Description Usage Arguments Details See Also Examples
Pre-processing sapflux data can be time consuming - while the whole process hasn't been automated, some outliers are easy enough to pull automatically.
1 2 | AutoDropOutliers(flux, byIQR = FALSE, drop_time = FALSE,
max_time_gap = 30, min_data = 5)
|
flux |
A 'flux' class object. |
byIQR |
Logical - use interquartile range to identify outliers? |
drop_time |
Drop empty time rows? TRUE or FALSE |
max_time_gap |
Maximum time length in minute to interpolate. |
min_data |
Minimum amount of data a column needs to be included, expressed as a percentage of total data. |
In order, AutoDropOutliers checks the following:
1) Are values above an 'absolute_maximum' defined in package metadata?
2) Are all values in a data column NA?
3) Do values occur before the probe install date, or after it's removal date?
4) Are there missing values in < 30 minute gaps? If so, fill them using
zoo::na.approx
.
The byIQR method is an early implementation - it still has trouble picking up on all but the most extreme outliers.
Other preprocess: BindRawFlux
,
ConsoleDropOutliers
,
DropRainyDays
,
GenerateMetaTemplate
,
ImportMeta
, ImportRawFlux
,
MetaDataImport
,
oldBindRawFlux
,
oldImportRawFlux
1 2 3 4 | # As of 0.1.9 - just use byIQR = FALSE, it needs improvement
flux_data <- AutoDropOutliers(flux = flux.data, byIQR = FALSE)
# Check the log to see how many data points got dropped:
print(flux_data@log)
|
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