In this exercise, I will analyze the classic iris dataset. The objective of this report is to load the dataset, inspect its structure, calculate summary statistics, and visualize the data using a simple scatter plot.
First, I will load the built-in iris dataset and display its structure to understand the variables I am working with.
# Display the structure of the iris dataset
str(iris)'data.frame': 150 obs. of 5 variables:
$ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
$ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
$ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
$ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
$ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...The output shows that the dataset contains 150 observations and 5 variables, including measurements for sepal length, sepal width, petal length, and petal width, as well as the specific iris species.
Next, I will compute the summary statistics for all variables in the dataset to examine the distribution, mean, and range of the flower measurements.
# Calculate summary 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 :3.057 Mean :3.758 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
setosa :50
versicolor:50
virginica :50
By looking at the summary, I can notice the differences in scales between sepal and petal measurements across the 150 samples.
To better understand the relationship between the flower characteristics, I will create a scatter plot. I will plot Petal Length against Petal Width, and color the points based on the different Species to see if they naturally form distinct groups.
# Create a scatter plot colored by species
plot(iris$Petal.Width, iris$Petal.Length,
col = as.factor(iris$Species),
pch = 16,
xlab = "Petal Width (cm)",
ylab = "Petal Length (cm)",
main = "Iris Petal by Species")
# Add a simple legend to identify species
legend("topleft", legend = levels(iris$Species), col = 1:3, pch = 16)
The plot clearly demonstrates that the species form distinct clusters, with ‘setosa’ having significantly smaller petals compared to ‘versicolor’ and ‘virginica’.