# DAL ToolBox
# version 1.01.727
source("https://raw.githubusercontent.com/cefet-rj-dal/daltoolbox/main/jupyter.R")
#loading DAL
load_library("daltoolbox")
Loading required package: daltoolbox
Registered S3 method overwritten by 'quantmod':
method from
as.zoo.data.frame zoo
Attaching package: ‘daltoolbox’
The following object is masked from ‘package:base’:
transform
The following class uses box-plot definition for outliers.
An outlier is a value that is below than $Q_1 - 1.5 \cdot IQR$ or higher than $Q_3 + 1.5 \cdot IQR$.
The class remove outliers for numeric attributes.
# code for outlier removal
out_obj <- outliers() # class for outlier analysis
out_obj <- fit(out_obj, iris) # computing boundaries
iris.clean <- transform(out_obj, iris) # returning cleaned dataset
# inspection of cleaned dataset
head(iris.clean)
nrow(iris.clean)
| Sepal.Length | Sepal.Width | Petal.Length | Petal.Width | Species | |
|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <fct> | |
| 1 | 5.1 | 3.5 | 1.4 | 0.2 | setosa |
| 2 | 4.9 | 3.0 | 1.4 | 0.2 | setosa |
| 3 | 4.7 | 3.2 | 1.3 | 0.2 | setosa |
| 4 | 4.6 | 3.1 | 1.5 | 0.2 | setosa |
| 5 | 5.0 | 3.6 | 1.4 | 0.2 | setosa |
| 6 | 5.4 | 3.9 | 1.7 | 0.4 | setosa |
Visualizing the actual outliers
idx <- attr(iris.clean, "idx")
print(table(idx))
iris.outliers <- iris[idx,]
head(iris.outliers)
idx FALSE TRUE 146 4
| Sepal.Length | Sepal.Width | Petal.Length | Petal.Width | Species | |
|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <fct> | |
| 16 | 5.7 | 4.4 | 1.5 | 0.4 | setosa |
| 33 | 5.2 | 4.1 | 1.5 | 0.1 | setosa |
| 34 | 5.5 | 4.2 | 1.4 | 0.2 | setosa |
| 61 | 5.0 | 2.0 | 3.5 | 1.0 | versicolor |