pat_aggregateOutlierCounts: Aggregate data with count of outliers in each bin

In AirSensor: Process and Display Data from Air Quality Sensors

Description

Aggregate data with count of outliers in each bin

Usage

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

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()
)

AirSensor documentation built on March 13, 2021, 1:07 a.m.