R/pat_aggregateOutlierCounts.R
pat_aggregateOutlierCounts.Rd
Aggregate data with count of outliers in each bin
pat_aggregateOutlierCounts( pat = NULL, unit = "minutes", count = 60, windowSize = 23, thresholdMin = 8 )
pat | PurpleAir Timeseries pat object. |
---|---|
unit | Character string specifying temporal units for binning. |
count | Number of units per bin. |
windowSize | the size of the rolling window. Must satisfy windowSize <= count. |
thresholdMin | the minimum threshold value to detect outliers via hampel filter |
data.frame
A data.frame with flag counts per bin.
pat_aggregateData
# \donttest{ library(AirSensor) library(ggplot2) df <- pat_aggregateOutlierCounts(example_pat_failure_A) # Plot the counts multi_ggplot( # A Channel ggplot(df, aes(x = datetime, y = pm25_A_outlierCount)) + geom_point(), # B Channel ggplot(df, aes(x = datetime, y = pm25_B_outlierCount)) + geom_point(), # Humidity ggplot(df, aes(x = datetime, y = humidity_outlierCount)) + geom_point(), # Temperature ggplot(df, aes(x = datetime, y = temperature_outlierCount)) + geom_point() )# }