ds <- readr::read_csv(here::here("/Volumes/Data/REDCap/DDV/talos_ddv.csv"))

df_raw <- ds |>
  dplyr::filter(!pase_score_missings_0, !pase_score_missings_4) |>
  dplyr::transmute(
    id = dplyr::row_number(),
    pase_0 = pase_score_sum_0,
    pase_4 = pase_score_sum_4,
    pase_0_cut = as.numeric(stRoke::quantile_cut(
      x = pase_0,
      groups = 4,
      group.names = paste0(1:4)
    )),
    pase_6_cut = as.numeric(stRoke::quantile_cut(
      x = pase_4,
      y = pase_0,
      groups = 4,
      inc.outs = TRUE,
      group.names = paste0(1:4)
    )),
    pase_diff = pase_4 - pase_0,
    pase_diff_rel = pase_diff / pase_0,
    pase_0_rank = rank(pase_0, ties.method = "first"),
    pase_4_rank = rank(pase_4, ties.method = "first"),
    change = dplyr::case_when(
      pase_0_cut %in% 2:4 & pase_6_cut == 1 ~ "drop",
      pase_6_cut %in% 2:4 & pase_0_cut == 1 ~ "hop",
      pase_0_cut %in% 2:4 & pase_6_cut %in% 2:4 ~ "hh",
      pase_0_cut %in% 1 & pase_6_cut == 1 ~ "ll"
    ),
    change_any = factor(dplyr::case_when(
      pase_6_cut > pase_0_cut ~ "hop",
      pase_6_cut < pase_0_cut ~ "drop",
      pase_0_cut %in% 2:4 & pase_6_cut %in% 2:4 ~ "hh",
      pase_0_cut %in% 1 & pase_6_cut == 1 ~ "ll"
    )),
    change_rel = factor(dplyr::case_when(
      pase_diff_rel > .5 ~ "hop",
      pase_diff_rel < -.5 ~ "drop",
      .default = "stat"
    ))
  )

df_raw |> skimr::skim()

df_long <- df_raw |>
  dplyr::select(pase_0_rank, pase_4_rank, change_rel,id) |>
  tidyr::pivot_longer(dplyr::starts_with("pase_"), names_to = "time", values_to = "pase")

df_long <- df_raw |>
  dplyr::select(pase_0, pase_4, change_rel,id) |>
  tidyr::pivot_longer(dplyr::starts_with("pase_"), names_to = "time", values_to = "pase")

ggplot(df_long, aes(x = time, y = pase,group=id,colour = change_rel)) +
  geom_line()+
  facet_wrap(~change_rel)