Data Import

iris <- read.csv("iris.data", header=FALSE)
colnames(iris) <- c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width", "Species")

Data Overview

head(iris)

The iris dataset (obtained from the UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/iris) contains a total of 150 observations and 5 variables:

Data Manipulation

iris$Species <- as.factor(iris$Species)

Descriptive Statistics

summary(iris)
##   Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
##  Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
##  1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
##  Median :5.800   Median :3.000   Median :4.350   Median :1.300  
##  Mean   :5.843   Mean   :3.054   Mean   :3.759   Mean   :1.199  
##  3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
##  Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
##             Species  
##  Iris-setosa    :50  
##  Iris-versicolor:50  
##  Iris-virginica :50  
##                      
##                      
##

The mean sepal length across all 150 iris flowers is 5.8433333.

Data Visualization

Histograms

par(mfrow=c(2,2))  
hist(iris$Sepal.Length, main="Sepal Length", xlab="Length (cm)")
hist(iris$Sepal.Width, main="Sepal Width", xlab="Width (cm)") 
hist(iris$Petal.Length, main="Petal Length", xlab="Length (cm)")
hist(iris$Petal.Width, main="Petal Width", xlab="Width (cm)")

Sepal length and width are fairly normally distributed, while petal length and width are right-skewed.

Scatterplot Matrix

pairs(iris[1:4], main="Iris Data", pch=21, bg=c("red","green3","blue")[unclass(iris$Species)])

By taking a look at the scatter plots the positive correlations between all the measurement variables become apparent. Moreover, it can be observed that the species are separated well, especially by petal dimensions.

Boxplots by Species

boxplot(Sepal.Length ~ Species, data=iris, main="Sepal Length by Species", 
        xlab="Iris Species", ylab="Sepal Length (cm)", col=c("red","green3","blue"))

As can be seen in the boxplot above, setosa has the smallest sepals, while virginica has the largest ones and versicolor is in between the two.

Conclusion

In a nutshell, this exploratory analysis of the iris dataset highlights the differences between species in their sepal and petal sizes, with setosa having the smallest floral dimensions and virginica the largest. Moreover, the variables are correlated with each other but the species can still be distinguished by petal measurements.