monitor_loadLatest(),
monitor_loadDaily() and monitor_loadAnnual()
with improved logic to handle multiple deployments at a single location.
These functions intentionally only return a single
deviceDeploymentID per locationID and
preferentially retain AirNow data over data from AIRSIS or WRCC. The new
logic applies to cases where multiple deployments exist within AirNow
(or AIRSIS or WRCC) and retains the deployment that has the most recent
data.monitor_arrange() to reorder time series in a
mts_monitor object based on a variable in
mts_monitor$meta.fullAQSID field in all
loaded data.monitor_loadAnnual() when loading years
before the first year of AirNow data.NW_Megafires dataset to combine data from EPA
AQS, AirNow and WRCC.epa_aqs_loadAnnual().monitor_loadAnnual() to load
epa_aqs data.epaPreference argument to
monitor_load().monitor_combine() with an
overlapStrategy argument. With
overlapStrategy = "replace all", values from later
timeseries (including NA) always replace values from
earlier timeseries. With overlapStrategy = "replace na",
values from later timeseries only replace NA values in
earlier timeseries.NW_Megafires dataset from corrected
database.mts_selectWhere() function.QC_removeSuspectData argument to
airsis_load~() and `wrcc_load~() functions.QC_invalidateConsecutiveSuspectValues()
function.QC_removeSuspectData = TRUE argument to
airsis_load~() and `wrcc_load~() functions to remove those
monitors that have values of 2000 ug/m3. A review of the data from
AIRSIS and WRCC shows some archival time series (2015, 2016, 2020) where
all values are one of 0, 1K, 2K, 3K, 4K, 5K.monitor_selectWhere() for data based
selection.monitor_toPWFSLSmoke() and
monitor_fromPWFSLSmoke() to support the
fullAQSID fieldmonitor_filterDate() and
monitor_filterDatetime() to allow one-sided filtering when
passing in only a single startdate or endddate
argument.Version 0.3 works with data built using a new fullAQSID
field available from AirNow. This unique identifier is more consistent
and should be better supported in the future than the older
AQSID field.
This change requires a few minor changes mostly in function examples.
CRAN fixes:
CRAN fixes:
simplfy = TRUE from calls to
base::apply() as this is the default.Ready for CRAN submission.
airnow_load~() functions o put
parameterName argument last.test-loadData.Repa_aqs_loadAnnual() until those data files get
rebuilt.monitor_timeRange().NaN data values with NA when
loading data.AirFire_S3_archiveBaseUrl as package data.monitor_dailyBarplot() to use the
palette argument.Introduction to AirMonitorDeveloper Style Guidemonitor_combine().archiveBaseUrl to point to
https://airfire-data-exports.s3.us-west-2.amazonaws.com/monitoring/v2.trimEmptyDays argument to
monitor_trimDate().monitor_aqi() and support for plots including:
US_EPA$breaks_AQIpollutant = "AQI" option to all
addAQI~() functionsmonitor_leaflet() so that it can handle
single-timeseries monitor objects.monitor_load() and monitor_loadLatest/Daily/Monthly/Annual()
to intelligently combine data from AirNow, AIRSIS and WRCC.airsis_loadAnnual().monitor_leaflet() now displays deviceDeploymentID in
bold.monitor_combine() with the
replaceMeta argument.airsis_loadAnnual(),
wrcc_loadAnnual(), airnow_loadMonthly().monitor_distance() to
monitor_getDistance() to imply that the returned object is
not a mts_monitor object.monitor_filterByDistance().monitor_getCurrentStatus():
monitor_filterDate()
and monitor_filterDatetime().monitor_isEmpty().monitor_extractData() to
monitor_getData().monitor_extractMeta() to
monitor_getMeta().monitor_getCurrentStatus().monitor_nowcast().monitor_dygraph().monitor_mutate().monitor_filterData() as too confusing because
it returns an irregular time axis. Anyone wanting to do this kind of
work should be familiar enough with dplyr to do it
themselves.monitor_dailyStatistic().monitor_dailyThreshold().monitor_dailyBarplot().aqiColors().monitor_timeseriesPlot() and
monitor_dailyBarplot().airsis_loadLatest(),
wrcc_loadLatest().~_loadDaily().AQSID to coreMetadataNames.monitor_toCSV(),
monitor_toPWFSLSmoke().monitor_collapse(),
monitor_distance() and monitor_select().monitor_filter() to
monitor_filterData() to be more explicit.monitor_timeseriesPlot() default to
addAQI = FALSE.monitor_leaflet() now always shows higher values on
top.monitor_timeseriesPlot().airnow_loadlatest().meta$countyName rather than
meta$county. This reflects changes in
MazamaLocationutils and thus the mts_monitor
objects created by AirMonitorIngest.monitor_leaflet().monitor_timeseriesPlot().monitor_timeInfo() function.CONUS and US_52 collections of state
codes.monitor_bestTimezone().CO, OZONE and
PM10 data.monitor_select() for easier selection of
monitors.monitor_replaceValues().QC_negativeValues argument to
epa_aqs_loadAnnual()