Plot updates

R/plot_aggregate_biomass.R

@@ -349,9 +349,16 @@ plot_aggregate_biomass <- function(shadedRegion = NULL,
                           color = "#ca0020",na.rm = T)
   }
 
-
-  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)
+  p <- ggpubr::ggarrange(
+    p1,
+    p2,
+    p3,
+    p4,
+    nrow = 4,
+    scale = 0.9)
+
+ # 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_annual_chl_pp.R

@@ -83,6 +83,17 @@ plot_annual_chl_pp <- function(shadedRegion = NULL,
       ecodata::theme_facet() +
       ecodata::theme_title()
 
+    if (report == "MidAtlantic") {
+      p <- p +
+        ggplot2::coord_cartesian(ylim = c(2e+07, 4e+07), xlim = c(1998, 2023)) +
+        ggplot2::ggtitle("MAB Primary Production") +
+        ggplot2::ylab("Carbon (mt)")
+    }
+    else {
+      p <- p +
+        ggplot2::theme(axis.title.x = ggplot2::element_blank())
+    }
+
   return(p)
 
 }

R/plot_bennet.R

@@ -88,6 +88,17 @@ plot_bennet <- function(shadedRegion = NULL,
       ecodata::theme_title()+
       ecodata::theme_facet()
 
+    if (report == "MidAtlantic") {
+  p <- p +
+        ggplot2::theme(legend.position = "bottom") +
+        ggplot2::facet_wrap(~Var,
+                            nrow = 2)
+    } else {
+        p <- p +
+           ggplot2::theme(legend.position = "bottom",
+            legend.title = ggplot2::element_blank(),
+             strip.text.y = ggplot2::element_blank())
+    }
 
   } else if (varName == "total") {
 
@@ -123,9 +134,35 @@ plot_bennet <- function(shadedRegion = NULL,
       #                            limits = y.lim, expand = c(0.01, 0.01)) +
       ecodata::theme_ts() +
       ggplot2::xlab(ggplot2::element_blank())+
-      ggplot2::theme(title = ggplot2::element_text(size = 10))+
       ecodata::theme_title()
 
+    if (report == "MidAtlantic") {
+      p <- p +
+        ggplot2::theme(text = ggplot2::element_text(size = 14)) +
+        ggplot2::theme(
+          legend.position = "bottom")
+    }
+
+    if (EPU == "GB") {
+      p <- p +
+        ggplot2::ggtitle("GB revenue components") +
+        ggplot2::theme(
+          legend.position = "none",
+          legend.title = ggplot2::element_blank()) +
+        ggplot2::ylab("Million USD (2023)") +
+        ggplot2::theme(text = ggplot2::element_text(size = 12))
+    }
+
+    if (EPU == "GOM") {
+      p <- p +
+        ggplot2::ggtitle("GOM revenue components") +
+        ggplot2::theme(
+          legend.position = "bottom",
+          legend.title = ggplot2::element_blank()) +
+        ggplot2::ylab("Million USD (2023)") +
+        ggplot2::theme(text = ggplot2::element_text(size = 12))
+    }
+
 
   } else if (varName == "total_guild") {
 

R/plot_benthos_index.R

@@ -69,8 +69,8 @@ plot_benthos_index <- function(shadedRegion = NULL,
                         xmin = setup$x.shade.min , xmax = setup$x.shade.max,
                         ymin = -Inf, ymax = Inf) +
       ggplot2::geom_ribbon(ggplot2::aes(ymin = Lower, ymax = Upper, fill = Season), alpha = 0.5)+
-      ggplot2::geom_point()+
-      ggplot2::geom_line()+
+      ggplot2::geom_point(ggplot2::aes(color = .data$Season)) +
+      ggplot2::geom_line(ggplot2::aes(color = .data$Season)) +
       ggplot2::ggtitle("")+
       ggplot2::ylab(paste("Relative",varName,"Biomass"))+
       ggplot2::xlab(ggplot2::element_blank())+
@@ -87,6 +87,11 @@ plot_benthos_index <- function(shadedRegion = NULL,
                        legend.title = ggplot2::element_blank())
 
     }
+
+    if (varName == "Megabenthos") {
+      p <- p +
+      ggplot2::theme(legend.position = "none")
+    }
   }
 
   if (plottype == "cog"){
@@ -111,18 +116,19 @@ plot_benthos_index <- function(shadedRegion = NULL,
                         xmin = setup$x.shade.min , xmax = setup$x.shade.max,
                         ymin = -Inf, ymax = Inf) +
       ggplot2::geom_ribbon(ggplot2::aes(ymin = Lower, ymax = Upper, fill = Season), alpha = 0.3)+ #
-      ggplot2::geom_point()+
-      ggplot2::geom_line()+
-      ggplot2::ggtitle("")+
-      ggplot2::ylab(paste(varName," Center of Gravity, km"))+
       ggplot2::xlab(ggplot2::element_blank())+
-      ggplot2::facet_wrap(~Direction, scales = "free_y")+ #Season
       ecodata::geom_gls()+
       ecodata::geom_lm(n=n)+
       ecodata::theme_ts()+
       ecodata::theme_facet()+
-      ecodata::theme_title()
-
+      ecodata::theme_title() +
+      ggplot2::coord_cartesian(xlim = c(1980, 2023)) +
+      ggplot2::ggtitle("Northeast U.S. Macrobenthos Distribution") +
+      ggplot2::ylab("Center of Gravity, km") +
+      ggplot2::geom_point(ggplot2::aes(color = .data$Season)) +
+      ggplot2::geom_line(ggplot2::aes(color = .data$Season)) +
+      ggplot2::theme(legend.position = 'bottom') +
+      ggplot2::facet_grid(cols = ggplot2::vars(Season), rows = ggplot2::vars(Direction), scales = "free_y")
   }
 
   return(p)

R/plot_cetacean_dist.R

@@ -66,7 +66,6 @@ plot_cetacean_dist <- function(shadedRegion = NULL,
       #ggplot2::scale_color_manual(values = c("blue", "black", "red"))+
       ggplot2::coord_sf(xlim = setup$xlims, ylim = setup$ylims) +
       ecodata::theme_map() +
-      ggplot2::ggtitle("Cetacean Species Distribution") +
     ggplot2::xlab("") +
     ggplot2::ylab("") +
     ggplot2::scale_x_continuous(breaks=c(-76,-72, -68) )+
@@ -77,8 +76,9 @@ plot_cetacean_dist <- function(shadedRegion = NULL,
                    strip.text = ggplot2::element_text(hjust=0),
                    axis.text = ggplot2::element_text(size = 8),
                    legend.title = ggplot2::element_blank())+#,
-    #ggplot2::legend.position = c(0.6, 0.15)) +
-    ggplot2::facet_wrap(~season)
+    ggplot2::ggtitle("Whale and Dolphin Distribution Shifts") +
+    ggplot2::facet_wrap(~season, nrow = 1) +
+    ggplot2::theme(legend.position = "bottom")
   # hms_map
 
    # # optional code for New England specific (2 panel) formatting

R/plot_chl_pp.R

@@ -125,7 +125,7 @@ plot_chl_pp <- function(shadedRegion = NULL,
         ggplot2::geom_point() +
         ggplot2::geom_line() +
         ecodata::geom_lm(n = n) +
-        ggplot2::scale_x_discrete(name = "", breaks = seq(min(out$Year),max(out$Year),10)) +
+        ggplot2::scale_x_discrete(name = "", breaks = scales::pretty_breaks(n = 1)) +
         ggplot2::facet_wrap(EPU~Month~., ncol = 12) +
         ggplot2::ggtitle(paste0("Monthly median ",varabbr)) +
         ggplot2::ylab(varunits) +
@@ -135,9 +135,13 @@ plot_chl_pp <- function(shadedRegion = NULL,
                             alpha = setup$hline.alpha,
                             linetype = setup$hline.lty)+
         ecodata::theme_facet() +
-        ggplot2::theme(axis.text.x = ggplot2::element_text(angle=45, hjust = 1),
+        ggplot2::theme(text = ggplot2::element_text(size = 16),
+                       axis.text.x = ggplot2::element_text(size = 8, angle=45, hjust = 1),
                        panel.spacing = ggplot2::unit(1, "lines"),
+                       panel.border = ggplot2::element_rect(color = "gray80"),
                        plot.margin = ggplot2::unit(c(0.1, 0, 0, 0), "cm"))+
+        ggplot2::facet_grid(rows = ggplot2::vars(EPU), cols = ggplot2::vars(Month)) +
+        ggplot2::geom_point(color = "white") + ggplot2::geom_line() +
         ecodata::theme_title()
 
     }

R/plot_comdat.R

@@ -216,6 +216,7 @@ plot_comdat <- function(shadedRegion = NULL,
       dplyr::filter(EPU %in% filterEPUs)
 
     ylabdat <- expression("Revenue (10"^6*" USD)")
+
   }
 
   # code for generating plot object p
@@ -246,7 +247,6 @@ plot_comdat <- function(shadedRegion = NULL,
       ggplot2::scale_color_manual(values = series.col2, aesthetics = "color")+
       ggplot2::facet_wrap(~EPU, scales = "free")+
       #ggplot2::guides(color = "none") +
-      ggplot2::ylab(ylabdat) +
       ggplot2::xlab(ggplot2::element_blank())+
       ggplot2::theme(legend.position = "bottom",
                      legend.title = ggplot2::element_blank())+
@@ -258,9 +258,22 @@ plot_comdat <- function(shadedRegion = NULL,
       ecodata::theme_ts() +
       ggplot2::ggtitle(setup$region)+
       ecodata::theme_title() +
-      ecodata::theme_facet()
+      ecodata::theme_facet() +
+      ggplot2::ylab(ylabdat)
+
+      if (report == "MidAtlantic") {
+        p <- p +
+          ggplot2::theme(strip.text.x = ggplot2::element_blank(),
+                         legend.position = "bottom",
+                         legend.title = ggplot2::element_blank())
+      } else {
+        p <- p +
+          ggplot2::facet_wrap(~EPU,
+                              nrow = 2)
+      }
   }
 
+
   if(plottype == "guild") {
 
     facet_names <- list("Apex" = expression("Apex"),
@@ -314,7 +327,9 @@ plot_comdat <- function(shadedRegion = NULL,
                      legend.position = "bottom",
                      legend.title = ggplot2::element_blank())+
       ggplot2::ggtitle(setup$region)+
-      ecodata::theme_title()
+      ecodata::theme_title() +
+      ggplot2::geom_point(size = 0.05) +
+      ggplot2::geom_line(size = 0.05)
 
   }
 

R/plot_commercial_div.R

@@ -89,13 +89,15 @@ plot_commercial_div <- function(shadedRegion = NULL,
                         size = setup$hline.size,
                         alpha = setup$hline.alpha,
                         linetype = setup$hline.lty) +
+    ggplot2::theme(plot.margin = ggplot2::unit(c(0.25, 0.5, 0.25, 0.5), "cm")) +
     ecodata::theme_ts()+
     ecodata::theme_title()
    # optional code for New England specific (2 panel) formatting
     if (report == "NewEngland") {
       p <- p +
         ggplot2::theme(legend.position = "bottom",
-                       legend.title = ggplot2::element_blank())
+                       legend.title = ggplot2::element_blank()) +
+        ggplot2::ylab('Effective Shannon Index')
 
     }
 

R/plot_community_climate_vulnerability.R

@@ -7,7 +7,7 @@
 #' @param varName Character string. Which variable: ocean acidification sensitivity,
 #' temperature sensitivity, stock biomass sensitivity, total sensitivity, total vulnerability
 #' ("oaSum","tempSum","stckSum","sensSum","vulnSum")
-#' @param plottype Character string. Which plot: proportion of communities (default) 
+#' @param plottype Character string. Which plot: proportion of communities (default)
 #' or regional sensitivity by revenue or landings ("propcomm", "regionrev", "regionland")
 #' @param n Numeric scalar. Number of years used (from most recent year) to estimate short term trend . Default = 0 (No trend calculated)
 #'
@@ -51,9 +51,9 @@ plot_community_climate_vulnerability <- function(shadedRegion = NULL,
                  "Total Climate Sensitivity", "Total Climate Vulnerability")
    names(varTitle) <- c("oaSum","tempSum","stckSum",
                         "sensSum","vulnSum")
-   
+
    if(plottype=="propcomm"){ #proportion of communities
-     
+
      props <- ecodata::community_climate_vulnerability |>
        dplyr::filter(!stringr::str_detect(Var, "Regional-")) |>
        tidyr::separate(Var, into = c("Town", "StateVar"), sep = ",") |> #using two steps because some towns have - in the name
@@ -72,18 +72,18 @@ plot_community_climate_vulnerability <- function(shadedRegion = NULL,
        dplyr::group_by(Time, EPU, risk) |>
        dplyr::mutate(catn = dplyr::n(),
                      propcat = catn/commn)
-     
+
      propsonly <- props |>
        dplyr::select(Time, EPU, risk, propcat) |>
        dplyr::distinct()
-     
+
      # code for generating plot object p
      # ensure that setup list objects are called as setup$...
      # e.g. fill = setup$shade.fill, alpha = setup$shade.alpha,
      # xmin = setup$x.shade.min , xmax = setup$x.shade.max
      #
-     
-     p <- ggplot2::ggplot() + 
+
+     p <- ggplot2::ggplot() +
        ggplot2::annotate("rect", fill = setup$shade.fill, alpha = setup$shade.alpha,
                          xmin = setup$x.shade.min , xmax = setup$x.shade.max,
                          ymin = -Inf, ymax = Inf) +
@@ -104,24 +104,25 @@ plot_community_climate_vulnerability <- function(shadedRegion = NULL,
        #ggplot2::ggtitle("Mid-Atlantic") +
        #ggplot2::theme(legend.position = "blank")
        ggplot2::ylab("Proportion communities") +
+       ggplot2::theme(legend.position = 'bottom')+
        ecodata::geom_lm(n=n) +
        ecodata::theme_ts()+
        ecodata::theme_facet()+
        ecodata::theme_title()
-     
-     
+
+
      return(p)
-     
+
    }else{ #regional plots
-     
+
      if(plottype == "regionrev"){
        units <- "value"
        type <- "Revenue"
      }else{
        units <- "pounds"
        type <- "Landings"
      }
-     
+
      regional <- ecodata::community_climate_vulnerability |>
        dplyr::filter(stringr::str_detect(Var, "Regional-")) |>
        dplyr::mutate(Varin = stringr::str_extract(Var, '\\b\\w+$'), #keep everything before -
@@ -130,7 +131,7 @@ plot_community_climate_vulnerability <- function(shadedRegion = NULL,
        dplyr::filter(Var %in% varName,
                      EPU %in% filterEPUs,
                      Units %in% units)
-     
+
      p <- ggplot2::ggplot() +
        #Highlight last ten years
        ggplot2::annotate("rect", fill = setup$shade.fill, alpha = setup$shade.alpha,
@@ -141,7 +142,7 @@ plot_community_climate_vulnerability <- function(shadedRegion = NULL,
                           alpha = .3) +
        ggplot2::scale_fill_manual(values=risk,
                                   breaks=c('Low', 'Moderate', 'High', 'Very High'),
-                                  name=varTitle[varName]) + 
+                                  name=varTitle[varName]) +
        # ecodata::geom_gls(data = regional, ggplot2::aes(x = Time, y = Value,
        #                              group = Var),
        #                 alpha = setup$trend.alpha, size = setup$trend.size) +
@@ -155,19 +156,21 @@ plot_community_climate_vulnerability <- function(shadedRegion = NULL,
        ggplot2::theme_bw() +
        #ggplot2::ggtitle("Mid-Atlantic") +
        #ggplot2::theme(legend.position = "blank")
-       ggplot2::ylab(paste("Regional", type, varTitle[varName])) +
+       ggplot2::ylab(paste("Total Climate Vulnerability \n Regional", type)) +
        ecodata::theme_ts()+
        ecodata::theme_facet()+
-       ecodata::theme_title()
-
-
+       ecodata::theme_title()+
+  #     ggplot2::ylab("Total Climate Vulnerability \n (Regional Landings)") +
+       ggplot2::theme(legend.position = 'bottom')
+
+
      return(p)
-     
-                     
-       
+
+
+
    }
-   
-   
+
+
 }
 
 attr(plot_community_climate_vulnerability,"report") <- c("MidAtlantic","NewEngland")