iris <- read.table("https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data", 
                   header = FALSE, 
                   sep = ",",
                   col.names = c("sepal_length", "sepal_width", "petal_length", "petal_width", "class"))
head(iris)
##   sepal_length sepal_width petal_length petal_width       class
## 1          5.1         3.5          1.4         0.2 Iris-setosa
## 2          4.9         3.0          1.4         0.2 Iris-setosa
## 3          4.7         3.2          1.3         0.2 Iris-setosa
## 4          4.6         3.1          1.5         0.2 Iris-setosa
## 5          5.0         3.6          1.4         0.2 Iris-setosa
## 6          5.4         3.9          1.7         0.4 Iris-setosa

Dataset: Iris Dataset

Source: https://archive.ics.uci.edu/ml/datasets/iris

Description:

The Iris dataset is a classic dataset that is often used for machine learning and data visualization exercises. It consists of measurements for 150 iris flowers from three different species - Setosa, Versicolor, and Virginica. The four variables measured for each flower are sepal length, sepal width, petal length, and petal width.

Column Description:

1: Sepal length (in cm)

2: Sepal width (in cm)

3: Petal length (in cm)

4: Petal width (in cm)

5: Class: Species of the flower (Setosa, Versicolor, or Virginica)

Each row in the dataset represents an individual iris flower, with 150 observations in total.

Descriptive Analytics:

The Iris dataset is a clean and complete dataset with no missing values.