test_that("conf_int works with grouped data", {
  res <- conf_int(iris, var = Sepal.Length, group = Species, plot = FALSE)

  expect_s3_class(res, "tbl_df")
  expect_equal(nrow(res), 3)
  expect_true(all(c("Species", "n", "media", "desvio_padrao", "se", "gl", "t_calc", "t_tab", "LI", "LS") %in% colnames(res)))

  # Validar valores estatísticos para setosa
  setosa_data <- iris$Sepal.Length[iris$Species == "setosa"]
  expect_equal(res$n[1], length(setosa_data))
  expect_equal(res$media[1], mean(setosa_data))
  expect_equal(res$desvio_padrao[1], sd(setosa_data))

  se_setosa <- sd(setosa_data) / sqrt(length(setosa_data))
  ttab_setosa <- qt(0.975, df = length(setosa_data) - 1)
  expect_equal(res$LI[1], mean(setosa_data) - ttab_setosa * se_setosa)
  expect_equal(res$LS[1], mean(setosa_data) + ttab_setosa * se_setosa)
})

test_that("conf_int works without grouping variable", {
  res <- conf_int(iris, var = Sepal.Length, plot = FALSE)

  expect_s3_class(res, "tbl_df")
  expect_equal(nrow(res), 1)
  expect_true(all(c("n", "media", "desvio_padrao", "se", "gl", "t_calc", "t_tab", "LI", "LS") %in% colnames(res)))
  expect_equal(res$n[1], nrow(iris))
  expect_equal(res$media[1], mean(iris$Sepal.Length))
})

test_that("conf_int works with grouped_df from dplyr", {
  res <- iris |>
    dplyr::group_by(Species) |>
    conf_int(Petal.Length, plot = FALSE)

  expect_equal(nrow(res), 3)
  expect_true("Species" %in% colnames(res))
})

test_that("conf_int generates plot object as attribute", {
  res <- conf_int(iris, var = Sepal.Length, group = Species, plot = TRUE)

  p <- attr(res, "plot")
  expect_s3_class(p, "ggplot")
  expect_equal(p$labels$x, "Sepal.Length")
  expect_equal(p$labels$y, "Species")
})

test_that("conf_int handles input errors gracefully", {
  expect_error(conf_int(iris, var = InexistentColumn, plot = FALSE))
  expect_error(conf_int(iris, var = Species, plot = FALSE)) # non-numeric
  expect_error(conf_int(iris, var = Sepal.Length, group = InexistentGroup, plot = FALSE))
})