tests/testthat/test-helpers.R

In tidyplots: Tidy Plots for Scientific Papers

test_that("is_hex_vector works", {
  expect_equal(is_hex_vector(c(2,3,3)), FALSE)
  expect_equal(is_hex_vector(c("jdj","koi", "klk")), FALSE)
  expect_equal(is_hex_vector(c("#jdj","#koi", "klk")), FALSE)
  expect_equal(is_hex_vector(c("abc","094", "aff")), FALSE)
  expect_equal(is_hex_vector(c("#jdj","#koi", "#klk")), FALSE)
  expect_equal(is_hex_vector(c("#889098","#89f987", "0099ff")), FALSE)
  expect_equal(is_hex_vector(c("#889098","#89f987", "#ff99ff")), TRUE)
  expect_equal(is_hex_vector(c("#889098","#89f987", "#ff99ffff")), TRUE)
  expect_equal(is_hex_vector(c("#889098","#89f987", "#ff99ffff0")), FALSE)
  expect_equal(is_hex_vector(c("#889098","#89f987", "#fff")), TRUE)
  expect_equal(is_hex_vector(c("#889098","#89f987", "#fff", NA)), TRUE)
})

test_that("check_input works", {
  p1 <-
    study |>
    ggplot2::ggplot(ggplot2::aes(treatment, score, color = treatment)) +
    ggplot2::geom_point()
  tp <-
    study |>
    tidyplot(treatment, score, color = treatment) |>
    add_data_points_beeswarm()
  p2 <- p1
  pw <- patchwork::wrap_plots(p1, p2)
  gg_list <- list(p1, p2)
  pw_list <- list(pw, pw)
  tp_list <- list(tp, tp)
  expect_equal(check_input(p1), "gg")
  expect_equal(check_input(pw), "pw")
  expect_equal(check_input(tp), "tp")
  expect_equal(check_input(gg_list), "gg_list")
  expect_equal(check_input(pw_list), "pw_list")
  expect_equal(check_input(tp_list), "tp_list")
  expect_equal(check_input(c("hello")), "none")
})

test_that("*_rows() functions work", {
  animals |>
    tidyplot(x = weight, y = size) |>
    add_data_points() |>
    add_data_points(data = all_rows(),
                    color = "red", shape = 1, size = 3) |>
    vdiffr::expect_doppelganger("All rows", fig = _)

  animals |>
    tidyplot(x = weight, y = size) |>
    add_data_points() |>
    add_data_points(data = max_rows(weight, n = 3),
                    color = "red", shape = 1, size = 3) |>
    vdiffr::expect_doppelganger("Highlight 3 animals with the highest weight", fig = _)

  animals |>
    tidyplot(x = weight, y = size) |>
    add_data_points() |>
    add_data_points(data = min_rows(weight, n = 3),
                    color = "red", shape = 1, size = 3) |>
    vdiffr::expect_doppelganger("Highlight 3 animals with the lowest weight", fig = _)

  animals |>
    tidyplot(x = weight, y = size) |>
    add_data_points() |>
    add_data_points(data = first_rows(n = 3),
                    color = "red", shape = 1, size = 3) |>
    vdiffr::expect_doppelganger("Highlight the first 3 animals in the dataset", fig = _)

  animals |>
    tidyplot(x = weight, y = size) |>
    add_data_points() |>
    add_data_points(data = last_rows(n = 3),
                    color = "red", shape = 1, size = 3) |>
    vdiffr::expect_doppelganger("Highlight the last 3 animals in the dataset", fig = _)

  set.seed(42)

  animals |>
    tidyplot(x = weight, y = size) |>
    add_data_points() |>
    add_data_points(data = sample_rows(n = 3),
                    color = "red", shape = 1, size = 3) |>
    vdiffr::expect_doppelganger("Highlight 3 random animals", fig = _)
})