detect_outliers: Detect outliers using multiple methods

In dataprep: Fast, Efficient, and Versatile Data Preprocessing and Reshaping with 'C++', 'OpenMP' & 'SIMD'

Detect outliers using multiple methods

Description

Identifies outliers in selected columns using one of several methods: IQR-based, Median Absolute Deviation (MAD), or percentile-based. The function can either return a logical mask indicating outlier positions or replace outliers with NA.

Usage

detect_outliers(data, cols = NULL, method = "iqr",
                top = 0.995, bottom = 0.0025, coef = 1.5,
                group = NULL, mask_only = TRUE, verbose = FALSE)

Arguments

data	A data frame, matrix, or numeric vector.
cols	The column indices or names of selected variables. If NULL, all numeric columns are used.
method	Detection method. One of "iqr" (default), "mad", "percentile".
top	The top percentile threshold for percentile method.
bottom	The bottom percentile threshold for percentile method.
coef	The coefficient for IQR or MAD method. For IQR, values beyond Q1 - coef*IQR and Q3 + coef*IQR are outliers. For MAD, values with |z| > coef are outliers.
group	Optional grouping column for group-wise detection.
mask_only	Logical. If TRUE (default), returns a logical matrix of outlier positions. If FALSE, returns data with outliers replaced by NA.
verbose	Logical; if TRUE, prints progress message.

Details

The IQR method uses Tukey's fences: values outside [Q1 - coef*IQR, Q3 + coef*IQR] are considered outliers. The MAD method uses robust z-scores: |0.6745*(x - median)/MAD| > coef. The percentile method flags values above the top percentile or below the bottom percentile.

Value

If mask_only = TRUE, a logical matrix with TRUE indicating outliers. If mask_only = FALSE, a data frame with outliers set to NA.

Examples

# Return mask
mask <- detect_outliers(data[1:100, c(1, 4, 17:19)], cols = 3:5, method = "iqr")
# Replace outliers with NA
cleaned <- detect_outliers(data[1:100, c(1, 4, 17:19)], cols = 3:5,
                           method = "mad", mask_only = FALSE)

dataprep documentation built on Oct. 1, 2026, 5:07 p.m.