Merge pull request #137 from NOAA-EDAB/dev

bring internal review changes to datasets and plots into master

R/plot_abc_acl.R

@@ -117,7 +117,7 @@ plot_abc_acl <- function(shadedRegion = NULL,
       ggplot2::ggtitle("ABC or ACL for Managed Species")+
       ggplot2::theme(legend.text = ggplot2::element_text(size = 8),
                      legend.key.height = ggplot2::unit(2, "mm"))+
-      ggplot2::ylab("ABC or ACL")+
+      ggplot2::ylab("ABC or ACL, metric tons")+
       ggplot2::xlab(ggplot2::element_blank())+
       ecodata::theme_ts()+
       ggplot2::guides(fill=ggplot2::guide_legend(ncol=1))+

R/plot_aggregate_biomass.R

@@ -335,13 +335,8 @@ plot_aggregate_biomass <- function(shadedRegion = NULL,
   }
 
 
-  p <- cowplot::plot_grid(p1, p2, p3, p4, nrow=4)
-
-
-  y.grob <- grid::textGrob(expression("Biomass (kg tow"^-1*")"),
-                     gp=grid::gpar( col="black", fontsize=15), rot=90)
-
-  p <- gridExtra::grid.arrange(gridExtra::arrangeGrob(p, left = y.grob))
+  p <- cowplot::plot_grid(p1, p2, p3, p4, nrow=4,scale = 0.9) +
+    cowplot::draw_label(expression("Biomass (kg tow"^-1*")"), x=  0, y=0.5, vjust= 1.5, angle=90)
 
     #ggplot2::ylab(expression("Biomass (kg tow"^-1*")"))
   return(p)

R/plot_bennet.R

@@ -76,7 +76,7 @@ plot_bennet <- function(shadedRegion = NULL,
       ggplot2::facet_grid(EPU~Var, scales = "free")+
       ggplot2::scale_colour_grey(name ="Component") +
       ggplot2::ggtitle("Bennet Indicator")+
-      ggplot2::labs(y=paste0("Value $1,000,000 ($", bennet_year, ")")) +
+      ggplot2::labs(y=paste0("Million USD (", bennet_year, ")")) +
 
       ggplot2::xlab(ggplot2::element_blank())+
       #ggplot2::scale_x_continuous(breaks = seq(1965, 2020, by = 10), expand = c(0.01, 0.01)) +
@@ -117,7 +117,7 @@ plot_bennet <- function(shadedRegion = NULL,
       ggplot2::geom_line(data = revchange, ggplot2::aes(x = Time, y = Value, color = "$"))+
       ggplot2::scale_colour_grey(name ="Revenue Change") +
       ggplot2::ggtitle("Bennet Indicator")+
-      ggplot2::labs(y=paste0("Value $1,000,000 ($", bennet_year, ")")) +
+      ggplot2::labs(y=paste0("Million USD (", bennet_year, ")")) +
       ggplot2::scale_x_continuous(breaks = seq(1980, 2020, by = 5), expand = c(0.01, 0.01)) +
       #::scale_y_continuous(breaks = seq(y.lim[1], y.lim[2], by = 100),
       #                            limits = y.lim, expand = c(0.01, 0.01)) +
@@ -160,7 +160,7 @@ plot_bennet <- function(shadedRegion = NULL,
       ggplot2::geom_line(data = revchange, ggplot2::aes(x = Time, y = Value, color = "$"))+
       ggplot2::scale_colour_grey(name ="Revenue Change") +
       ggplot2::ggtitle("Bennet Indicator")+
-      ggplot2::labs(y=paste0("Value $1,000,000 ($", bennet_year, ")")) +
+      ggplot2::labs(y=paste0("Million USD (", bennet_year, ")")) +
       ggplot2::scale_x_continuous(breaks = seq(1980, 2015, by = 10), expand = c(0.01, 0.01)) +
       #ggplot2::scale_y_continuous(breaks = seq(y.lim[1], y.lim[2], by = 100),
       #                            limits = y.lim, expand = c(0.01, 0.01)) +

R/plot_ch_bay_sal.R

@@ -39,8 +39,8 @@ plot_ch_bay_sal <- function(shadedRegion = NULL,
     ggplot2::ggplot() +
     ggplot2::geom_ribbon(ggplot2::aes(x = Time, ymin = minLTA, ymax = maxLTA), fill = "grey", alpha = 0.5)+
 
-    ggplot2::geom_line(ggplot2::aes(x = Time, y = YearLTA, color= "Long Term Average 2010-2020")) +
-    ggplot2::geom_line(ggplot2::aes(x = Time, y = Year, color = "Daily (Current Year)")) +
+    ggplot2::geom_line(ggplot2::aes(x = Time, y = YearLTA, color= "Long Term Average 2010-2022")) +
+    ggplot2::geom_line(ggplot2::aes(x = Time, y = Year, color = "Daily 2023")) +
     ggplot2::ylab("Salinity") +
     ggplot2::ggtitle("Chesapeake Bay Salinity") +
     ggplot2::theme(legend.position = "bottom",

R/plot_ch_bay_temp.R

@@ -43,8 +43,8 @@ plot_ch_bay_temp <- function(shadedRegion = NULL,
     ggplot2::ggplot() +
     ggplot2::geom_ribbon(ggplot2::aes(x = Time, ymin = minLTAC, ymax = maxLTAC), fill = "grey", alpha = 0.5)+
 
-    ggplot2::geom_line(ggplot2::aes(x = Time, y = YearLTAC, color= "Long Term Average 2010-2020")) +
-    ggplot2::geom_line(ggplot2::aes(x = Time, y = YearC, color = "Daily 2022")) +
+    ggplot2::geom_line(ggplot2::aes(x = Time, y = YearLTAC, color= "Long Term Average 2010-2022")) +
+    ggplot2::geom_line(ggplot2::aes(x = Time, y = YearC, color = "Daily 2023")) +
     ggplot2::ylab("Temperature (C)") +
     ggplot2::ggtitle("Chesapeake Bay Temperature") +
     ggplot2::theme(legend.position = "bottom",

R/plot_chl_pp.R

@@ -157,6 +157,9 @@ plot_chl_pp <- function(shadedRegion = NULL,
                  linetype = setup$hline.lty)+
       ecodata::theme_facet() +
       ggplot2::theme(strip.text=ggplot2::element_text(hjust=0))
+
+    p <- "This data set is currently under review"
+
   }
 
   if(plottype == "weekly") {
@@ -193,12 +196,12 @@ plot_chl_pp <- function(shadedRegion = NULL,
   }
 
   # optional code for New England specific (2 panel) formatting
-  if (report == "NewEngland") {
-    p <- p +
-      ggplot2::theme(legend.position = "bottom",
-                     legend.title = ggplot2::element_blank())
-
-  }
+  # if (report == "NewEngland") {
+  #   p <- p +
+  #     ggplot2::theme(legend.position = "bottom",
+  #                    legend.title = ggplot2::element_blank())
+  #
+  # }
 
   return(p)
 

R/plot_comdat.R

@@ -124,7 +124,7 @@ plot_comdat <- function(shadedRegion = NULL,
     totdat<- landings_agg |>
       dplyr::filter(EPU %in% filterEPUs)
 
-    ylabdat <- expression("Landings (10"^3*"metric tons)")
+    ylabdat <- expression("Landings (10"^3*" metric tons)")
   }
 
   if(varName == "revenue") {
@@ -154,7 +154,8 @@ plot_comdat <- function(shadedRegion = NULL,
                                "Piscivore JOINT managed species - Revenue",
                                "Planktivore JOINT managed species - Revenue",
                                "Benthivore JOINT managed species - Revenue",
-                               "Benthos JOINT managed species - Revenue")) |>
+                               "Benthos JOINT managed species - Revenue"),
+                    Time >= 1982) |>
       #rbind(apex) |>
       dplyr::filter(stringr::str_detect(Var, paste0("JOINT|", setup$council_abbr))) |>
       dplyr::mutate(Status = c("Council Managed"))  #Create groups for
@@ -179,13 +180,15 @@ plot_comdat <- function(shadedRegion = NULL,
                                "Benthivore Revenue",
                                "Apex Predator Revenue",
                                "Benthos Revenue",
-                               "Other Revenue")) |>
+                               "Other Revenue"),
+                    Time >= 1982) |>
       dplyr::mutate(grouping = c("Total")) |>
       tidyr::separate(Var, into = c("feeding.guild"), sep = " ") |>
       dplyr::mutate(Value = Value/1000)
 
     rev_total<- ecodata::comdat |>
-      dplyr::filter(Var == "Revenue") |>
+      dplyr::filter(Var == "Revenue",
+                    Time >= 1982) |>
       dplyr::mutate(Status = c("Total")) |>
       dplyr::group_by(Status, Time, EPU) |>
       dplyr::summarise(Total = sum(Value)/1000000) |>
@@ -210,7 +213,7 @@ plot_comdat <- function(shadedRegion = NULL,
       dplyr::filter(Time >1982) |>
       dplyr::filter(EPU %in% filterEPUs)
 
-    ylabdat <- expression("Revenue (10"^6*"USD)")
+    ylabdat <- expression("Revenue (10"^6*" USD)")
   }
 
   # code for generating plot object p

R/plot_engagement.R

@@ -58,7 +58,8 @@ plot_engagement <- function(shadedRegion = NULL,
                              show.legend = FALSE, direction = "both", box.padding = 0.2, size = 3)+
     ggplot2::scale_color_manual(values = c("MedHigh to High"="#D95F02",
                                            "Medium"="#7570B3",
-                                           "All Other Communities" = "#1B9E77")) +
+                                           "All Other Communities" = "#1B9E77"),
+                                labels = c('MedHigh to High' = 'Medium-High to High','Medium' = 'Medium','All Other Communities'='All Other Communities')) +
     # ggplot2::scale_color_brewer(palette = c("#F8766D","#00BA38","#619CFF"),#"Dark2", #Change legend labels for clarity
     #                             breaks = eng$EJRating) +
 
@@ -79,10 +80,15 @@ plot_engagement <- function(shadedRegion = NULL,
   #ecodata::theme_facet()
 
 
-  p <- gridExtra::grid.arrange(eng2, bottom = grid::textGrob("Low <---------------------------------------------------------------------------------------------------------------------------> High",
-                                                  x = 0.5, y = 1, gp = grid::gpar(fontsize = 7)),
-                          left = grid::textGrob("Low <--------------------------------------------------------------------------------------> High", rot = 90,
-                                          x = 1, y = 0.5, gp = grid::gpar(fontsize = 7)))
+  #p <- gridExtra::grid.arrange(eng2, bottom = grid::textGrob("Low <---------------------------------------------------------------------------------------------------------------------------> High",
+  #                                                x = 0.5, y = 1, gp = grid::gpar(fontsize = 7)),
+  #                        left = grid::textGrob("Low <--------------------------------------------------------------------------------------> High", rot = 90,
+  #                                        x = 1, y = 0.5, gp = grid::gpar(fontsize = 7)))
+
+  p <- cowplot::plot_grid(eng2, nrow=1,scale = 0.9) +
+    cowplot::draw_label(expression("Low <------------------------------------------------------------------------------------------------------> High"), x=  0.02, y=0.5, vjust= 1.5, angle=90, size = 10) +
+    cowplot::draw_label(expression("Low <---------------------------------------------------------------------------------------------------------------------------> High"), x=  0.5, y=0.05, vjust= 1.5, angle=0, size = 10)
+
 
     return(p)
 

R/plot_grayseal.R

@@ -39,6 +39,10 @@ plot_grayseal <- function(shadedRegion = NULL,
   p <- ecodata::grayseal |>
     dplyr::filter(Var %in% c("pbr", "totalest5y", "totalest1y")) |>
     ggplot2::ggplot()+
+    #Highlight last ten years
+    ggplot2::annotate("rect", fill = setup$shade.fill, alpha = setup$shade.alpha,
+                      xmin = setup$x.shade.min , xmax = setup$x.shade.max,
+                      ymin = -Inf, ymax = Inf) +
     ggplot2::geom_line(ggplot2::aes(x = Time, y = Value, linetype = Var, color = Var))+
     ggplot2::geom_ribbon(data = ribbon, ggplot2::aes(ymin = total5yLCI, ymax =total5yUCI, x = Time), fill = "blue", alpha = 0.2)+
     ggplot2::ggtitle("Gray Seal Bycatch")+

R/plot_gsi.R

@@ -77,7 +77,7 @@ plot_gsi <- function(shadedRegion = NULL,
     # }
 
   if (report == "NewEngland") {
-    p <- NULL
+    p <- "This indicator is only present in the `MidAtlantic` report"
   }
 
   return(p)

R/plot_habitat_diversity.R

@@ -130,5 +130,5 @@ plot_habitat_diversity <- function(shadedRegion = NULL,
 
 }
 
-attr(plot_habitat_diversity,"varName") <- c("Diversity","Richness")
+attr(plot_habitat_diversity,"varName") <- c("Richness") #removed "Diversity", argument for varName per Laurel's request
 attr(plot_habitat_diversity,"report") <- c("MidAtlantic","NewEngland")

R/plot_habs.R

@@ -63,7 +63,7 @@ plot_habs <- function(shadedRegion = NULL,
      ecodata::theme_facet()
 
   if(report == "MidAtlantic") {
-    p <- NULL
+    p <- "This indicator is only present in the 'NewEngland' report"
   }
 
   return(p)

R/plot_harborporpoise.R

@@ -39,6 +39,10 @@ plot_harborporpoise <- function(shadedRegion = NULL,
   p <- ecodata::harborporpoise |>
     dplyr::filter(Var %in% c("pbr", "totalest5y", "totalest1y")) |>
     ggplot2::ggplot()+
+    #Highlight last ten years
+    ggplot2::annotate("rect", fill = setup$shade.fill, alpha = setup$shade.alpha,
+                      xmin = setup$x.shade.min , xmax = setup$x.shade.max,
+                      ymin = -Inf, ymax = Inf) +
     ggplot2::geom_line(ggplot2::aes(x = Time, y = Value, linetype = Var, color = Var))+
     ggplot2::geom_ribbon(data = ribbon, ggplot2::aes(ymin = total5yLCI, ymax =total5yUCI, x = Time), fill = "blue", alpha = 0.2)+
     ggplot2::ggtitle("Harbor Porpoise Bycatch")+
@@ -50,7 +54,7 @@ plot_harborporpoise <- function(shadedRegion = NULL,
                                 labels = c("PBR", "Annual Estimates", "5yr rolling mean and CI"))+
     ggplot2::theme(#legend.position = "none",
       legend.title = ggplot2::element_blank(),
-      legend.position = c(0.75, 0.8),
+      legend.position = c(0.5, 0.8),
       legend.text = ggplot2::element_text(size = 8),
       legend.background = ggplot2::element_rect(
         colour = "transparent", fill = "transparent"))+

R/plot_mab_inshore_survey.R

@@ -28,19 +28,37 @@ plot_mab_inshore_survey <- function(shadedRegion = NULL,
 
   # optional code to wrangle ecodata object prior to plotting
   # e.g., calculate mean, max or other needed values to join below
-   fix <- ecodata::mab_inshore_survey |>
-     dplyr::filter(EPU %in% filterEPUs,
-                   grepl("Value",Var)) |>
-     tidyr::separate(Var,into = c("Var","Trash"),sep ="-") |>
-     dplyr::select(-Trash) |>
-     dplyr::mutate(Var = as.factor(Var)) |>
-     dplyr::group_by(Var) |>
-     dplyr::mutate(hline = mean(Value))
+  fix <- ecodata::mab_inshore_survey |>
+    tidyr::separate(Var, into = c("Var",  "Val"), sep = "-") |>
+    tidyr::pivot_wider(names_from = Val, values_from = Value) |>
+    dplyr::mutate(Value = as.numeric(Value),
+                  CV = as.numeric(CV)) |>
+    dplyr::group_by(Var) |>
+    dplyr::mutate(hline = mean(Value),
+                  SD = Value * CV, #calculate SD from CV
+                  upper = Value + (2*SD),
+                  lower = Value - (2*SD))
 
-   fix$Var <- factor(fix$Var,levels =  c("Piscivore Spring","Piscivore Fall",
-                                         "Benthivore Spring","Benthivore Fall",
-                                         "Planktivore Spring","Planktivore Fall",
-                                         "Benthos Spring","Benthos Fall"))
+  fix$Var <- factor(fix$Var,levels = c("Piscivore Spring","Piscivore Fall",
+                                       "Benthivore Spring", "Benthivore Fall",
+                                       "Planktivore Spring", "Planktivore Fall",
+                                       "Benthos Spring", "Benthos Fall"))
+
+
+    # code before adding standard deviation calculation
+   #fix <- ecodata::mab_inshore_survey |>
+    # dplyr::filter(EPU %in% filterEPUs,
+     #              grepl("Value",Var)) |>
+     #tidyr::separate(Var,into = c("Var","Trash"),sep ="-") |>
+     #dplyr::select(-Trash) |>
+     #dplyr::mutate(Var = as.factor(Var)) |>
+     #dplyr::group_by(Var) |>
+     #dplyr::mutate(hline = mean(Value))
+
+   #fix$Var <- factor(fix$Var,levels =  c("Piscivore Spring","Piscivore Fall",
+                                        #"Benthivore Spring","Benthivore Fall",
+                                         #"Planktivore Spring","Planktivore Fall",
+                                         #"Benthos Spring","Benthos Fall"))
 
 
    ymax <- fix |>
@@ -75,6 +93,10 @@ plot_mab_inshore_survey <- function(shadedRegion = NULL,
                         linewidth = setup$hline.size,
                         alpha = setup$hline.alpha,
                         linetype = setup$hline.lty)+
+    ggplot2::geom_ribbon(data = fix,
+                         ggplot2::aes(x = Time, ymin = pmax(lower,0), ymax = upper),
+                         alpha = 0.5,
+                         fill = "gray")+
     ggplot2::ggtitle("NEAMAP inshore BTS")+
     ggplot2::ylab(expression("Biomass (kg tow"^-1*")"))+
     ggplot2::xlab(ggplot2::element_blank())+