
library(testthat)

source("user_code.R")
if (!requireNamespace("ggplot2", quietly = TRUE)) install.packages("ggplot2")
library(ggplot2)
library(testthat)

source("user_code.R")
test_that("p1 exists and is a ggplot object with correct years", {
  expect_true(exists("p1"), info = "p1 should exist.")
  expect_true("ggplot" %in% class(p1), info = "p1 should be a ggplot object.")
  data_years <- layer_data(p1)$x
  expect_equal(sort(unique(data_years)), 2016:2018, info = "p1 should use years 2016-2018.")
  expect_true(any(grepl("Customer Satisfaction: 2016-2018", p1$labels$title)), info = "p1 should have correct title.")
})

test_that("p2 exists, is ggplot, uses color, and correct years", {
  expect_true(exists("p2", inherits = TRUE), info = "p2 should exist.")
  p2_obj <- get("p2", inherits = TRUE)

  expect_true(ggplot2::is.ggplot(p2_obj), info = "p2 should be a ggplot object.")

  # Check years from the data used to build p2 (reliable)
  expect_equal(sort(unique(p2_obj$data$year)), 2016:2020,
               info = "p2 should use years 2016-2020.")

  # Check geoms
  p2_layers <- vapply(p2_obj$layers, function(l) class(l$geom)[1], character(1))
  expect_true(any(p2_layers == "GeomPoint"), info = "p2 should include points.")

  # Check color mapping (ggplot stores it as 'colour')
  mapped_colour <- p2_obj$mapping$colour
  if (is.null(mapped_colour)) mapped_colour <- p2_obj$mapping$color

  expect_false(is.null(mapped_colour), info = "p2 should map color/colour to a variable.")
  expect_equal(rlang::as_label(mapped_colour), "score",
               info = "p2 should map color/colour to 'score'.")

  # Check title
  expect_equal(p2_obj$labels$title, "Customer Satisfaction Improvement: 2016-2020",
               info = "p2 should have correct title.")
})


test_that("p3 exists, is ggplot, uses annotation, and correct years", {
  expect_true(exists("p3"), info = "p3 should exist.")
  expect_true("ggplot" %in% class(p3), info = "p3 should be a ggplot object.")
  data_years <- layer_data(p3)$x
  expect_equal(sort(unique(data_years)), 2016:2023, info = "p3 should use years 2016-2023.")
  # Check for annotation: geom_text layer
  p3_layers <- sapply(p3$layers, function(l) class(l$geom)[1])
  expect_true(any(p3_layers == "GeomText"), info = "p3 should include annotation with geom_text.")
  expect_true(any(grepl("Customer Satisfaction with Key Event: 2016-2023", p3$labels$title)), info = "p3 should have correct title.")
})

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: Tell a Data Story

Oplossing

Challenge: Tell a Data Story

Oplossing