library(testthat)

source("user_code.R")

test_that("bar plot for data1 is created and uses correct geometry", {
  expect_true(exists("p1", inherits = TRUE), info = "Variable 'p1' does not exist.")
  p1_obj <- get("p1", inherits = TRUE)

  geoms <- vapply(p1_obj$layers, function(l) class(l$geom)[1], character(1))
  expect_true(any(grepl("GeomBar", geoms)), info = "Bar plot should use geom_bar.")

  # Optional: verify fill mapping exists
  mapped_fill <- p1_obj$mapping$fill
  expect_false(is.null(mapped_fill), info = "Bar plot should map fill aesthetic.")
  expect_equal(rlang::as_label(mapped_fill), "category",
               info = "Bar plot should map fill to 'category'.")
})

test_that("line plot for data2 is created and uses correct geometry", {
  expect_true(exists("p2", inherits = TRUE), info = "Variable 'p2' does not exist.")
  p2_obj <- get("p2", inherits = TRUE)

  geoms <- vapply(p2_obj$layers, function(l) class(l$geom)[1], character(1))
  expect_true(any(grepl("GeomLine", geoms)), info = "Line plot should use geom_line.")
})

test_that("scatter plot for data3 is created and uses correct geometry and color aesthetic", {
  expect_true(exists("p3", inherits = TRUE), info = "Variable 'p3' does not exist.")
  p3_obj <- get("p3", inherits = TRUE)

  geoms <- vapply(p3_obj$layers, function(l) class(l$geom)[1], character(1))
  expect_true(any(grepl("GeomPoint", geoms)), info = "Scatter plot should use geom_point.")

  # ggplot stores color aesthetic as 'colour'
  mapped_colour <- p3_obj$mapping$colour
  if (is.null(mapped_colour)) mapped_colour <- p3_obj$mapping$color

  expect_false(is.null(mapped_colour),
               info = "Scatter plot should map color/colour aesthetic.")
  expect_equal(rlang::as_label(mapped_colour), "group",
               info = "Scatter plot should map color/colour to 'group'.")
})

test_that("plots are arranged in a 2x2 grid and fourth slot is empty", {
  skip_if_not_installed("gridExtra")

  # Ensure p4 exists and is a null grob
  expect_true(exists("p4", inherits = TRUE), info = "Variable 'p4' does not exist.")
  p4_obj <- get("p4", inherits = TRUE)

  expect_true(inherits(p4_obj, "null") || inherits(p4_obj, "grob"),
              info = "p4 should be a grob (expected grid::nullGrob()).")

  # Ensure arranging runs without error
  p1_obj <- get("p1", inherits = TRUE)
  p2_obj <- get("p2", inherits = TRUE)
  p3_obj <- get("p3", inherits = TRUE)

  expect_error(
    gridExtra::grid.arrange(p1_obj, p2_obj, p3_obj, p4_obj, ncol = 2),
    NA,
    info = "Should arrange plots in a 2x2 grid with one empty slot."
  )
})

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: Create Your First Plots

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