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#' @export
#' @importFrom dplyr across
#'
#' @title Load annual AirNow monitoring data
#'
#' @param year Year [YYYY].
#' @param archiveBaseUrl Base URL for monitoring v2 data files.
#' @param archiveBaseDir Local base directory for monitoring v2 data files.
#' @param QC_negativeValues Type of QC to apply to negative values.
#' @param parameterName One of the EPA AQS criteria parameter names.
#'
#' @return A \emph{mts_monitor} object with AirNow data. (A list with
#' \code{meta} and \code{data} dataframes.)
#'
#' @description Loads pre-generated .rda files containing hourly AirNow data.
#'
#' If \code{archiveDataDir} is defined, data will be loaded from this local
#' archive. Otherwise, data will be loaded from the monitoring data repository
#' maintained by the USFS AirFire team.
#'
#' The files loaded by this function contain a single year's worth of data
#'
#' For the most recent data in the last 10 days, use \code{airnow_loadLatest()}.
#'
#' For daily updates covering the most recent 45 days, use \code{airnow_loadDaily()}.
#'
#' For archival data for a specific month, use \code{airnow_loadMonthly()}.
#'
#' Pre-processed AirNow exists for the following parameters:
#' \enumerate{
# #' \item{BARPR}
# #' \item{BC}
# #' \item{CO}
# #' \item{NO}
# #' \item{NO2}
# #' \item{NO2Y}
# #' \item{NO2X}s
# #' \item{NOX}
# #' \item{NOOY}
# #' \item{OC}
# #' \item{OZONE}
# #' \item{PM10}
#' \item{PM2.5}
# #' \item{PM2.5_nowcast}
# #' \item{PRECIP}
# #' \item{RHUM}
# #' \item{SO2}
# #' \item{SRAD}
# #' \item{TEMP}
# #' \item{UV-AETH}
# #' \item{WD}
# #' \item{WS}
#' }
#'
#' @seealso \code{\link{airnow_loadDaily}}
#' @seealso \code{\link{airnow_loadLatest}}
#' @seealso \code{\link{airnow_loadMonthly}}
#'
#' @examples
#' \dontrun{
#' library(AirMonitor)
#'
#' # Fail gracefully if any resources are not available
#' try({
#'
#' # See https://en.wikipedia.org/wiki/2017_Montana_wildfires
#'
#' # Daily Barplot of Montana wildfires
#' airnow_loadAnnual(2017) \%>\%
#' monitor_filter(stateCode == "MT") \%>\%
#' monitor_filterDate(20170701, 20170930, timezone = "America/Denver") \%>\%
#' monitor_dailyStatistic() \%>\%
#' monitor_timeseriesPlot(
#' ylim = c(0, 300),
#' xpd = NA,
#' addAQI = TRUE,
#' main = "Montana 2017 -- AirNow Daily Average PM2.5"
#' )
#'
#' }, silent = FALSE)
#' }
airnow_loadAnnual <- function(
year = NULL,
archiveBaseUrl = paste0(
"https://airfire-data-exports.s3.us-west-2.amazonaws.com/",
"monitoring/v2"
),
archiveBaseDir = NULL,
QC_negativeValues = c("zero", "na", "ignore"),
parameterName = "PM2.5"
) {
# ----- Validate parameters --------------------------------------------------
MazamaCoreUtils::stopIfNull(year)
MazamaCoreUtils::stopIfNull(parameterName)
QC_negativeValues <- match.arg(QC_negativeValues)
if ( is.null(archiveBaseUrl) && is.null(archiveBaseDir) )
stop("one of 'archiveBaseUrl' or 'archiveBaseDir' must be defined")
# Parameter code
validParameterNames <- c(
# "BARPR",
# "BC",
# "CO",
# "NO",
# "NO2",
# "NO2Y",
# "NO2X",
# "NOX",
# "NOOY",
# "OC",
# "OZONE",
# "PM10",
"PM2.5"
# "PM2.5_nowcast"
# "PRECIP",
# "RHUM",
# "SO2",
# "SRAD",
# "TEMP",
# "UV-AETH",
# "WD",
# "WS"
)
parameterName <- as.character(parameterName)
if ( !parameterName %in% validParameterNames ) {
stop(sprintf(
"data for parameterName '%s' has not been processed",
parameterName
))
}
# ----- Load data ------------------------------------------------------------
# Create file name and path according to the AirMonitorIngest scheme
if ( is.null(archiveBaseUrl) ) {
dataUrl <- NULL
} else {
dataUrl <- file.path(archiveBaseUrl, "airnow", year, "data")
}
if ( is.null(archiveBaseDir) ) {
dataDir <- NULL
} else {
dataDir <- file.path(archiveBaseDir, "airnow", year, "data")
}
metaFileName <- sprintf("airnow_%s_%s_meta.rda", parameterName, year)
dataFileName <- sprintf("airnow_%s_%s_data.rda", parameterName, year)
meta <- MazamaCoreUtils::loadDataFile(metaFileName, dataUrl, dataDir)
data <- MazamaCoreUtils::loadDataFile(dataFileName, dataUrl, dataDir)
# Guarantee that 'meta' and 'data' match
ids <- names(data)[-1]
meta <-
meta %>%
dplyr::filter(.data$deviceDeploymentID %in% ids)
# Guarantee presence of fullAQSID
if ( !"fullAQSID" %in% names(meta) ) meta$fullAQSID <- NA_character_
data <-
data %>%
dplyr::select(dplyr::all_of(c("datetime", meta$deviceDeploymentID))) %>%
# Replace any NaN that snuck in
dplyr::mutate(across(tidyselect::vars_select_helpers$where(is.numeric), function(x) ifelse(is.nan(x), NA, x)))
# Create monitor object
monitor <- list(meta = meta, data = data)
monitor <- structure(monitor, class = c("mts_monitor", "mts", class(monitor)))
MazamaTimeSeries::mts_check(monitor)
# ----- Apply QC -------------------------------------------------------------
if ( QC_negativeValues == "zero" ) {
monitor <- monitor_replaceValues(monitor, data < 0, 0)
} else if ( QC_negativeValues == "na" ) {
monitor <- monitor_replaceValues(monitor, data < 0, as.numeric(NA))
}
# ----- Return ---------------------------------------------------------------
return(monitor)
}
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