Creating Scatter Plots
Why Use Scatter Plots?
A scatter plot is ideal for:
-
Visualizing relationships between two numerical variables;
-
Detecting patterns, clusters, or outliers;
-
Exploring correlation (positive/negative/none).
Basic Syntax for Scatter Plots in ggplot2
ggplot(data = df, aes(x = variable_x, y = variable_y)) +
geom_point()
To add group-based color, use: aes(x, y, color = group_var)
Example: Selling Price vs Kilometers Driven
ggplot(df, aes(x = km_driven, y = selling_price)) +
geom_point() +
labs(title = "Scatter Plot of Selling Price vs. Kilometers Driven",
x = "Kilometers Driven",
y = "Selling Price")
This shows how a car’s usage relates to its price — often revealing depreciation trends.
Дякуємо за ваш відгук!
Запитати АІ
Запитати АІ
Запитайте про що завгодно або спробуйте одне із запропонованих запитань, щоб почати наш чат
Awesome!
Completion rate improved to 4
Creating Scatter Plots
Свайпніть щоб показати меню
Why Use Scatter Plots?
A scatter plot is ideal for:
-
Visualizing relationships between two numerical variables;
-
Detecting patterns, clusters, or outliers;
-
Exploring correlation (positive/negative/none).
Basic Syntax for Scatter Plots in ggplot2
ggplot(data = df, aes(x = variable_x, y = variable_y)) +
geom_point()
To add group-based color, use: aes(x, y, color = group_var)
Example: Selling Price vs Kilometers Driven
ggplot(df, aes(x = km_driven, y = selling_price)) +
geom_point() +
labs(title = "Scatter Plot of Selling Price vs. Kilometers Driven",
x = "Kilometers Driven",
y = "Selling Price")
This shows how a car’s usage relates to its price — often revealing depreciation trends.
Дякуємо за ваш відгук!