diff --git a/global.R b/global.R
index 73ea80a..3c0a545 100644
--- a/global.R
+++ b/global.R
@@ -7,65 +7,37 @@
 # load packages
 {
   library(tidyverse)
-  #library(plyr)
   library(data.table)
   library(janitor)
-  #library(doBy)
   
   library(spatial)
-  library(sp)
   library(sf)
   library(spdep)
   library(sfdep)
   library(fields)
   library(terra)
   
-  #library(rasterVis)
-  #library(raster)
-  
   library(RColorBrewer)
   library(viridis)
   
   library(leaflet)
-  #library(kableExtra)
   library(shiny)
-  #library(plotly)
   library(htmlwidgets)
   library(shinythemes)
   library(shinycustomloader)
   library(leaflet.extras2)
-  #library(leafem)
-  
-  #library(rjson)
-  #library(KernSmooth)
-  #library(ks)
 }
 
-#cleanMem <- function(n=10) { for (i in 1:n) gc() }
-
 # Most recent export
-Data.In <- data.table::fread("data/allsharks.csv")
-
-#My.Vessel <- "No Bull"
+Data.In <- fread("data/allsharks.csv")
 
 #Subset for time
 My.Date.Start <- as.Date("2015-06-01", format = "%Y-%m-%d") 
 My.Date.End <- as.Date("2024-12-31", format = "%Y-%m-%d")
 
-#My.Months <- c(1,2,3,4,5,6,7,8,9,10,11,12)  
-
 #Subset for fishery
 My.Trip.Type <- c("Longline")  
 
-# # Species Lookup Table
-# Species.Lookup <- read.csv("data/EM Species Codes Lookup.csv")
-# # Modify species lookup
-# Species.Lookup = plyr::rename(Species.Lookup, c("Number"="Species.Number"))
-# colnames(Species.Lookup)=gsub(".","_",colnames(Species.Lookup), fixed=T)
-# Species.Lookup$Species_Lookup = gsub("/"," or ",Species.Lookup$Species_Lookup)
-
-#My.species = Species.Lookup[Species.Lookup$Shark == "Y",]$Species.Number
-
 # Combine filter conditions
 All.Data.Final <- setDT(Data.In[Data.In$Retrieval_Begin_Date > My.Date.Start &
                             Data.In$Retrieval_Begin_Date < My.Date.End &
@@ -90,12 +62,6 @@ top_species <- setDT(All.Data.Final)[, .N, by = .(Retrieval_Year, Common_Name)][
 top_species_all_years_allsharks <- names(sort(tapply(top_species$N, top_species$Common_Name, sum), decreasing = TRUE))[1:10]
 top.sub <- All.Data.Final[Common_Name %in% top_species_all_years_allsharks]
 
-# Create a copy of top.sub for further processing
-#top.sub2 <- copy(top.sub)
-
-# species caught table
-#Species.Count <- summaryBy(Species_Number ~ Common_Name, data=unique(All.Data.Final[,c("Species_Number","Common_Name")]), FUN=c(length)) %>%
-  #dplyr::summarise(Number_of_Unique_Species_Groups = sum(Species_Number.length))
 
 All.Species <- All.Data.Final %>%
   dplyr::group_by(Common_Name) %>%
@@ -123,11 +89,6 @@ gridshp <- st_read(dsn="shapefiles", layer = "GOM_GRID_10MIN_fullgulf")
 gridshp=gridshp[1]
 names(gridshp)[names(gridshp) == "Id"] <- "GRID_ID"
 
-#gearrest <- st_read(dsn="shapefiles", layer="shapefile_reef_fish_longline_buoy_gear_po_GOMx_SERO")
-#gearrest <- st_transform(gearrest, crs=4326)
-#seasonalclose <- st_read(dsn="shapefiles", layer="Gulf_ReefLL_seasonal_po")
-#seasonalclose <- st_transform(seasonalclose, crs=4326)
-
 # home port
 lats <- c(27.332160)
 lons <- c(-82.577740)
@@ -138,7 +99,6 @@ port_icon <- makeIcon("www/MoteLogomark.svg",
                       iconWidth=30, iconHeight=30,
                       iconAnchorX=15, iconAnchorY=15)
 
-# Vessel activity over time
 # Calculate the number of unique retrievals per unique trip
 unique_retrievals <- All.Data.Final %>%
   dplyr::group_by(Retrieval_Year) %>%
@@ -156,7 +116,6 @@ summary_data <- All.Data.Final %>%
 # Merge the summary data with the number of unique retrievals
 summary_data <- left_join(summary_data, unique_retrievals, by = "Retrieval_Year")
   
-
 summarylinechart <- ggplot(summary_data) +
   geom_point(aes(x = Retrieval_Year, y = Sea_Days_sum, color = "Sea Days"), size = 2) +
   geom_point(aes(x = Retrieval_Year, y = Number_Trips, color = "Trips"), size = 2) +