## Examples

# stRoke::talos |>
#   dplyr::mutate(across(tidyselect::starts_with("mrs"), as.numeric)) |>
#   stRoke::generic_stroke(group = "rtreat", score = "mrs_6", variables = c("hypertension", "diabetes", "civil")) |>
#   purrr::pluck(3)
#
# stRoke::talos |>
#   dplyr::mutate(mrs_6_bin = as.numeric(mrs_6 < 1)) |>
#   finalfit::or_plot(dependent = "mrs_6_bin", explanatory = c("hypertension", "diabetes", "civil"))


# Consider utilising plotting like finalfit::or_plot


## Sample data
# df_coefs <- list(
#   mrs_6 = stRoke::talos |>
#     dplyr::select(tidyselect::all_of(c("mrs_6", "rtreat", "hypertension", "diabetes", "civil"))) |>
#     lm(data = _, mrs_6 ~ .),
#   mrs_1 = stRoke::talos |>
#     dplyr::select(tidyselect::all_of(c("mrs_1", "rtreat", "hypertension", "diabetes", "civil"))) |>
#     lm(data = _, mrs_1 ~ .)
# ) |>
#   lapply(gtsummary::tbl_regression) |>
# purrr::map(function(.x) {
#   .x |> purrr::pluck("table_body") |>
#     dplyr::select(tidyselect::all_of(c("variable","estimate"))) |>
#     na.omit()
#   }) |> purrr::reduce(dplyr::full_join,by="variable") |>
#   setNames(c("variable","increase","decrease"))

get_coefs(path = here::here("1 PA Decline/Fra DDV/240624/pa_change_analyses.docx"),index.table = 2)


source(here::here("1 PA Decline/dst import.R"))
df_coefs_raw <- get_coefs(path = here::here("1 PA Decline/Fra DDV/240624/pa_change_analyses.docx"),index.table = 2) |>
  dplyr::filter(variable != "(Intercept)") |>
  dplyr::select(variable, hop_median, drop_median) |>
  setNames(c("variable", "INCREASE", "DECREASE"))

## Real work
df_coefs <- df_coefs_raw |>
  dplyr::mutate(dplyr::across(
    tidyselect::all_of(c("INCREASE", "DECREASE")),
    function(.x) {
      # signif(
        as.numeric(.x)#,
      #   3
      # )
    }
  )) |>
  dplyr::mutate(variable = dplyr::if_else(variable == "Alcohol consumption above recommendations",
    "High alcohol consumption", variable
  ))|>
  ## Important step to keep the data ordered for ggplot
  (function(.y) {
    .y |> dplyr::mutate(variable = factor(variable, levels = rev(.y$variable)))
  })()

## Highest ORs
list(df_coefs[c(1,2)],df_coefs[c(1,3)]) |> 
  setNames(names(df_coefs)[2:3]) |> 
  purrr::map(function(.x){
  .x |> 
      setNames(c("var","val"))|> 
      dplyr::mutate(sorting=abs(log(val))) |> 
      dplyr::arrange(1-sorting) |> 
      dplyr::mutate(dplyr::across(dplyr::where(is.numeric),~signif(.x,2))) |> 
      head(5)
})


df_long <- df_coefs |>
  tidyr::pivot_longer(cols = !tidyselect::matches("variable")) |>
  dplyr::mutate(name = factor(name, levels = rev(unique(name))))


cols <- c(
  "#CE0045",
  "#66c1a3"
) # Lighter Midtrød


create_log_tics <- function(data){
  sort(round(unique(c(1/data,data)),2))
}

x.tics <- create_log_tics(c(.25, .4, .6, .8, 1))

legend.title=""

levels(df_long$name) <- c("OR for decrease",
                          "OR for increase")

p1 <- df_long |>
  # dplyr::filter(name=="decrease") |>
  ggplot2::ggplot(ggplot2::aes(x = log(value), y = variable, color = name, fill = name)) +
  ggplot2::geom_vline(ggplot2::aes(xintercept = 0), linewidth = .5, linetype = "dashed") +
  # ggplot2::geom_errorbarh(ggplot2::aes(xmax = boxCIHigh, xmin = boxCILow), size = .5, height =
  #                  .2, color = "gray50") +
  ggplot2::geom_point(ggplot2::aes(shape = name), size = 6)  + 
  # ggplot2::coord_trans(x = scales:::exp_trans(10)) +
  ggplot2::scale_x_continuous(
    breaks = log(x.tics),
    labels = x.tics,
    limits = log(range(x.tics))
  ) +
  ggplot2::scale_color_manual(values = cols) +
  ggplot2::scale_fill_manual(values = cols) +
  ggplot2::scale_shape_manual(values=c(25,24)) +
  ggplot2::theme_bw() +
  ggplot2::theme(panel.grid.minor = ggplot2::element_blank(),
                 # legend.title = ggplot2::element_text(""),
                 legend.position = "bottom") +
  ggplot2::ylab("") +
  ggplot2::xlab("Odds ratio (log)") +
  ggplot2::labs(shape=legend.title,
                color=legend.title,
                fill=legend.title)
  
# 
# png(
#   filename = here::here("1 PA Decline/coef_plot_change_ARTICLEA.png"),
#   units = "mm",
#   width = 300,
#   height = 300,
#   pointsize = 5,
#   res = 300
# )
# p1 +
#   # ggplot2::theme_minimal() +
#   ggplot2::theme( 
#     # legend.position = "none",
#     # panel.grid.major = ggplot2::element_blank(),
#     # panel.grid.minor = ggplot2::element_blank(),
#     # axis.text.y = ggplot2::element_blank(),
#     # axis.title.y = ggplot2::element_blank(),
#     # axis.text.x = element_blank(),
#     text = ggplot2::element_text(size = 25)#,
#     # plot.title = element_text(),
#     # panel.background = ggplot2::element_rect(fill = "transparent")#,
#     # plot.background = ggplot2::element_rect(fill = "transparent", color = NA)
#   )
# dev.off()


x.tics <- create_log_tics(c(.25, .6, 1))

ggplot2::ggsave(
  filename = here::here("1 PA Decline/coef_plot_change_ARTICLEA_facet.png"),
  plot = p1 +
    ggplot2::scale_x_continuous(
      breaks = log(x.tics),
      labels = x.tics,
      limits = log(range(x.tics))
    ) +
    ggplot2::facet_wrap(facets = ggplot2::vars(name),ncol=2) +
    # ggplot2::theme_minimal() +
    ggplot2::theme( 
      legend.position = "none",
      # panel.grid.major = ggplot2::element_blank(),
      # panel.grid.minor = ggplot2::element_blank(),
      # axis.text.y = ggplot2::element_blank(),
      # axis.title.y = ggplot2::element_blank(),
      # axis.text.x = element_blank(),
      text = ggplot2::element_text(size = 16)#,
      # plot.title = element_text(),
      # panel.background = ggplot2::element_rect(fill = "transparent")#,
      # plot.background = ggplot2::element_rect(fill = "transparent", color = NA)
    ),
  units = "mm",
  width = 200,
  height = 200,
  pointsize = 5,
  dpi = 600
)

ggplot2::ggsave(
  filename = here::here("1 PA Decline/coef_plot_change_ARTICLEA_facet.pdf"),
  plot = p1 +
    ggplot2::scale_x_continuous(
      breaks = log(x.tics),
      labels = x.tics,
      limits = log(range(x.tics))
    ) +
    ggplot2::facet_wrap(facets = ggplot2::vars(name),ncol=2) +
    # ggplot2::theme_minimal() +
    ggplot2::theme( 
      legend.position = "none",
      # panel.grid.major = ggplot2::element_blank(),
      # panel.grid.minor = ggplot2::element_blank(),
      # axis.text.y = ggplot2::element_blank(),
      # axis.title.y = ggplot2::element_blank(),
      # axis.text.x = element_blank(),
      text = ggplot2::element_text(size = 16)#,
      # plot.title = element_text(),
      # panel.background = ggplot2::element_rect(fill = "transparent")#,
      # plot.background = ggplot2::element_rect(fill = "transparent", color = NA)
    ),
  units = "mm",
  width = 200,
  height = 200,
  pointsize = 5,
  dpi = 1200
)



# p1 <- df_long |>
#   # dplyr::mutate(value=log10(value)
#   #                              ) |>
#   ggplot2::ggplot(ggplot2::aes(x = variable, y = log(value), fill = name)) +
#   ggplot2::geom_bar(stat = "identity", position = ggplot2::position_dodge()) +
#   ggplot2::coord_trans(y = scales:::exp_trans(10)) +
#   ggplot2::scale_y_continuous(
#     breaks = log10(c(.2, .4, .5, 1, 1.2, 1.4, 1.6, 2, 2.5)),
#     labels = c(.2, .4, .5, 1, 1.2, 1.4, 1.6, 2, 2.5),
#     limits = log10(c(0.09, 2.5))
#   ) +
#   ggplot2::geom_hline(yintercept = 0) +
#   ggplot2::coord_flip() +
#   ggplot2::scale_fill_manual(values = cols) +
#   # REF: https://stackoverflow.com/a/22517219/21019325
#   ggplot2::guides(fill = ggplot2::guide_legend(reverse = TRUE)) +
#   ggplot2::ylab("OR (log))") +
#   ggplot2::labs(
#     fill = "Model" # ,
#     # title = "Prediction models: increase and decrease after stroke",
#     # subtitle = "Median coeficient after cross validation"
#   ) +
#   ggplot2::theme_classic(11) +
#   ggplot2::theme(
#     axis.title.x = ggplot2::element_text(),
#     axis.title.y = ggplot2::element_blank(),
#     axis.text.y = ggplot2::element_blank(),
#     axis.line.y = ggplot2::element_blank(),
#     axis.ticks.y = ggplot2::element_blank() # ,
#     # legend.position = "none"
#   )
# p1

# df_plot <- df_long |>
#   dplyr::mutate(
#     id = seq_len(dplyr::n()),
#     dplyr::across(where(is.numeric), \(.i) signif(.i, digits = 2))
#   ) |>
#   (function(.x) {
#     split(.x, .x[["variable"]]) |>
#       purrr::map(function(.y) {
#         .y |> dplyr::mutate(id = rev(id))
#       }) |>
#       dplyr::bind_rows()
#   })() |>
#   dplyr::mutate(id = rev(id)) |>
#   (function(.z) {
#     .z |> dplyr::mutate(var_label = variable |> (function(.x) {
#       split(.z, .x) |>
#         purrr::map(function(.y) {
#           c("", unique(as.character(.y[[1]])))
#         }) |>
#         purrr::list_c()
#     })())
#   })() |>
#   dplyr::mutate(val_label = paste("OR:", value))



# df_plot <- df_long |>
#   dplyr::mutate(
#     id = seq_len(dplyr::n()),
#     dplyr::across(where(is.numeric), \(.i) signif(.i, digits = 2))
#   ) |>
#   (function(.x) {
#     split(.x, .x[["variable"]]) |>
#       purrr::map(function(.y) {
#         .y |> dplyr::mutate(id = rev(id))
#       }) |>
#       dplyr::bind_rows()
#   })() |>
#   dplyr::mutate(id = rev(id)) |>
#   (function(.z) {
#     .z |> dplyr::mutate(var_label = variable |> (function(.x) {
#       split(.z, .x) |>
#         purrr::map(function(.y) {
#           c("", unique(as.character(.y[[1]])))
#         }) |>
#         purrr::list_c()
#     })())
#   })() |>
#   dplyr::mutate(val_label = paste("OR:", value))
# 
# table_text_size <- 5
# title_text_size <- 20
# column_space <- c(0, .6)
# 
# t1 <- df_plot |> ggplot2::ggplot(ggplot2::aes(x = var, y = variable)) +
#   ggplot2::annotate("text",
#     x = column_space[1], y = df_plot$variable,
#     label = df_plot[[5]], hjust = 0, size = table_text_size
#   ) +
#   # ggplot2::annotate("text",
#   #   x = column_space[2], y = df_plot$id,
#   #   label = df_plot[[2]], hjust = 0, size = table_text_size
#   # ) +
#   ggplot2::annotate("text",
#     x = column_space[2], y = df_plot$variable,
#     label = df_plot[[6]], hjust = 0, size = table_text_size
#   ) +
#   ggplot2::xlim(0, .8) +
#   ggplot2::theme_classic(14) +
#   ggplot2::theme(
#     axis.title.x = ggplot2::element_text(colour = "white"),
#     axis.text.x = ggplot2::element_text(colour = "white"),
#     axis.title.y = ggplot2::element_blank(),
#     axis.text.y = ggplot2::element_blank(),
#     axis.ticks.y = ggplot2::element_blank(),
#     line = ggplot2::element_blank()
#   )
# 
# patchwork::wrap_plots(t1,
#   p1,
#   ncol = 2, widths = c(1, 1.5)
# ) + patchwork::plot_annotation(title = "Prediction models: decrease and increase PA")
# 
# 
# gridExtra::grid.arrange(t1,
#   p1,
#   ncol = 2,
#   widths = c(1, 1.5),
#   top = grid::textGrob("Prediction models: decrease and increase PA",
#     x = 0.02, y = 0.2, gp = grid::gpar(fontsize = title_text_size),
#     just = "left"
#   )
# )