Summary  
This chapter explains how to map data variables to visual properties of a plot (aesthetics) using the aes() function, allowing dynamic control of attributes like axis positions, color, and size in code.  

General domain of usage  
Data visualization

When using `ggplot2` in R, **aesthetic mappings** are the way you connect your data variables to visual properties of your plot. These properties, called **aesthetics**, include features like position on the x- and y-axes, color, size, shape, and more. By mapping variables to these aesthetics, you make your data visually meaningful: for example, mapping a variable to color can help you distinguish groups, while mapping to size can show magnitude. Aesthetic mappings are set inside the `aes()` function, either globally in `ggplot()` or locally within a specific geom layer. This approach allows you to quickly explore relationships and patterns in your data, making your plots both informative and visually appealing.

library(ggplot2)

# Sample data
df <- data.frame(
  height = c(150, 160, 170, 180, 190),
  weight = c(55, 65, 72, 80, 90),
  gender = c("Female", "Male", "Female", "Male", "Male"),
  age = c(23, 35, 29, 41, 33)
)

# Scatter plot mapping variables to aesthetics
ggplot(df, aes(x = height, y = weight, color = gender, size = age)) +
  geom_point()

Changing which variables are mapped to which aesthetics can dramatically alter both the appearance of your plot and the insights you can draw from it. For instance, mapping `gender` to **color** allows you to easily compare groups, while mapping `age` to **size** adds an extra dimension, showing how another variable relates to the others. If you swap these mappings - say, mapping `age` to color and `gender` to size - the visual emphasis and the story your plot tells will change. Thoughtful **aesthetic mapping** helps you highlight patterns, identify outliers, and communicate your findings more effectively.

library(testthat)
library(ggplot2)

# Завантажуємо код користувача
source("user_code.R")

# Наш золотий стандарт для чистого виводу
check_logic <- function(test_name, condition, error_msg) {
  test_that(test_name, {
    if (isTRUE(condition)) {
      expect_true(TRUE)
    } else {
      fail(error_msg)
    }
  })
}

# --- ПОШУК ОБ'ЄКТА ПЛАТФОРМОЮ ---
# Шукаємо останній створений графік, якщо plt не знайдено прямо
if (!exists("plt")) {
  plt <- last_plot()
}

# --- ТЕСТИ ---

# 1. Перевірка об'єкта
ok1 <- !is.null(plt) && inherits(plt, "ggplot")
check_logic(
  "1. A ggplot object is created",
  ok1,
  "We couldn't find a ggplot object. Make sure you created a plot using ggplot()."
)

# 2. Перевірка мапінгів
ok2 <- FALSE
if (!is.null(plt)) {
  # ggplot_build розгортає всі мапінги (навіть якщо вони всередині geom_point)
  # Це найнадійніший спосіб перевірити, що бачить сам ggplot
  built <- ggplot_build(plt)
  
  # Отримуємо вирази мапінгу
  # Користуємося rlang для надійного отримання тексту
  check_aes_name <- function(p, name, expected) {
    # Перевіряємо глобальний мапінг
    m <- p$mapping[[name]]
    # Якщо там немає, перевіряємо мапінг першого шару
    if (is.null(m) && length(p$layers) > 0) {
      m <- p$layers[[1]]$mapping[[name]]
    }
    
    if (is.null(m)) return(FALSE)
    
    # Перетворюємо в текст і видаляємо тильду ~
    txt <- as.character(rlang::quo_get_expr(rlang::enquo(m)))
    txt <- gsub("~", "", txt[length(txt)])
    return(txt == expected)
  }

  ok2 <- check_aes_name(plt, "x", "hours_studied") &&
         check_aes_name(plt, "y", "exam_score") &&
         (check_aes_name(plt, "colour", "student_group") || check_aes_name(plt, "color", "student_group")) &&
         check_aes_name(plt, "size", "assignments_completed")
}

check_logic(
  "2. Correct aesthetic mappings (x, y, color, size)",
  ok2,
  "Check your aes() settings. x should be hours_studied, y should be exam_score, color should be student_group, and size should be assignments_completed."
)

# 3. Перевірка наявності точок
ok3 <- FALSE
if (!is.null(plt)) {
  # Перевіряємо кожен шар, чи є там точки
  layers_classes <- sapply(plt$layers, function(x) class(x$geom)[1])
  ok3 <- any(layers_classes == "GeomPoint")
}

check_logic(
  "3. Points layer added correctly",
  ok3,
  "You need to add geom_point() to your plot."
)

test_main.R

This beginner-friendly course introduces learners to the fundamentals of data visualization using the ggplot2 package in R. Through practical exercises, students will learn how to create a variety of essential visualizations, including scatter plots, bar charts, histograms, and boxplots. The course emphasizes hands-on experience, guiding learners step-by-step through the process of building, customizing, and interpreting visualizations to effectively communicate data insights.

Master all essential aspects of data visualization in R using ggplot2, from basic plots to advanced customization and exporting graphics.

Mapping Variables to Aesthetics

Ratkaisu