test_that("iq_shares returns correct class", {
  d <- iq_sample_data("income")
  s <- iq_shares(d$income)
  expect_s3_class(s, "iq_shares")
})

test_that("default breaks produce 4 segments", {
  d <- iq_sample_data("income")
  s <- iq_shares(d$income)
  expect_equal(nrow(s$shares), 4)
})

test_that("shares sum to approximately 1", {
  d <- iq_sample_data("income")
  s <- iq_shares(d$income)
  expect_equal(sum(s$shares$income_share), 1, tolerance = 0.05)
})

test_that("equal distribution has proportional shares", {
  s <- iq_shares(rep(100, 100))
  # Bottom 50% should get ~50% of income in an equal distribution
  expect_equal(s$shares$income_share[1], 0.50, tolerance = 0.05)
})

test_that("custom quintile breaks work", {
  d <- iq_sample_data("income")
  s <- iq_shares(d$income, breaks = c(0.20, 0.40, 0.60, 0.80, 1.00))
  expect_equal(nrow(s$shares), 5)
})

test_that("top segments capture disproportionate share for skewed data", {
  d <- iq_sample_data("income")
  s <- iq_shares(d$income)
  # Top 1% should hold more than 1% of income
  top1 <- s$shares[s$shares$segment == "Top 1%", ]
  expect_true(top1$income_share > top1$pop_share)
})

test_that("print method runs without error", {
  expect_no_error(print(iq_shares(iq_sample_data("income")$income)))
})