Title:	Detect and Treat Outliers in Data Mining
Version:	0.1.5
Description:	Implements a suite of tools for outlier detection and treatment in data mining. It includes univariate methods (Z-score, Interquartile Range), multivariate detection using Mahalanobis distance, and density-based detection (Local Outlier Factor) via the 'dbscan' package. It also provides functions for visualization using 'ggplot2' and data cleaning via Winsorization.
License:	MIT + file LICENSE
Encoding:	UTF-8
RoxygenNote:	7.3.3
Imports:	dbscan, ggplot2, isotree, plotly, stats
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0)
Config/testthat/edition:	3
VignetteBuilder:	knitr
URL:	https://github.com/daniellop1/quickOutlier
BugReports:	https://github.com/daniellop1/quickOutlier/issues
NeedsCompilation:	no
Packaged:	2026-02-13 19:27:34 UTC; dlopez
Author:	Daniel López Pérez [aut, cre]
Maintainer:	Daniel López Pérez <dlopez350@icloud.com>
Repository:	CRAN
Date/Publication:	2026-02-13 19:40:02 UTC

Detect Rare Categories (Categorical Outliers)

Description

Identifies categories in a character or factor vector that appear less frequently than a specified threshold.

Usage

detect_categorical_outliers(data, min_freq = 0.01)

Arguments

Details

The function calculates the relative frequency of each unique level. If the frequency is below min_freq, the category is flagged as an outlier.

Value

A data frame summarizing the categories:

Examples

cities <- c(rep("Madrid", 10), "Barcalona")
detect_categorical_outliers(cities, min_freq = 0.1)

Detect Density-Based Anomalies (LOF)

Description

Uses the Local Outlier Factor (LOF) algorithm to identify anomalies based on local density. It is useful for detecting outliers in multi-dimensional data that Z-score misses.

Usage

detect_density(data, k = 5, threshold = 1.5)

Arguments

Value

A data frame with the outliers and their LOF score.

Examples

df <- data.frame(x = c(rnorm(50), 5), y = c(rnorm(50), 5))
detect_density(df, k = 5)

Detect Outliers using Isolation Forest (Machine Learning)

Description

This function applies the Isolation Forest algorithm to detect anomalies in high-dimensional or complex datasets. Unlike statistical methods that measure distance from a mean, Isolation Forest isolates observations by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature.

Usage

detect_iforest(data, ntrees = 100, contamination = 0.05)

Arguments

Details

Recursive partitioning can be represented by a tree structure, and the number of splittings required to isolate a sample is equivalent to the path length from the root node to the terminating node. Random trees produce shorter path lengths for anomalies, as they are essentially "fewer" and "different" from normal observations.

The function relies on the efficient isotree package for computation.

Value

A data frame with the original columns plus:

Note

This function requires the isotree package.

See Also

isolation.forest

Examples

# Example: Detect anomalies in a generated dataset
df <- data.frame(x = c(rnorm(100), 1000), y = c(rnorm(100), 1000))
result <- detect_iforest(df, ntrees = 50, contamination = 0.02)
tail(result)

Detect Multivariate Anomalies (Mahalanobis Distance)

Description

Identifies outliers based on the relationship between multiple variables using Mahalanobis Distance. This is useful when individual values are normal, but their combination is anomalous (e.g., high weight for low height).

Usage

detect_multivariate(data, columns, confidence_level = 0.99)

Arguments

Value

A data frame with the multivariate outliers and their Mahalanobis distance.

Examples

# Generate dataset (n=50) with strong correlation
df <- data.frame(x = rnorm(50), y = rnorm(50))
df$y <- df$x * 2 + rnorm(50, sd = 0.5) # y depends on x

# Add an anomaly: normal x, but impossible y
anomaly <- data.frame(x = 0, y = 10)
df <- rbind(df, anomaly)

# Detect
detect_multivariate(df, columns = c("x", "y"))

Detect Anomalies in a Data Frame

Description

This function identifies rows containing outliers in a specific numeric column. It supports two methods:

• zscore: Based on the standard deviation (statistical approach). Best for normal distributions.

• iqr: Based on the Interquartile Range (robust approach). Best for data with skewness or extreme outliers.

Usage

detect_outliers(data, column, method = "zscore", threshold = 3)

Arguments

Value

A data frame containing only the rows considered outliers, with an additional column displaying the calculated score or bounds.

Examples

# Example with a clear outlier
df <- data.frame(
  id = 1:6,
  value = c(10, 12, 11, 10, 500, 11)
)

# Detect using IQR (Robust)
detect_outliers(df, column = "value", method = "iqr")

# Detect using Z-Score
detect_outliers(df, column = "value", method = "zscore")

Detect Anomalies in Time Series using STL Decomposition

Description

Performs a seasonal-trend decomposition using Loess (STL) to separate the time series into three components: Trend, Seasonality, and Remainder (Noise). Outliers are then detected in the Remainder component using the Interquartile Range (IQR) method.

Usage

detect_ts_outliers(data, frequency = 12)

Arguments

Details

This method is superior to simple thresholding for time series because it accounts for:

• Seasonality: Repeating patterns (e.g., higher sales in December).

• Trend: Long-term increase or decrease.

An observation is flagged as an outlier only if it is unusual after accounting for these normal temporal patterns.

Value

A data frame containing:

Examples

# Example: Synthetic monthly data with a clear anomaly
sales <- c(sin(seq(1, 20, 0.5)) * 10 + 50) # Normal pattern
sales[10] <- 200 # Inject outlier
result <- detect_ts_outliers(sales, frequency = 12)
subset(result, Is_Outlier == TRUE)

Diagnose Influential Points in Linear Models (Cook's Distance)

Description

Fits a linear model between two variables and calculates Cook's Distance to identify influential points. An influential point is an outlier that specifically affects the slope of the regression line.

Usage

diagnose_influence(data, target, predictor, cutoff = NULL)

Arguments

Details

Cook's distance (D_i) measures the effect of deleting a given observation. Points with a large D_i are considered to have high leverage and influence.

The default threshold for detection is calculated as:

Threshold = \frac{4}{n}

Where n is the number of observations. This is a standard rule of thumb in regression diagnostics.

Value

A data frame with the original data plus:

Examples

# Example: A point that pulls the regression line
df <- mtcars
# Artificially create a leverage point
df[1, "wt"] <- 10
df[1, "mpg"] <- 50
result <- diagnose_influence(df, "mpg", "wt")
head(result)

Create an Interactive Outlier Plot

Description

Generates an interactive scatter plot using 'plotly'. Outliers detected by the specified method are highlighted in red.

Usage

plot_interactive(data, x_col, y_col, confidence_level = 0.95)

Arguments

Value

A plotly object.

Plot Outliers with ggplot2

Description

Visualizes the distribution of a variable and highlights detected outliers in red. It combines a boxplot (for context) and jittered points (for individual data visibility).

Usage

plot_outliers(data, column, method = "zscore", threshold = 3)

Arguments

Value

A ggplot object. You can add more layers to it using +.

Examples

library(ggplot2)
df <- data.frame(val = c(rnorm(50), 10)) # 50 normal points and one outlier
plot_outliers(df, "val", method = "iqr")

Scan Entire Dataset for Outliers

Description

Iterates through all numeric columns in the dataset and provides a summary table of outliers found.

Usage

scan_data(data, method = "iqr")

Arguments

Value

A summary data frame with columns: Column, Outlier_Count, and Percentage.

Examples

df <- data.frame(
  a = c(1:10, 100),
  b = c(1:10, 1)
)
scan_data(df, method = "iqr")

Treat Outliers (Winsorization/Capping)

Description

Instead of removing outliers, this function replaces extreme values with the calculated upper and lower boundaries (caps). This technique is often called "Winsorization".

Usage

treat_outliers(data, column, method = "iqr", threshold = 1.5)

Arguments

Value

A data frame with the modified column values.

Examples

# Example: 100 is an outlier
df <- data.frame(val = c(1, 2, 3, 2, 1, 100))

# The 100 will be replaced by the maximum allowed IQR value
clean_df <- treat_outliers(df, "val", method = "iqr")
print(clean_df$val)