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Write an R script to create a scatter plot, incorporating categorical analysis through color-coded data points representing different groups, using ggplot2.
# Load necessary libraries
library(ggplot2)
library(dplyr)
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
Explanation:
The iris dataset contains 150 samples of iris flowers categorized into three species: setosa, versicolor, and virginica.
Each sample has sepal and petal measurements.
head(data) displays the first few rows.
# Load the iris dataset
data <- iris
# Display first few rows
head(data) Sepal.Length Sepal.Width Petal.Length Petal.Width Species
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
X-Axis (Sepal.Length)
Y-Axis (Sepal.Width)
Color (Species)
Customization
geom_point(size = 3, alpha = 0.7): Increases the size of points and makes them slightly transparent.
labs(): Adds a title and axis labels.
theme_minimal(): Uses a clean background for readability
theme(legend.position = "top"): Moves the legend to the top.
# Create a scatter plot using ggplot2
ggplot(data, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
geom_point(size = 3, alpha = 0.7) + # Increase point size & transparency
labs(title = "Scatter Plot of Sepal Dimensions",
x = "Sepal Length",
y = "Sepal Width",
color = "Species") + # Legend title
theme_minimal() + # Clean layout
theme(legend.position = "top") # Move legend to the top