Class Activity 3: Iris Dataset Visualization

data()

Introduction In this assignment, I explore the Iris dataset (Edgar Anderson’s Iris Data), which contains information about the measurements of different Iris flowers.

Variables:

Sepal.Length: Sepal length in centimeters (Numeric)

Sepal.Width: Sepal width in centimeters (Numeric)

Petal.Length: Petal length in centimeters (Numeric)

Petal.Width: Petal width in centimeters (Numeric)

Species: Species of iris flower (Setosa, Versicolor, Virginica) (Categorical)

install.packages("tidyverse")

Error in install.packages : Updating loaded packages

library(tidyverse) # Load the tidyverse package for data manipulation and visualization
library(ggplot2) # Load ggplot2 for creating plots

# Load the dataset (built into R)
data(iris)

# View the first few rows to understand the data
head(iris)

Scatter Plot

# Convert 'Species' to a factor (it should already be a factor, but I did it just for clarity)
iris$Species <- as.factor(iris$Species)
# Scatter plot of Sepal.Length vs Sepal.Width
plot(iris$Sepal.Length, iris$Sepal.Width, 
     xlab = "Sepal Length", ylab = "Sepal Width", 
     main = "Scatter Plot of Sepal Length vs Sepal Width",
     col = iris$Species, pch = 19)
legend("topright", legend = levels(iris$Species), col = 1:3, pch = 19)

Line Graph

library(dplyr)
library(ggplot2)

# Add an index column to the iris dataset to represent row numbers
iris_line <- iris %>% mutate(index = row_number())

# Create the line graph of Sepal.Length by index (row number)
ggplot(iris_line, aes(x = index, y = Sepal.Length)) +
  geom_line() +  # Add a line
  labs(title = "Sepal Length by Index", x = "Index", y = "Sepal Length")  # Add plot labels

Horizontal Bar Chart

# Calculate the average Sepal.Length for each species
avg_sepal_length_by_species <- iris %>% 
  group_by(Species) %>% 
  summarize(avg_sepal_length = mean(Sepal.Length))

# Create a horizontal bar chart of average Sepal Length by Species
ggplot(avg_sepal_length_by_species, aes(y = Species, x = avg_sepal_length)) +
  geom_bar(stat = 'identity', fill = "lightblue") +  # Create bars based on average Sepal Length
  labs(title = "Average Sepal Length by Species", y = "Species", x = "Average Sepal Length")  # Add plot labels

Stacked Bar Chart

library(tidyr)

# Calculate average values for each measurement and pivot the data into a long format
bar_data_iris <- iris %>% 
  group_by(Species) %>% 
  summarize(
    Sepal.Length = mean(Sepal.Length), 
    Sepal.Width = mean(Sepal.Width),
    Petal.Length = mean(Petal.Length),
    Petal.Width = mean(Petal.Width)
  ) %>% 
  pivot_longer(cols = c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width"), 
               names_to = "Measurement", values_to = "Average")

# Create a stacked bar chart of average Sepal and Petal measurements by Species
ggplot(bar_data_iris, aes(x = Species, fill = Measurement, y = Average)) +
  geom_bar(stat = "identity") +  # Create bars based on the calculated averages
  labs(title = "Average Measurements by Species", x = "Species", y = "Average Measurement")  # Add plot labels

Conclusion

This notebook demonstrates various data visualization techniques using the Iris dataset which is a public dataset built into R.