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Detect Statistical Outliers Using IQR Method

Source: R/find_outliers.R
find_outliers.Rd

Identifies statistical outliers in a numeric variable using the Interquartile Range (IQR) method. Provides detailed diagnostics about the outlier detection process.

Usage

find_outliers(data, variable_name, verbose = TRUE)

Arguments

data

Data frame containing the variable to analyze.

variable_name

Unquoted name of the numeric variable to check for outliers.

verbose

Logical. If TRUE, prints diagnostic information about quartiles, fences, and number of outliers found. Default is TRUE.

Value

If outliers are found:

  • Data frame containing rows with outlier values

  • Prints diagnostic information about quartiles and fences

If no outliers:

  • Returns NULL

  • Prints confirmation message

Details

The function uses the standard IQR method for outlier detection:

  • Calculates Q1 (25th percentile) and Q3 (75th percentile)

  • Computes IQR = Q3 - Q1

  • Defines outliers as values outside (Q1 - 1.5IQR, Q3 + 1.5IQR)

Diagnostic Output

  • Variable name

  • Q1 and Q3 values

  • IQR value

  • Upper and lower fence values

  • Number of outliers found

Examples

data(mtcars)
find_outliers(mtcars, mpg)
#> Variable name: mpg
#> Q1: 15.425 Q3: 22.8 IQR: 7.375
#> Upper fence: 33.8625 Lower fence: 4.3625
#> Number of outliers: 1
#>                 mpg cyl disp hp drat    wt qsec vs am gear carb
#> Toyota Corolla 33.9   4 71.1 65 4.22 1.835 19.9  1  1    4    1
find_outliers(mtcars, wt, verbose = FALSE)
#>                      mpg cyl disp  hp drat    wt  qsec vs am gear carb
#> Cadillac Fleetwood  10.4   8  472 205 2.93 5.250 17.98  0  0    3    4
#> Lincoln Continental 10.4   8  460 215 3.00 5.424 17.82  0  0    3    4
#> Chrysler Imperial   14.7   8  440 230 3.23 5.345 17.42  0  0    3    4