#' @export
#' @importFrom rlang .data
#'
#' @title Apply hourly aggregation QC using "AB_O4" algorithm
#'
#' @param pat A PurpleAir timeseries object.
#' @param min_count Aggregation bins with fewer than \code{min_count} measurements
#' will be marked as NA.
#' @param returnAllColumns Logical specifying whether to return all columns
#' of statistical data generated for QC algorithm or just the final \code{pm25}
#' result.
#'
#' @description Creates a \emph{pm25} timeseries by averaging aggregated data
#' from the A and B channels and applying the following QC logic:
#'
#' \enumerate{
#' \item{Create pm25 by averaging the A and B channel aggregation means}
#' \item{Invalidate data where: (min_count < 20)}
#' \item{Invalidate data where: (A/B hourly difference > 5 AND A/B hourly percent difference > 70\%)}
#' \item{Invalidate data where: (A/B hourly data recovery < 90\%)}
#' }
#'
#' @note Purple Air II sensors reporting after the June, 2019 firmware
#' upgrade report data every 2 minutes or 30 measurements per hour. The default
#' setting of \code{min_count = 20} is equivalent to a required data recovery
#' rate of 67\%.
#'
#' @return Data frame with columns \code{datetime} and \code{pm25}.
#'
#' @examples
#' \donttest{
#' library(AirSensor)
#'
#' df_00 <-
#' example_pat %>%
#' pat_qc() %>%
#' PurpleAirQC_hourly_AB_00()
#'
#' df_01 <-
#' example_pat %>%
#' pat_qc() %>%
#' PurpleAirQC_hourly_AB_01()
#'
#' df_02 <-
#' example_pat %>%
#' pat_qc() %>%
#' PurpleAirQC_hourly_AB_02()
#'
#' layout(matrix(seq(2)))
#'
#' plot(df_00, pch = 16, cex = 0.8, col = "red")
#' points(df_01, pch = 16, cex = 0.8, col = "black")
#' title("example_pat_failure_A -- PurpleAirQC_hourly_AB_01")
#'
#' plot(df_00, pch = 16, cex = 0.8, col = "red")
#' points(df_02, pch = 16, cex = 0.8, col = "black")
#' title("example_pat_failure_A -- PurpleAirQC_hourly_AB_02")
#'
#' layout(1)
#' }
PurpleAirQC_hourly_AB_03 <- function(
pat = NULL,
min_count = 20,
returnAllColumns = FALSE
) {
# ----- Validate parameters -------------------------------------------------
MazamaCoreUtils::stopIfNull(pat)
MazamaCoreUtils::stopIfNull(min_count)
MazamaCoreUtils::stopIfNull(returnAllColumns)
# ----- Prepare aggregated data ---------------------------------------------
# Hourly counts
countData <-
pat %>%
pat_aggregate( function(x) { base::length(na.omit(x)) } ) %>%
pat_extractData()
# Get percent recovered
recoveredData <-
pat %>%
pat_extractData() %>%
patData_aggregate(
function(df) {
A_recovered <- base::length(na.omit(df$pm25_A))/dim(df)[1]
B_recovered <- base::length(na.omit(df$pm25_B))/dim(df)[1]
data.frame(A_recovered, B_recovered)
}
)
# Hourly means
meanData <-
pat %>%
pat_aggregate( function(x) { base::mean(x, na.rm = TRUE) } ) %>%
pat_extractData()
# Hourly pctDiff
pctDiff <- abs(meanData$pm25_A - meanData$pm25_B) /
((meanData$pm25_A + meanData$pm25_B + 0.01)/2) # add 0.01 to avoid division by zero
minCount <- pmin(countData$pm25_A, countData$pm25_B, na.rm = TRUE)
meanDiff <- abs(meanData$pm25_A - meanData$pm25_B)
# ----- Create masks --------------------------------------------------------
# When only a fraction of the data are reporting, something is wrong.
# Invalidate data where: (min_count < SOME_THRESHOLD)
minCountMask <- pmin(countData$pm25_A, countData$pm25_B, na.rm = TRUE) < min_count
# When the A/B channels differ by a lot relative to their absolute value,
# something is wrong.
# Invalidate data where (pctDiff > 0.5)
pctDiffMask <- pctDiff > 0.7
# Channel difference Mask
# Invalidate when difference > 5
diffMask <- meanDiff > 5
# NOTE: Typical data recovery per 60-minute bin is 45/60 = 0.75.
# NOTE: Flag anything below 0.75*0.9 = 0.675
recoveredMask <- recoveredData$A_recovered < (0.75*0.9) | recoveredData$B_recovered < (0.75*0.9)
# ----- Create hourly dataframe ---------------------------------------------
# NOTE: Include variables used in QC so that they can be used to create a
# NOTE: plot visualizing how the QC algorithm rejects values.
hourlyData <-
# Fill dataframe
dplyr::tibble(datetime = meanData$datetime) %>%
dplyr::mutate(pm25 = (meanData$pm25_A + meanData$pm25_B) / 2) %>%
dplyr::mutate(mean_diff = meanDiff) %>%
dplyr::mutate(pct_diff = pctDiff) %>%
# Replace pm25 with NA if count < minCount
dplyr::mutate(
pm25 = replace(
.data$pm25,
minCountMask,
NA
)
) %>%
# Replace pm25 with NA if percent difference > 70% AND difference > 5
dplyr::mutate(
pm25 = replace(
.data$pm25,
pctDiffMask & diffMask,
NA
)
) %>%
# Replace pm25 with NA if recovered data is < 90%
dplyr::mutate(
pm25 = replace(
.data$pm25,
recoveredMask,
NA
)
)
# ----- Return ---------------------------------------------------------------
if ( returnAllColumns ) {
# Add other columns of data used in this QC
hourlyData <-
hourlyData %>%
dplyr::mutate(
min_count = pmin(countData$pm25_A, countData$pm25_B, na.rm = TRUE),
pm25_A_count = countData$pm25_A,
pm25_B_count = countData$pm25_B,
pm25_A_mean = meanData$pm25_A,
pm25_B_mean = meanData$pm25_B,
pm25_A_recovered = recoveredData$A_recovered,
pm25_B_recovered = recoveredData$B_recovered,
)
} else {
hourlyData <-
hourlyData %>%
dplyr::select(.data$datetime, .data$pm25)
}
return(hourlyData)
}
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