library(testthat)

source("user_code.R")

# Fast PNG dimension reader (no external packages)
read_png_dims <- function(path) {
  con <- file(path, "rb")
  on.exit(close(con), add = TRUE)

  sig <- readBin(con, "raw", n = 8)
  stopifnot(identical(sig, as.raw(c(0x89, 0x50, 0x4E, 0x47, 0x0D, 0x0A, 0x1A, 0x0A))))

  len  <- readBin(con, "integer", n = 1, size = 4, endian = "big", signed = FALSE)
  type <- rawToChar(readBin(con, "raw", n = 4))
  stopifnot(type == "IHDR", len >= 13)

  w <- readBin(con, "integer", n = 1, size = 4, endian = "big", signed = FALSE)
  h <- readBin(con, "integer", n = 1, size = 4, endian = "big", signed = FALSE)

  c(width = as.numeric(w), height = as.numeric(h))
}

test_that("p exists and is a ggplot object", {
  # Look up through parent envs; many graders/source setups put objects elsewhere
  expect_true(exists("p", inherits = TRUE), info = "Variable 'p' does not exist.")

  p_obj <- get("p", inherits = TRUE)

  # Robust ggplot check
  expect_true(ggplot2::is.ggplot(p_obj), info = "'p' is not a ggplot object.")
})

test_that("custom_plot.png is created and has correct pixel dimensions (preferred) or correct aspect", {
  expect_true(file.exists("custom_plot.png"), info = "custom_plot.png was not created.")

  dims <- read_png_dims("custom_plot.png")
  w <- dims[["width"]]
  h <- dims[["height"]]

  # Prefer exact 5in*250dpi and 4in*250dpi
  if (!(w == 1250 && h == 1000)) {
    # Fallback: enforce 5:4 aspect ratio (in case device backend changes pixel dims)
    expect_true(
      abs((h / w) - (4 / 5)) <= 0.01,
      info = paste0("PNG aspect ratio is incorrect. Got ", w, "x", h, " (expected ~5:4).")
    )
  } else {
    succeed()
  }
})

test_that("custom_plot.pdf is created and has a valid PDF header", {
  expect_true(file.exists("custom_plot.pdf"), info = "custom_plot.pdf was not created.")

  con <- file("custom_plot.pdf", "rb")
  on.exit(close(con), add = TRUE)
  header <- readChar(con, nchars = 5, useBytes = TRUE)

  expect_equal(header, "%PDF-", info = "custom_plot.pdf does not have a valid PDF header.")
})

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: Export and Share Your Work

Ratkaisu

Challenge: Export and Share Your Work

Ratkaisu