Detect outliers using the Tukey IQR rule and visualize results

This function identifies potential outliers in a numeric variable using the Tukey interquartile range (IQR) rule (Tukey, 1977). It returns a tibble of the detected outlier rows and a ggplot visualization showing the variable across groups with outlier points highlighted.

Usage

IQROutliers(df, Variable, id = NULL, group = NULL)

Arguments

df: A data frame or tibble containing the variable to evaluate.
Variable: A string specifying the name of the numeric variable to test.
id: A string specifying the identifier column to include in the returned outlier table. If NULL, no ID column is included in the returned table. Defaults to NULL.
group: A string specifying the grouping or batch column to use on the x-axis of the diagnostic plot. If NULL, the function will produce a single combined boxplot across all rows. Defaults to NULL.

Value

A list with two elements:

outlierdf: a tibble containing only rows flagged as outliers, including the ID (when requested), variable, group (when requested), and outlier flag.
p: a ggplot2 object showing a boxplot and jittered points colored by outlier status.

Details

The outlier rule is defined as: $$value < Q1 - 1.5 * IQR \; \textrm{or} \; value > Q3 + 1.5 * IQR$$

Outliers are visually highlighted using jittered points colored red, while the boxplot remains uncolored to prevent creation of a separate outlier-only box.

The color aesthetic is mapped only within geom_jitter(), ensuring the boxplot is drawn once per group rather than once per outlier class. Missing values are ignored when computing quartiles. If id or group are provided they must be present in the input data frame.

References

Tukey, J. W. (1977). Exploratory Data Analysis. Addison-Wesley.

Examples

if (FALSE) { # \dontrun{
  IQROutliers(df_Revalued_Data, "PS",
              id = "record_id",
              group = "Cohort")

  # Without id or group
  IQROutliers(df_Revalued_Data, "PS", id = NULL, group = NULL)
} # }