context("Tidyverse methods for specific classes")

test_that("ped class is preserved after dplyr operations", {

  data("tumor")
  tumor <- dplyr::slice(tumor, 2:3)
  ped <- as_ped(
    data    = tumor,
    formula = Surv(days, status) ~ complications + age,
    cut     = c(0, 100, 400),
    id      = "id")

  expect_is(filter(ped, id == 1), "ped")
  expect_is(slice(ped, 1), "ped")
  expect_is(arrange(ped, desc(id)), "ped")
  expect_is(select(ped, id), "ped")
  expect_is(rename(ped, ID = id), "ped")
  expect_is(mutate(ped, id = id + 1), "ped")
  expect_is(transmute(ped, id = id + 1), "ped")
  expect_is(sample_n(ped, 1), "ped")
  expect_is(sample_frac(ped, 0.5), "ped")
  expect_is(right_join(distinct(ped, id, interval), ped), "ped")

})


test_that("attributes are preserved", {
  # recurrent events data
  test_df <- data.frame(
    id     = c(1,1, 2,2,2),
    tstart = c(0, .5, 0, .8, 1.2),
    tstop  = c(.5, 3, .8, 1.2, 3),
    status = c(1, 0, 1, 1, 0),
    enum   = c(1, 2, 1, 2, 3),
    age    = c(50, 50, 24, 24, 24))
  # GAP timescale
  gap_df <- as_ped(
    data       = test_df,
    formula    = Surv(tstart, tstop, status)~ enum + age,
    transition = "enum",
    id         = "id",
    timescale  = "gap")

  expect_subset(names(attributes(gap_df)), c("names", "row.names", "class",
    "breaks", "id_var", "intvars", "trafo_args", "time_var"))
  expect_subset(
    names(attributes(mutate(gap_df, age = 10))),
    c("names", "row.names", "class", "breaks", "id_var", "intvars", "trafo_args",
    "time_var"))
  expect_subset(
    names(attributes(group_by(gap_df, id))),
    c("names", "row.names", "class", "breaks", "id_var", "intvars", "trafo_args",
    "time_var", "groups"))
  expect_subset(
    names(attributes(ungroup(group_by(gap_df, id)))),
    c("names", "row.names", "class", "breaks", "id_var", "intvars", "trafo_args",
    "time_var"))

})