Aggregate data with count of outliers in each bin

pat_aggregateOutlierCounts(
  pat = NULL,
  unit = "minutes",
  count = 60,
  windowSize = 23,
  thresholdMin = 8
)

Arguments

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

Value

data.frame A data.frame with flag counts per bin.

See also

pat_aggregateData

Examples

# \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() )
# }