Created new plot for Anchor River Optimum Yeild profile, that removes…

… other panels and only shows 90% MSY line, and updates the escapement goal shading to 3200-6400. OYP_plotUpdate.R creates the new figure.

Updated plot_profile() function within the plot.R script, to include an option for selection 70,80,90 % MSY lines to include in figure. The old function is just commented out.

Script/OYP_plotUpdate.R

@@ -0,0 +1,38 @@
+# Update to Optimal Yeild Profile plot as requested 9/21/2023 - 
+# Removed other panels, updated Esc goal range to 3200-6400 , remove 70 & 80% lines
+
+
+#### Import data and Function ####
+packs <- c("jagsUI", "tidyverse")
+lapply(packs, require, character.only = TRUE)
+
+#source functions
+function_files <- list.files(path=".\\functions")
+lapply(function_files, function(x) source(paste0(".\\functions\\", x)))
+
+#load datasets
+data_names <- list.files(path=".\\data")
+lapply(data_names, function(x) load(paste0(".\\data\\", x), .GlobalEnv))
+
+post <- readRDS(".\\output\\post_1977on") #OUTPUT is currently located in S:\RTS\Reimer\Anchor_River_Chinook
+
+####
+
+
+
+#### Create Plot ####
+
+profile_full <- get_profile(post)
+OYP_update = plot_profile(profile_full,rug=FALSE ,goal_range = c(3200, 6400),profiles = c("OYP"),percent = c("90"))+
+  scale_x_continuous("Spawners", breaks = seq(0, 9000,800), labels = scales::comma)+theme(legend.position = "none")+
+  labs(caption = "
+  Probability of achieving sustained yield within 90 percent of maximum sustained yield for Anchor River Chinook salmon.
+  The shaded area represents to escapement goal recommendation of 3,200-6,400 Chinook salmon.
+       ")+
+  theme(plot.caption = element_text(hjust=0))
+OYP_update
+
+####
+
+#ggsave("OYP_update.png",height = 3,width = 9,units = "in")
+

functions/plot.R

@@ -428,8 +428,56 @@ plot_horse_full <- function(post_dat, firstyr = 1977){
 #' lapply(profiles, plot_profile)
 #'
 #' @export
-plot_profile <- function(profile_dat, limit = NULL, rug = TRUE, goal_range = NA, profiles = c("OYP", "ORP", "OFP")){
-  temp <-unlist(lapply(profiles, function(x){paste0(x, c("70", "80", "90"))}))
+# plot_profile <- function(profile_dat, limit = NULL, rug = TRUE, goal_range = NA, profiles = c("OYP", "ORP", "OFP")){
+#   temp <-unlist(lapply(profiles, function(x){paste0(x, c("70", "80", "90"))}))
+#   profile_label <- ggplot2::as_labeller(c('OYP' = "Optimum Yield Profile",
+#                                           'OFP' = "Overfishing Profile",
+#                                           'ORP' = "Optimum Recruitment Profile"))
+#   S.msy50 <- median(profile_dat$s[which.max(profile_dat$OYP90)]) #approximate
+#   rug_dat <- get_BEGbounds(S.msy50)
+#   
+#   if(is.null(limit)){
+#     xmax <- S.msy50 * 2.25
+#   }
+#   else xmax <- limit
+#   
+#   plot <- profile_dat %>%
+#     dplyr::select_("s", .dots = temp) %>%
+#     dplyr::group_by(s) %>%
+#     dplyr::filter(s <= xmax) %>%
+#     dplyr::summarise(across(starts_with("O"), function(x) mean(x, na.rm = TRUE))) %>%
+#     tidyr::gather("key", "prob", -s, factor_key = TRUE) %>%
+#     dplyr::mutate(profile = factor(stringr::str_extract(key, "[A-Z]+"),
+#                                    levels = c("OYP", "OFP", "ORP")),
+#                   max_pct = stringr::str_extract(key, "[0-9]+")) %>%
+#     ggplot2::ggplot(ggplot2::aes(x = s, y = prob, linetype = max_pct)) +
+#     ggplot2::geom_line() +
+#     ggplot2::scale_x_continuous("Spawners", limits = c(0, xmax), labels = scales::comma) +
+#     ggplot2::scale_y_continuous("Probability", breaks = seq(0, 1, 0.2), limits = c(0, 1)) +
+#     ggplot2::scale_linetype_discrete(name = "Percent of Max.") +
+#     ggplot2::facet_grid(profile ~ ., labeller = profile_label) +
+#     ggplot2::theme_bw()
+#   
+#   if(rug == TRUE) {
+#     plot2 <- plot +     
+#       ggplot2::geom_rug(ggplot2::aes(x = lb_Kenai), data = rug_dat, inherit.aes = FALSE, sides = "b", color = "darkgrey") +
+#       ggplot2::geom_rug(ggplot2::aes(x = ub_Kenai), data = rug_dat, inherit.aes = FALSE, sides = "b", color = "black")
+#   }
+#   else plot2 <- plot
+#   
+#   if(!anyNA(goal_range)) {
+#     plot2 + ggplot2::geom_rect(ggplot2::aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax),
+#                                data.frame(xmin = goal_range[1], xmax = goal_range[2], ymin = -Inf, ymax = Inf),
+#                                inherit.aes = FALSE, fill = "red", alpha = 0.2)
+#   }
+#   else plot2
+# }
+
+# # Version 2
+## Adds the ability to select which %MSY curves to show. 
+plot_profile <- function(profile_dat, limit = NULL, rug = TRUE, goal_range = NA, profiles = c("OYP", "ORP", "OFP"),
+                         percent = c("70","80","90")){
+  temp <-unlist(lapply(profiles, function(x){paste0(x, percent)}))
   profile_label <- ggplot2::as_labeller(c('OYP' = "Optimum Yield Profile",
                                           'OFP' = "Overfishing Profile",
                                           'ORP' = "Optimum Recruitment Profile"))
@@ -474,6 +522,7 @@ plot_profile <- function(profile_dat, limit = NULL, rug = TRUE, goal_range = NA,
 }
 
 
+
 # State variable plot -----------------------------------------------------
 
 #' State Variable Plot