R/epa_createDataDataframe.R

Defines functions epa_createDataDataframe

Documented in epa_createDataDataframe

#' @keywords EPA
#' @export
#' @import MazamaCoreUtils
#'
#' @title Create EPA data dataframe
#'
#' @param tbl an EPA raw tibble after metadata enhancement
#' @description After additional columns(i.e. \code{datetime}, and \code{monitorID})
#' have been applied to an EPA dataframe, we are ready to
#' extract the PM2.5 values and store them in a \code{data} dataframe
#' organized as time-by-monitor.
#'
#' The first column of the returned dataframe is named \code{datetime} and
#' contains a \code{POSIXct} time in UTC. Additional columns contain data
#' for each separate monitorID.
#'
#' @return A \code{data} dataframe for use in a \emph{ws_monitor} object.

epa_createDataDataframe <- function(tbl) {

  logger.debug(" ----- epa_createDataDataframe() ----- ")

  # Create a column with the datetime
  timeString <- paste0(tbl$`Date GMT`,' ',tbl$`Time GMT`,':00')
  tbl$datetime <- lubridate::ymd_hms(timeString, tz = "UTC")

  # NOTE:  Add monitorID to match what is done in epa_createMetaDataframes().

  tbl$siteID <- paste0(tbl$`State Code`,tbl$`County Code`,tbl$`Site Num`)
  tbl$instrumentID <- sprintf("%02d", as.numeric(tbl$POC))
  tbl$monitorID <- paste(tbl$siteID, tbl$instrumentID, sep='_')

  # "melt" the data frame into long-format data
  # The "melt" function will turn the column names into their own column and the rest of the data into a second.
  melted <- reshape2::melt(data=tbl, id.vars = c("datetime","monitorID"), measure.vars="Sample Measurement")

  # Sanity check -- only one pm25DF measure per hour
  valueCountPerCell <- reshape2::dcast(melted, datetime ~ monitorID, length)
  maxCount <- max(valueCountPerCell[,-1])
  if (maxCount > 1) logger.warn("Up to %s measurements per hour -- median used",maxCount)

  # create a dataframe for values
  pm25DF <- reshape2::dcast(melted, datetime ~ monitorID, stats::median)

  # create a dataframe for hours
  hourlyDF <- data.frame(seq(min(melted$datetime), max(melted$datetime), by="hours"))
  names(hourlyDF) <- "datetime"

  # combine the two dataframes together by doing a left join
  data <- dplyr::left_join(hourlyDF, pm25DF, by="datetime")

  logger.trace("'data' dataframe has %d rows and %d columns", nrow(data), ncol(data))

  return(data)
}

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PWFSLSmoke documentation built on July 8, 2020, 7:19 p.m.