interquartile: Computes interquartile range to flag environmental outliers

In specleanr: Detecting Environmental Outliers in Data Analysis Pipelines

Computes interquartile range to flag environmental outliers

Description

Computes interquartile range to flag environmental outliers

Usage

interquartile(
  data,
  var,
  output,
  x = 1.5,
  pc = FALSE,
  pcvar = NULL,
  boot = FALSE
)

Arguments

data	Dataframe to check for outliers
var	Variable considered in flagging suspicious outliers
output	Either clean: for dataframe with no suspicious outliers or outlier: to retrun dataframe with only outliers.
x	A constant to create a fence or boundary to detect outliers.
pc	Whether principal component analysis will be computed. Default FALSE
pcvar	Principal component analysis to e used for outlier detection after PCA. Default PC1
boot	Whether bootstrapping will be computed. Default FALSE

Details

Interquartile range (IQR) uses quantiles that are resistant to outliers compared to mean and standard deviation (Seo 2006). Records were considered as mild outliers if they fell outside the lower and upper bounding fences [Q1 (lower quantile) -1.5*IQR (Interquartile range); Q3 (upper quantile) +1.5*IQR] respectively (Rousseeuw & Hubert 2011). Extreme outliers were also considered if they fell outside \[Q1-3*IQR, Q3+3*IQR\] (García-Roselló et al. 2014). However, using the interquartile range assumes uniform lower and upper bounding fences, which is not robust to highly skewed data (Hubert & Vandervieren 2008).

Value

Dataframe with or with no outliers.

References

Rousseeuw PJ, Hubert M. 2011. Robust statistics for outlier detection. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery 1:73-79.

Examples

data("efidata")

danube <- system.file('extdata/danube.shp.zip', package='specleanr')

db <- sf::st_read(danube, quiet=TRUE)

wcd <- terra::rast(system.file('extdata/worldclim.tiff', package='specleanr'))

refdata <- pred_extract(data = efidata, raster= wcd , lat = 'decimalLatitude',
                          lon= 'decimalLongitude',
                          colsp = "scientificName",
                          bbox = db,
                          minpts = 10)

 iqrout <- interquartile(data = refdata[["Thymallus thymallus"]], var = 'bio6', output='outlier')

specleanr documentation built on Nov. 26, 2025, 1:07 a.m.