfc
#' @importFrom dplyr row_number filter select
#' @importFrom tidyr extract unite fill
#' @importFrom tidyselect all_of
#' @export

getIDVars <- function(schema = NULL, input = NULL){

  assertTRUE(x = schema@validated)

  clusters <- schema@clusters
  nClusters <- max(lengths(clusters))

  variables <- schema@variables
  filter <- schema@filter

  idVars <- map(.x = seq_along(variables), .f = function(ix){
    # unselect those id variables that are also cluster or group id
    if(variables[[ix]]$type == "id" & !names(variables)[ix] %in% c(clusters$id, clusters$group)){
      variables[ix]
    }
  })
  idVars <- unlist(idVars, recursive = FALSE)

  if(length(idVars) != 0){

    out <- map(.x = 1:nClusters, .f = function(ix){
      vars <- NULL
      for(i in 1:length(idVars)){

        tempVar <- idVars[[i]]
        varRow <- clusters$row[ix]:(clusters$row[ix]+clusters$height[ix] - 1)

        if(!is.null(tempVar$value)){
          temp <- tibble(X = tempVar$value)
        } else {

          if(!is.null(tempVar$row[ix])){
            if(!tempVar$dist){
              # in case a row value is set, this means we deal with a variable that is not tidy ...
              temp <- input[tempVar$row[ix], tempVar$col]
              rowFilter <- NULL
              if(!is.null(filter$col)){
                colFilter <- colnames(temp)[tempVar$col %in% filter$col]
              } else {
                colFilter <- NULL
              }
            } else {
              # ... or distinct from clusters
              temp <- input[unique(tempVar$row), unique(tempVar$col)]
              rowFilter <- NULL
              colFilter <- NULL
            }
          } else {

            if(!is.null(tempVar$merge)){
              temp <- input[varRow, tempVar$col]
              rowFilter <- filter$row
              colFilter <- NULL
            } else {
              temp <- input[varRow, tempVar$col[ix]]
              rowFilter <- which(varRow %in% filter$row)
              colFilter <- NULL
            }

          }

          # split ...
          if(!is.null(tempVar$split)){
            # need to distinguish between one and several columns
            if(dim(temp)[2] == 1){
              temp <- temp %>%
                extract(col = 1, into = names(temp), regex = paste0("(", tempVar$split, ")"))
            } else {
              temp <- map_dfc(.x = seq_along(temp), .f = function(iy){
                temp %>%
                  select(all_of(iy)) %>%
                  extract(col = 1, into = names(temp)[iy], regex = paste0("(", tempVar$split, ")"))
              })
            }
          }

          # ... or merge the variable
          if(!is.null(tempVar$merge)){
            newName <- paste0(names(temp), collapse = tempVar$merge)
            temp <- temp %>%
              unite(col = !!newName, sep = tempVar$merge)
          }

          # apply a row filter ...
          if(!is.null(rowFilter)){
            temp <- temp %>%
              filter(row_number() %in% rowFilter)
          }

          # ... and column filter
          if(!is.null(colFilter)){
            temp <- temp %>%
              select(all_of(colFilter))
          }

          # and copy missing values downwards
          if(anyNA(temp[1])){
            message("filling NA-values in downwards direction in column '", names(idVars[i]),"'.")
            temp <- temp %>%
              fill(1, .direction = "down")
          }

        }

        vars <- c(vars, set_names(x = list(temp), nm = names(idVars)[i]))

      }
      return(vars)

    })



  } else {
    out <- NULL
  }

  return(out)

}