library(testthat)
library(ggplot2)

source("user_code.R")

test_that("plot_city_temps exists and is a function", {
  expect_true(exists("plot_city_temps", inherits = TRUE),
              info = "plot_city_temps should be defined.")
  expect_true(is.function(plot_city_temps),
              info = "plot_city_temps should be a function.")
})

test_that("plot_city_temps returns a ggplot object", {
  p <- plot_city_temps(city_temps)
  expect_true(ggplot2::is.ggplot(p),
              info = "plot_city_temps should return a ggplot object.")
})

test_that("plot uses correct data and mappings", {
  p <- plot_city_temps(city_temps)

  expect_equal(p$data, city_temps,
               info = "Plot should use the input data frame.")

  expect_equal(rlang::as_label(p$mapping$x), "date",
               info = "x-axis should be mapped to date.")
  expect_equal(rlang::as_label(p$mapping$y), "high",
               info = "y-axis should be mapped to high temperature.")
})

test_that("plot includes point and smooth layers", {
  p <- plot_city_temps(city_temps)

  geoms <- vapply(p$layers, function(l) class(l$geom)[1], character(1))

  expect_true("GeomPoint" %in% geoms,
              info = "Plot should include geom_point.")
  expect_true("GeomSmooth" %in% geoms,
              info = "Plot should include geom_smooth.")
})

test_that("plot annotates the hottest day correctly", {
  p <- plot_city_temps(city_temps)

  # Expected hottest day
  hottest_idx <- which.max(city_temps$high)
  hottest_date <- city_temps$date[hottest_idx]
  hottest_temp <- city_temps$high[hottest_idx] + 1

  b <- ggplot_build(p)

  # Find the text layer by geom class
  geom_classes <- vapply(p$layers, function(l) class(l$geom)[1], character(1))
  text_layer_idx <- which(geom_classes %in% c("GeomText", "GeomLabel"))[1]

  expect_false(is.na(text_layer_idx),
               info = "Plot should include a text annotation layer.")

  text_data <- b$data[[text_layer_idx]]

  expect_true("label" %in% names(text_data), info = "Text layer should contain 'label'.")
  expect_true(any(text_data$label == "Hottest Day"),
              info = "Annotation label should be 'Hottest Day'.")

  # Compare x/y for the row with the label
  row <- text_data[text_data$label == "Hottest Day", , drop = FALSE][1, ]

  # x is numeric (date converted), so compare via numeric Date
  expect_equal(as.numeric(row$x), as.numeric(hottest_date),
               info = "Annotation x-position should be hottest date.")

  expect_equal(row$y, hottest_temp,
               tolerance = 1e-8,
               info = "Annotation y-position should be hottest temperature + 1.")
})

test_that("plot uses a minimal theme", {
  p <- plot_city_temps(city_temps)

  # theme_minimal removes panel background
  expect_true(
    inherits(p$theme$panel.background, "element_blank"),
    info = "plot should use a minimal-style theme."
  )
})


test_main.R

Master the essentials of data visualization and reporting in R. Learn to create compelling graphics, perform exploratory data analysis, and communicate insights effectively using R's powerful tools.

Learn the basics of data visualization in R, focusing on the ggplot2 package and its core plotting capabilities.

Dive into EDA techniques in R, including summary statistics, visual inspection, and outlier detection.

Learn how to turn your visualizations and analyses into clear, actionable reports.

Challenge: Build a Layered Visualization

Solution