R/aggregate_data_by_class.R

In NOAA-EDAB/neusCatch: Multispecies length expansion of commercial catch

#' Aggregates rows of data by a given variable
#'
#'The landings or length data are aggregated over a variable (for example MARKET_CODE) conditional on another variable (eg NEGAR == "050")
#'
#'@param data tibble. Data set to aggregate
#'@param variable Character string. Name of the variable to aggregate over
#'@param classes Character vector. Two codes (from the variable) to aggregate (Assign 1st to 2nd)
#'@param conditionalOn Character Vector. Name and value of variable to condition the aggregation on eg. c("NEGEAR","050"). Default = NULL
#'@param dataset Character string. Denoting the type of data set. "landings" or "lengths
#'
#'@return tibble
#'\item{data}{The same form and content as the input tibble, just aggregated}
#'
#'@noRd


aggregate_data_by_class <- function(data,variable,classes,conditionalOn=NULL,dataset,aggregate_to="QTR") {

  ### NOte is anything other that QTR, SEMESTER is passed as variable this will fail since
  # the data is assumed to have either a column called QTR or SEMESTER

  nrows <- dim(conditionalOn)[1]
  # rename class conditional on another variable
  ind <- data[,variable]==classes[1]
  indCond <- 1
  if(!is.null(conditionalOn)) { # multiple conditionals
    for (irow in 1:nrows){
      indCond <- as.logical(indCond) * (data[,conditionalOn[irow,1]] == conditionalOn[irow,2])
    }
  }
  data[as.logical(ind*indCond),variable] <- classes[2]


  # aggregate QTR/SEMESTER to annual. Code QTR = 0
  if (aggregate_to %in% c("QTR","YEAR")) {
    aggregate_to <- "QTR"
  } else if (aggregate_to == "SEMESTER") {
  } else {
    stop("Check how you plan to aggregate data to annual data")
  }


  # aggregate classes
  if (dataset == "landings") {
    data <- data %>%
      dplyr::group_by(YEAR,.data[[aggregate_to]],NEGEAR,MARKET_CODE) %>%
      dplyr::summarise(landings_land=sum(landings_land, na.rm=T),
                       landings_nn=sum(landings_nn, na.rm=T),
                       len_totalNumLen=sum(len_totalNumLen,na.rm=T),
                       len_numLengthSamples=sum(len_numLengthSamples, na.rm=T),
                       .groups = "drop")

  } else if (dataset == "lengths") {
    data <- data %>%
      dplyr::group_by(YEAR,.data[[aggregate_to]],NEGEAR,MARKET_CODE,LENGTH) %>%
      dplyr::summarise(NUMLEN = sum(as.numeric(NUMLEN),na.rm=T),
                       .groups="drop")
  } else {
    stop(paste("Can not aggregate a dataset of type = ",dataset))
  }


  return(data)

}