Summary  
This chapter demonstrates how to implement code that maps two numeric variables to axes and produces a basic scatter plot by adding a point geometry layer.  

General domain of usage  
Exploratory data analysis

Scatter plots are one of the most fundamental tools in data visualization, especially when you want to explore the relationship between two numeric variables. In a scatter plot, each point represents a pair of values from your dataset, with the position along the x-axis and y-axis corresponding to the values of each variable. This type of plot is commonly used in exploratory data analysis to quickly identify patterns, such as positive or negative associations, outliers, or clusters within your data. Scatter plots are particularly useful in fields like economics, biology, and social sciences, where understanding the connection between variables is essential for analysis and decision-making.

library(ggplot2)

# Sample data frame with two numeric variables
df <- data.frame(
  height = c(150, 160, 165, 170, 175, 180, 185, 190),
  weight = c(50, 55, 60, 65, 70, 75, 80, 85)
)

# Basic scatter plot: height vs. weight
ggplot(df, aes(x = height, y = weight)) +
  geom_point()

When you look at a scatter plot, you can interpret the relationship between the two variables by examining the pattern of the points. If the points tend to rise together from left to right, it suggests a **positive relationship**—meaning as one variable increases, so does the other. If the points fall from left to right, this indicates a **negative relationship**, where an increase in one variable corresponds to a decrease in the other. A scatter plot with no clear pattern suggests little or no relationship between the variables. You can also spot **outliers**—points that do not fit the overall trend—and **clusters** of points that might represent subgroups in your data. Recognizing these patterns helps you decide on further analysis or modeling steps, and can guide you toward more complex visualizations or statistical techniques.


library(testthat)

source("user_code.R")
test_that("plot_speed_vs_dist creates a plot with correct x and y", {
  user_plot <- plot_speed_vs_dist(cars_data)
  expected_plot <- ggplot(cars_data, aes(x = speed, y = dist)) + geom_point()
  expect_true(inherits(user_plot, "ggplot"),
    info = "The function should return a ggplot object.")
  expect_equal(user_plot$mapping$x, expected_plot$mapping$x,
    info = "The x aesthetic should be mapped to speed.")
  expect_equal(user_plot$mapping$y, expected_plot$mapping$y,
    info = "The y aesthetic should be mapped to dist.")
})

test_that("plot_speed_vs_dist works with cars_data dataset", {
  expect_silent(plot_speed_vs_dist(cars_data))
})

test_that("plot_speed_vs_dist includes correct number of points", {
  user_plot <- plot_speed_vs_dist(cars_data)
  built <- ggplot_build(user_plot)
  expect_equal(nrow(built$data[[1]]), nrow(cars_data),
    info = "The plot should contain the same number of points as rows in the data.")
})

test_main.R

Master the art of data visualization in R using the powerful ggplot2 package. This course guides you from the basics of plotting to advanced visualization techniques, enabling you to create insightful and visually appealing graphics for data analysis and presentation.

Comprehensive exploration of information visualization in R using ggplot2, from foundational concepts to advanced techniques.

Basic Scatter Plots

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