Description Usage Arguments Details Value Examples
View source: R/monitor_dailyThreshold.R
Calculates the number of hours per day each monitor in ws_monitor
was at or above a given threshold
1 2 3 4 5 6 7 | monitor_dailyThreshold(
ws_monitor,
threshold = "unhealthy",
dayStart = "midnight",
minHours = 0,
na.rm = TRUE
)
|
ws_monitor |
ws_monitor object |
threshold |
AQI level name (e.g. |
dayStart |
one of |
minHours |
minimum number of hourly observations required |
na.rm |
logical value indicating whether NA values should be ignored |
NOTE: The returned counts include values at OR ABOVE the given threshold; this applies to both categories and values.
For example, passing a threshold
argument = "unhealthy" will return a daily count of values that are unhealthy,
very unhealthy, or extreme (i.e. >= 55.5), as will passing a threshold
argument = 55.5.
AQI levels for threshold
argument = one of "good|moderate|usg|unhealthy|very unhealthy|extreme"
Sunrise and sunset times are calculated based on the first monitor encountered. This should be accurate enough for all use cases involving co-located monitors. Monitors from different regions should have daily statistics calculated separately.
The returned ws_monitor object has a daily time axis where each time is set to 00:00, local time.
A ws_monitor object with a daily count of hours at or above threshold
.
1 2 3 4 5 6 7 | library(PWFSLSmoke)
N_M <- monitor_subset(Northwest_Megafires, tlim=c(20150801,20150831))
Twisp <- monitor_subset(N_M, monitorIDs='530470009_01')
Twisp_daily <- monitor_dailyThreshold(Twisp, "unhealthy", dayStart='midnight', minHours=1)
monitor_timeseriesPlot(Twisp_daily, type='h', lwd=6, ylab="Hours")
title("Twisp, Washington Hours per day Above 'Unhealthy', 2015")
|
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