added NE groundfish rmd

vignettes/groundfishNEFleet.Rmd

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+---
+title: "NE Groundfish fleet"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{NE Groundfish fleet}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+
+
+```{r setup, warning=FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>"
+)
+
+library(atlantiscas)
+library(ncdf4)
+library(sf)
+```
+
+
+## Species
+
+The [NE multispecies (Groundfish) FMP](https://www.nefmc.org/management-plans/northeast-multispecies) comprises the following species:
+
+* Atlantic Cod (COD)
+* Haddock (HAD)
+* Yellowtail Flounder (YTF)
+* Pollock (POL)
+* American Plaice (PLA)
+* Witch Flounder (WTF)
+* White Hake (WHK)
+* Winter Flounder (WIF)
+* Redfish (RED)
+* Atlantic Halibut (HAL)
+
+Non target species
+
+* Windowpane flounder (WPF)
+* Ocean Pout (OPT)
+* Atlantic Wolffish (WOL)
+
+These GEARCAT(s) associated with groundfish species are:
+
+* Bottom Trawl
+* Sink Gillnet
+
+** Still need to separate some species (skates, sharks) **
+
+
+```{r groundfishdata, echo = F}
+gf <- readRDS(here::here("data/NEGroundfishDataCAMS.rds")) |> 
+  dplyr::mutate(Year = as.integer(Year))
+
+gfCodes <- c("COD","HAD","YTF","POL","PLA","WTF","WHK","WIF","RED","HAL","WPF","OPT","WOL")
+
+gf |> dplyr::distinct(GEARCAT,GEARCODE) |> 
+  dplyr::arrange(GEARCAT) |> 
+  DT::datatable()
+```
+
+## Landings
+
+The landings of trips with associated bycatch
+
+```{r plotland1, echo = F}
+landings <- gf |> 
+  dplyr::group_by(Year,Functional_Group) |> 
+  dplyr::summarise(lbs = sum(InsideLANDED),
+                   .groups = "drop")
+                     
+speciesLanded <- landings |> 
+  dplyr::group_by(Functional_Group) |> 
+  dplyr::summarise(nyrs = dplyr::n(),
+                   .groups = "drop") |> 
+  dplyr::filter(nyrs > 1) |> 
+  dplyr::pull(Functional_Group)
+
+landings |> 
+  dplyr::filter(Functional_Group %in% speciesLanded) |> 
+  ggplot2::ggplot() + 
+  ggplot2::geom_line(ggplot2::aes(x=Year,y=lbs)) +
+  ggplot2::facet_wrap(~Functional_Group)
+
+```
+
+## Total groundfish landings for species in FMP
+
+```{r plotcompare, echo = F}
+landings <- gf |> 
+  dplyr::filter(Code %in% gfCodes) |>  
+  dplyr::group_by(Year,Code) |> 
+  dplyr::summarise(lbs = sum(InsideLANDED),
+                   .groups = "drop")
+
+landings |> 
+  ggplot2::ggplot() + 
+  ggplot2::geom_line(ggplot2::aes(x=Year,y=lbs)) +
+  ggplot2::facet_wrap(~Code)
+```
+
+## Total groundfish landings for bycatch species
+
+```{r plotcompare2, echo = F}
+landings <- gf |> 
+  dplyr::filter(!(Code %in% gfCodes)) |>  
+  dplyr::group_by(Year,Code) |> 
+  dplyr::summarise(lbs = sum(InsideLANDED),
+                   .groups = "drop")
+
+landings |> 
+  ggplot2::ggplot() + 
+  ggplot2::geom_line(ggplot2::aes(x=Year,y=lbs)) +
+  ggplot2::facet_wrap(~Code)
+```
+
+## Landings of groundfish by species
+
+```{r plotland2, echo =F}
+
+gf |> 
+  dplyr::group_by(Functional_Group,Code) |> 
+  dplyr::summarise(millionlbs = sum(InsideLANDED)/1000000,
+                   .groups = "drop") |> 
+  dplyr::arrange(desc(millionlbs)) |> 
+  DT::datatable(options = list(pageLength = 10,
+                               order = list(list(0, 'asc'))
+          ))
+```
+
+
+## Landings of Groundfish by state
+
+```{r plotland3, echo = F}
+landings <- gf |> 
+  dplyr::filter(Code %in% gfCodes) |>  
+  dplyr::group_by(Year,STATEABB) |> 
+  dplyr::summarise(lbs = sum(InsideLANDED),
+                   .groups = "drop")
+                     
+landings |> 
+  ggplot2::ggplot() + 
+  ggplot2::geom_line(ggplot2::aes(x=Year,y=lbs)) +
+  ggplot2::facet_wrap(~STATEABB)
+
+```
+
+## Landings of groundfish by port (species in FMP)
+
+```{r plotland4, echo=F}
+landings <- gf |> 
+  dplyr::filter(Code %in% gfCodes) |>  
+  dplyr::group_by(Year,STATEABB,newport) |> 
+  dplyr::summarise(lbs = sum(InsideLANDED),
+                   .groups = "drop")
+
+ports10 <- landings |> 
+  dplyr::group_by(newport,STATEABB) |> 
+  dplyr::summarise(lbs = sum(lbs),
+                   .groups = "drop") |> 
+  dplyr::arrange(desc(lbs)) |> 
+  dplyr::slice(1:10) |> 
+  dplyr::pull(newport)
+
+
+landings |> 
+  dplyr::filter(newport %in% ports10) |> 
+  dplyr::group_by(Year,newport) |> 
+  dplyr::summarise(lbs = sum(lbs),
+                   .groups="drop") |> 
+  ggplot2::ggplot() + 
+  ggplot2::geom_line(ggplot2::aes(x=Year,y=lbs)) +
+  ggplot2::facet_wrap(~newport)
+
+```
+
+
+## Landings by Atlantis Box
+compared to model output from v2.2.0
+
+```{r plotland5, echo=F}
+landings <- gf |> 
+  dplyr::filter(Code %in% gfCodes) |>  
+  dplyr::group_by(Year,Area) |> 
+  dplyr::summarise(lbs = sum(InsideLANDED),
+                   .groups = "drop") |>
+  tidyr::separate(col=Area,into = c("text","Box"),sep = "ID_") |> 
+  dplyr::mutate(Box = as.numeric(Box)) |> 
+  dplyr::mutate(mt = lbs*0.000453592) |> 
+  dplyr::select(-text,-lbs) |> 
+  dplyr::mutate(source = "CAMS")
+
+# Compare to atlantis
+
+# 
+# x.nc = nc_open(here::here("data-raw/atlantis",'neus_outputCATCH.nc'))
+# varname = names(x.nc$var)
+# vn <- grep('SCA_Catch_FC1',varname,value = T)
+# 
+# dd <- ncvar_get(x.nc,"SCA_Catch_FC1",collapse_degen = F)
+# nc_close(x.nc)
+# 
+# rownames(dd) <- 1:nrow(dd)
+# colnames(dd) <- 1:ncol(dd)
+# 
+# d2 <- as.data.frame(dd) |>
+#   tibble::rownames_to_column(var="Box") |>
+#   dplyr::mutate(Box = as.integer(Box) -1) |>
+#   tidyr::pivot_longer(cols=-Box,names_to = "t",values_to = "value") |>
+#   dplyr::mutate(t = as.integer(t),
+#                 Year = 1963+ceiling(t/5)) |> 
+#   dplyr::group_by(Box,Year) |> 
+#   dplyr::summarise(mt = mean(value),
+#                    .groups="drop") |> 
+#   dplyr::relocate(Year,Box,mt) |> 
+#   dplyr::mutate(source = "atlantis")
+# 
+# dataF <- rbind(d2,landings) |> 
+#   dplyr::filter(Year > 1995)
+# 
+
+dataF <- landings
+dataF |>
+  ggplot2::ggplot() +
+  ggplot2::geom_line(ggplot2::aes(x=Year,y=mt,color=source)) +
+  ggplot2::facet_wrap(~Box) +
+  ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90))
+
+```
+
+## Landings (data) by box spatially
+
+```{r plotland6, echo =F}
+dataBox <- dataF |> 
+  dplyr::filter(source == "CAMS") |> 
+  dplyr::group_by(Box) |> 
+  dplyr::summarise(mt = sum(mt))
+
+# select boxes which account fo 95% of landings
+landings <- dataBox |> 
+  dplyr::arrange(desc(mt)) |> 
+  dplyr::mutate(cumsum = cumsum(mt), prop = cumsum/sum(mt)) |> 
+  dplyr::filter(prop <= 0.95)
+
+NEFSCspatial::Neus_atlantis |> 
+  dplyr::left_join(landings,by = c("BOX_ID"="Box"))  |> 
+  ggplot2::ggplot() +
+  ggplot2::geom_sf(ggplot2::aes(fill = mt)) +
+  ggplot2::geom_sf_text(ggplot2::aes(label = BOX_ID),
+                        position = ggplot2::position_dodge(width=0.9),  size=3) 
+
+```
+
+Boxes that contribute 95% of landings `r landings |> dplyr::pull(Box) |> sort()`
+
+
+## Landings (data) by box spatially for main ports
+
+```{r plotland7, echo =F}
+
+# pick out 3 busiest ports based on landings
+ports <- gf |> 
+  dplyr::filter(Code %in% gfCodes) |> 
+  dplyr::group_by(newport,PORTID,lat,lon) |> 
+  dplyr::summarise(lb = sum(InsideLANDED),
+                   .groups = "drop") |> 
+  dplyr::arrange(desc(lb)) |> 
+  dplyr::slice(1:3) 
+
+portids <- ports |> dplyr::pull(PORTID)
+
+landings <- gf |> 
+  dplyr::filter(Code %in% gfCodes,PORTID %in% portids) |>  
+  dplyr::group_by(Year,Area,PORTID) |> 
+  dplyr::summarise(lbs = sum(InsideLANDED),
+                   .groups = "drop") |>
+  tidyr::separate(col=Area,into = c("text","Box"),sep = "ID_") |> 
+  dplyr::mutate(Box = as.numeric(Box)) |> 
+  dplyr::mutate(mt = lbs*0.000453592) |> 
+  dplyr::select(-text,-lbs)
+
+# create map for each
+for (iport in portids) {
+  
+  portName <- ports |> 
+    dplyr::filter(PORTID == iport) |> 
+    dplyr::pull(newport)
+  
+  # portlatlon <- ports |> 
+  #   dplyr::filter(PORTID == iport)
+  
+  dataBox <- landings |> 
+    dplyr::filter(PORTID == iport) |> 
+    dplyr::group_by(Box) |> 
+    dplyr::summarise(mt = sum(mt),
+                     .groups="drop")
+  
+  # create landings by box (summed over time)
+  boxLandings <- dataBox |>
+  dplyr::arrange(desc(mt)) |>
+  dplyr::mutate(cumsum = cumsum(mt), prop = cumsum/sum(mt)) |>
+  dplyr::filter(prop <= 0.95)
+
+  # select boxes which account fo 95% of landings
+  p <- NEFSCspatial::Neus_atlantis |>
+    dplyr::left_join(boxLandings,by = c("BOX_ID"="Box"))  |>
+    ggplot2::ggplot() +
+    ggplot2::geom_sf(ggplot2::aes(fill = mt)) +
+    ggplot2::geom_sf_text(ggplot2::aes(label = BOX_ID),
+                          size=3) +
+    ggplot2::geom_point(data = ports,
+                        mapping=ggplot2::aes(x=lon,y=lat),
+                        color="red") +
+    ggplot2::geom_text(data = ports,
+                       ggplot2::aes(x=lon,y=lat,label = newport),
+                       size = 2.5,
+                       hjust=1, vjust=0) + 
+    ggplot2::ggtitle(portName) +
+    ggplot2::coord_sf(xlim = c(-80,-62)) 
+  
+  suppressWarnings(print(p))
+}
+
+
+```
+
+## Location of ports
+
+Location of ports that land gf
+
+```{r ports1, echo =F}
+ports <- gf |> 
+  dplyr::filter(Code %in% gfCodes) |> 
+  dplyr::group_by(newport,PORTID,lat,lon) |> 
+  dplyr::summarise(lb = sum(InsideLANDED),
+                   .groups = "drop") |> 
+  dplyr::arrange(desc(lb)) |> 
+  DT::datatable()
+ports
+
+```
+
+
+
+## Effort
+
+```{r effort1, echo =F}
+
+
+effort <- gf |> 
+  dplyr::filter(Code %in% gfCodes) |> 
+  dplyr::group_by(Year,newport) |>
+  dplyr::summarise(effort = sum(InsideDAS),
+                   lbs = sum(InsideLANDED),
+                   .groups = "drop") |> 
+  dplyr::filter(newport %in% ports10)
+# |> 
+#   tidyr::pivot_longer(-c(Year,PORTLANDED),names_to = "type",values_to = "value")
+
+ggplot2::ggplot(effort) +
+  ggplot2::geom_line(ggplot2::aes(x=Year,y=effort)) +
+  ggplot2::facet_wrap(~as.factor(newport)) + 
+  ggplot2::ylab("Effort (Days)") +
+  ggplot2::ggtitle("Effort by port for top 10 productive ports")
+
+```
+
+
+
+
+