tests/testthat/test-stat-ydensity.R

In ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics

test_that("calc_bw() requires at least two values and correct method", {
  expect_snapshot_error(calc_bw(1, "nrd0"))
  expect_silent(calc_bw(1:5, "nrd0"))
  expect_snapshot_error(calc_bw(1:5, "test"))
})

test_that("`drop = FALSE` preserves groups with 1 observations", {
  df <- head(data_frame0(
    x = factor(rep(1:2, each = 4)),
    y = rep(1:2, 4),
    g = rep(c("A", "A", "B", 'B'), 2)
  ), -1)

  p <- ggplot(df, mapping = aes(x, y, fill = g))

  expect_snapshot_warning(
    ld <- get_layer_data(p + geom_violin(drop = TRUE))
  )
  expect_length(unique(ld$x), 3)

  expect_snapshot_warning(
    ld <- get_layer_data(p + geom_violin(drop = FALSE))
  )
  expect_length(unique(ld$x), 4)
})

test_that("mapped_discrete class is preserved", {

  df <- data_frame0(
    x = factor(rep(c("A", "C"), each = 3), c("A", "B", "C")),
    y = 1:6
  )

  ld <- get_layer_data(
    ggplot(df, aes(x, y)) + geom_violin() +
      scale_x_discrete(drop = FALSE)
  )

  expect_s3_class(ld$x, "mapped_discrete")
  expect_equal(unique(ld$x), c(1, 3))
})

test_that("quantiles are based on actual data (#4120)", {

  df <- data.frame(y = 0:10)
  q <- seq(0.1, 0.9, by = 0.1)

  p <- ggplot(df, aes("X", y)) +
    stat_ydensity(quantiles = q)
  ld <- get_layer_data(p)

  expect_equal(ld$y[!is.na(ld$quantile)], 1:9)
})