FUNCTION_SW_CRC_Estimate - TWG Update.R

#####Load Packages
library(tidyr)
library(dplyr)
library(magrittr)
library(eeptools)
library(readxl)
library(lubridate)
library(plyr)
library(writexl)

###Run function to summarize catch from CRCs


CRC.Estimate.function <- function(Year=Year, Season=Season, prop.25C=prop.25C, data.name=data.name){

############Bring in data#########

  #Set the current directory of interest
  current.directory<-paste0(getwd(),"/CRC Data/")
  
  #Bring in data files
  catch=read_excel(paste0(current.directory,data.name))
  
############Prep catch file############
  catch$num_crab=as.numeric(catch$num_crab) #Ensure crabs reported is numeric
  
  ##Handling NA crab (this is not supposed to happen but might show up). Likely due to deactivated holders account. 
  catch %>% filter(is.na(num_crab)) #rows that report NA crab harvested
  catch$num_crab[is.na(catch$num_crab)]=0 #Treat as an unsuccessful trip/0 catch card
  
  ##########Total Catch Reported from all CRCs (including coastal catch)
  sum(catch$num_crab)
  
  #Resolve area formatting issues
  #Convert any "8-1" and "8-2" area format to 81 and 82 for processing
  catch$area[catch$area=="8-1"] <- "81"  
  catch$area[catch$area=="8-2"] <- "82"  
  #Resolve other area formatting issues that may appear. This is rare.
  catch$area[catch$area=="01" | catch$area=="001"] <- "1"  
  catch$area[catch$area=="02" | catch$area=="002"] <- "2"  
  catch$area[catch$area=="03" | catch$area=="003"] <- "3"  
  catch$area[catch$area=="04" | catch$area=="004"] <- "4"  
  catch$area[catch$area=="05" | catch$area=="005"] <- "5"  
  catch$area[catch$area=="06" | catch$area=="006"] <- "6"  
  catch$area[catch$area=="07" | catch$area=="007"] <- "7"  
  catch$area[catch$area=="08" | catch$area=="008"] <- "8"  
  catch$area[catch$area=="09" | catch$area=="009"] <- "9" 
  
  #Some Marine Areas (with reported catch) are entered as NA, set these to 192 (code for an unknown Marine Area). This is rare.
  catch$area[is.na(catch$area) & catch$num_crab > 0]=192
  
  ####Handling catch from coastal Marine Areas (don't include coastal catch in Puget Sound estimate)
  unique(catch$area) #Identify Marine Areas in data frame
  
  catch.coast=catch %>% filter(area=="1"|area=="2"|area=="2-1"|area=="2-2"|area=="21" |area=="22" |area=="3"|area=="519")
  unique(catch.coast$area) #coastal MA's present in data
  crabs.reported.coastal=sum(catch.coast$num_crab) 
  crabs.reported.coastal #Total reported crabs from the coast
 
  ##Puget Sound catch
  
  #Convert areas present in the data frame that are NOT Coastal/PS to unknown (192).
   #Any non-sensible areas present are converted to unknown. This is rare but might be present on a mailed in card.
  #Vector listing all coastal and PS areas. Note: A '0' catch card (did not attempt to crab or catch crab) will have NA for marine area...leave as NA.
  known.areas=c("1","2","21","22","2-1","2-2","3","519","4","5","6","7","8","81","82","9","10","11","12","13","192",NA)
  catch$area[!catch$area %in% known.areas] <- "192"  
  
  ##Select only Puget Sound MA's (including unknown's)
  catch.crc2=catch %>% filter(area=="4"|area=="5"|area=="6"|area=="7"|area=="8"|area=="81"|area=="82"|area=="9"|
                                area=="10"|area=="11"|area=="12"|area=="13"|area=="192")
  catch.crc2$area=as.numeric(catch.crc2$area) # convert area to numeric
  crabs.reported.PS=sum(catch.crc2$num_crab)
  crabs.reported.PS #Total reported crabs from Puget Sound
  
  #Some CRC data sets will not have any catch reported for a specific MA. Because catch is reported/summarized for all areas (even if the catch is 0),
   #we need each area to be present in the catch file.
   #Apply zero catch to one trip for each MA.
  define.area=c(4,5,6,7,8,81,82,9,10,11,12,13,192)
  
  for (i in 1:13)
  {
    catch.crc2[nrow(catch.crc2) + 1,1] = c(define.area[i])
    catch.crc2[nrow(catch.crc2),5] = c(0)
  }
  
 
  ##Cleaning day and month data entry errors. These are very rare.
  #For successful trips, some months and days are entered into the data frame as NA instead of 99 (code for an unknown day or month). Set each NA to 99.
  #Also, set values for day and month that don't make sense on a calendar to 99.
  
  unique(catch.crc2$month) #Identify months in data frame
  catch.crc2$month=as.numeric(catch.crc2$month)
  
  unique(catch.crc2$day) #Identify days in data frame
  catch.crc2$day=as.numeric(catch.crc2$day)
  
  catch.crc2$day[is.na(catch.crc2$day) & catch.crc2$num_crab > 0]=99 #For successful trips, if day=NA set day to 99.
  catch.crc2$month[is.na(catch.crc2$month) & catch.crc2$num_crab > 0]=99 #For successful trips, if month=NA set month to 99.
  
  #Convert any non-sensible months in the data frame to unknown month (99)
  known.months=c(1,2,3,4,5,6,7,8,9,10,11,12,99,NA)
  catch.crc2$month[!catch.crc2$month %in% known.months] <- 99  
  
  #Convert any non-sensible days in the data frame to unknown day (99)
  known.days=c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,99,NA)
  catch.crc2$day[!catch.crc2$day %in% known.days] <- 99  
  
  #Create a DATE column for filtering in-season and out-season catch: Convert year, month, and day columns into a date column for filtering
  catch.crc2$date=as.Date(with(catch.crc2, paste(year,month,day,sep="-")), "%Y-%m-%d")
  
  #Identify failed dates (this identifies data entry errors related to the day).
  ##Example: June 31st, September 31st, etc.
  date.error=catch.crc2 %>% filter(is.na(date) & day < 32 & month < 13) #Identify where the date failed when the day/month is known
  #Change the day to 99 for dates identified above
  catch.crc2 <- catch.crc2 %>%
    mutate(day = case_when(
      is.na(date) & day < 32 & month < 13 ~ 99,
      TRUE ~ day))
  
#############    
  ##Read in file that contains which areas were fully closed (did not have a season)
  
  closed.data.name=paste0("Closed_Areas_Inputs_",Season,".xlsx")
  current.directory2<-paste0(getwd(),"/CRC Data/CRC Estimate Function Inputs/",Season,"/")
  closed.areas=read_excel(paste0(current.directory2,closed.data.name),sheet = paste0(Year))
  
  ##Create new column to identify an area that was fully closed (never had a season). Used later.
  catch.crc2 = catch.crc2 %>%
    mutate(area.closed = ifelse( (area %in% unique(closed.areas$closed.areas)), "yes", "no"))
  
  
#############   
  ##Read in file that contains the season dates by area
  
  dates=paste0("Seasons_Areas_Inputs_",Season,".xlsx")
  current.directory2<-paste0(getwd(),"/CRC Data/CRC Estimate Function Inputs/",Season,"/")
  yr.data=read_excel(paste0(current.directory2,dates),sheet = paste0(Year))
  
  yr.data$season.start=as.Date(yr.data$season.start) #Convert season start to a date
  yr.data$season.end=as.Date(yr.data$season.end) #Convert season end to a date
  
  
###########Run loop to summarize catch by date categories and Marine Area#############
  
  #Pre-define lists and vectors for looping
  area.list=list()
  date.list=list()
  areas=unique(catch.crc2$area)
  in.season=list()
  in.season.sum=c()
  in.season.no.day=list()
  in.season.no.day.sum=c()
  out.season=list()
  out.season.sum=c()
  out.season.no.day=list()
  out.season.no.day.sum=c()
  Unk.date=list()
  Unk.date.sum=c()
  closed.area=list()
  closed.area.sum=c()
  
  ##Run for loop to summarize catch:
  #1) Open Season
  #2) Closed Season
  #3) Catch with an unknown month
  
  #Any crab reported in September with an unknown day is included in the Sept 1-Labor Day estimate.
  
  areas=c(4,5,6,7,8,81,82,9,10,11,12,13,192)
  
  #Run for loop based on Marine Area while filtering for the date
  for (i in 1:length(unique(catch.crc2$area)))
  {
    
    ##Filter by area
    area.list[[i]]=catch.crc2 %>% filter(area==areas[i])
    date.list[[i]]=yr.data %>% filter(area==areas[i])
    
    ##1) In-season catch
    #Filter and sum all in-season catch with a known date
     #Will only filter for dates defined in the seasons by area file.
    in.season[[i]]=area.list[[i]] %>% filter(date >= date.list[[i]]$season.start & date <= date.list[[i]]$season.end)
    in.season.sum[i]=sum(in.season[[i]]$num_crab)
    
    #Filter and sum all in-season catch with an unknown date (known month but unknown day)
     #Will only filter for months defined in the seasons by area file.
      #Assume anything reported during an in-season month without a day is in-season.
    in.season.no.day[[i]]=area.list[[i]] %>% filter(month==date.list[[i]]$in.month.1 & day==99 | month==date.list[[i]]$in.month.2 & day==99 | 
                                                      month==date.list[[i]]$in.month.3 & day==99 | month==date.list[[i]]$in.month.5 & day==99 |
                                                      month==date.list[[i]]$in.month.5 & day==99 | month==date.list[[i]]$in.month.6 & day==99 |
                                                      month==date.list[[i]]$in.month.7 & day==99 | month==date.list[[i]]$in.month.8 & day==99 | 
                                                      month==date.list[[i]]$in.month.9 & day==99 | month==date.list[[i]]$in.month.10 & day==99 |
                                                      month==date.list[[i]]$in.month.11 & day==99 | month==date.list[[i]]$in.month.12 & day==99)
    in.season.no.day.sum[i]=sum(in.season.no.day[[i]]$num_crab)
    
    ##2) Out of season catch
    #Filter and sum all out-season catch with a known date
     #Will only filter for dates defined in the seasons by area file.
    out.season[[i]]=area.list[[i]] %>% filter(date < date.list[[i]]$season.start | date > date.list[[i]]$season.end)
    out.season.sum[i]=sum(out.season[[i]]$num_crab)
    
    #Filter and sum all out-season catch with an unknown date (known month but unknown day)
     #Will only filter for months defined in the seasons by area file.
    out.season.no.day[[i]]=area.list[[i]] %>% filter(month==date.list[[i]]$out.month.1 & day==99 | month==date.list[[i]]$out.month.2 & day==99 |
                                                       month==date.list[[i]]$out.month.3 & day==99 | month==date.list[[i]]$out.month.4 & day==99 |
                                                       month==date.list[[i]]$out.month.5 & day==99 | month==date.list[[i]]$out.month.6 & day==99 | 
                                                       month==date.list[[i]]$out.month.7 & day==99 | month==date.list[[i]]$out.month.8 & day==99 |
                                                       month==date.list[[i]]$out.month.9 & day==99 | month==date.list[[i]]$out.month.10 & day==99 | 
                                                       month==date.list[[i]]$out.month.11 & day==99 | month==date.list[[i]]$out.month.12 & day==99)
    out.season.no.day.sum[i]=sum(out.season.no.day[[i]]$num_crab)
    
    #2a) Closed area catch (area without a season) is treated as out-season catch
    #Filter for closed areas and sum all catch and treat as out-season
    closed.area[[i]]=area.list[[i]] %>% filter(area.closed=="yes")
    closed.area.sum[i]=sum(closed.area[[i]]$num_crab)
    
    ##3) Catch with unknown month
    #Filter and sum all catch without a month
    Unk.date[[i]]=area.list[[i]] %>% filter(month==99)
    Unk.date.sum[i]=sum(Unk.date[[i]]$num_crab) 
    
  }
  
  #Add all in-season catch categories together 
  ##1) 'in season with known date' 
  ##2) 'in season with known month but unknown day'
  in.season.sum2=c(in.season.sum+in.season.no.day.sum)
  
  #Add all out-season catch categories together 
  ##1) 'out season with known date' 
  ##2) 'out season with known month but unknown day'
  ##3) 'closed are catch'
  out.season.sum2=c(out.season.sum+out.season.no.day.sum+closed.area.sum)
  
  #Check that all catch has been accounted for (not including catch from coastal Marine Areas)
  total.crab.check=sum(catch.crc2$num_crab)==sum(c(in.season.sum2+out.season.sum2+Unk.date.sum))
  #STOP the function if this condition is not met (If FALSE, the for loop did not account for every category of catch). Should always be TRUE.
  if(total.crab.check==FALSE)
  {
    stop()
  }
  
  #############Create CRC Table 1: Sum catch by each date category and Marine Area##########
  
  #Order the data by Marine Area: 4-13
  catch.crc2=catch.crc2 %>% arrange(factor(area, levels = c('4', '5', '6', '7', '8', '81', '82','9','10','11','12','13','192','Total')))
  Marine.Area=unique(catch.crc2$area) #define marine area column
  #Put everything together in a table
  Table1=as.data.frame(cbind(Marine.Area,in.season.sum2,out.season.sum2,Unk.date.sum))
  Table1
  
  colnames(Table1) <- c("Marine Area", "In-Season", "Out of Season", "Unknown Month")
  
  ##Create an output table (Table1 Output: Total crabs, total pounds, w/ decimals rounded)
  Table1.output=Table1
  
  ###Create a new row and column for total crab and pounds
  Table1.output$`Total Crab`=rowSums(Table1.output[,2:4])
  Table1.output$`Pounds (lbs.)`=Table1.output$`Total Crab`*1.8
  Total=c(colSums(Table1.output[,2:6]))
  Total=c("Total",Total)
  Table1.output=as.data.frame(rbind(Table1.output,Total))
  
  #Ensure all counts of crab are numeric not a character (for rounding)
  Table1.output[,2:ncol(Table1.output)]=Table1.output[,2:ncol(Table1.output)] %>%
    mutate_all(as.numeric)
  
  ##Round to nearest crab
  Table1.output[1:13,ncol(Table1.output)]=round_any(Table1.output[1:13,ncol(Table1.output)], 1)
  #Update Total Pounds after rounding
  Table1.output[14,6]=sum(Table1.output$`Pounds (lbs.)`[1:13])
  
  #########Create CRC Table 2: Distribute MA 8 catch to 8-1 and 8-2############
  Table2=Table1
  
  #Find catch proportions for 81 and 82
  eight.prop.in=Table2$`In-Season`[6:7]/sum(Table2$`In-Season`[6:7])
  eight.prop.out=Table2$`Out of Season`[6:7]/sum(Table2$`Out of Season`[6:7])
  eight.prop.unk=Table2$`Unknown Month`[6:7]/sum(Table2$`Unknown Month`[6:7])
  eight.prop.combined=as.data.frame(cbind(eight.prop.in,eight.prop.out, eight.prop.unk))
  
  #If the sum of the catch proportions for 8-1/8-2 is 0 (or NaN; cant divide by zero), assign in-season catch proportions of 8-1/8-2.
  for(i in 1:3){
    if(sum(eight.prop.combined[1:2,i])==0 | sum(is.nan(eight.prop.combined[1:2,i]))){
      eight.prop.combined[1:2,i]=eight.prop.combined[1:2,1]}} #Assign in-season catch proportions
  
  #Apportion MA8 catch to 81 and 82 based on proportions
  Table2[6:7,2]=(eight.prop.combined[,1]*Table2$`In-Season`[5])+(Table2[6:7,2])
  Table2[6:7,3]=(eight.prop.combined[,2]*Table2$`Out of Season`[5])+(Table2[6:7,3])
  Table2[6:7,4]=(eight.prop.combined[,3]*Table2$`Unknown Month`[5])+(Table2[6:7,4])
  
  #Now remove MA 8 row
  Table2=Table2[-c(5),]
  
  #########Distribute unknown area catch (192) to known areas############
  
  #Find catch proportions for each area
  unk.prop.in=Table2$`In-Season`[1:11]/sum(Table2$`In-Season`[1:11])
  unk.prop.out=Table2$`Out of Season`[1:11]/sum(Table2$`Out of Season`[1:11])
  unk.prop.unk=Table2$`Unknown Month`[1:11]/sum(Table2$`Unknown Month`[1:11])
  unk.prop.combined=as.data.frame(cbind(unk.prop.in,unk.prop.out, unk.prop.unk))
  
  ###############
  #If the sum of the unknown month catch proportions for MAs 4-13 is 0 (or NaN; cant divide by zero), assign in-season catch proportions for all areas.
  #Highly unlikely for in-season and out-season catch but may happen for unknown month catch.
  for(i in 1:3){
    if(sum(unk.prop.combined[1:11,i])==0 | sum(is.nan(unk.prop.combined[1:11,i]))){
      unk.prop.combined[1:11,i]=unk.prop.combined[1:11,1]}} #Assign in-season catch proportions
  ###############
  
  #Apportion area 192 (unknown) catch to all areas based on proportions
  Table2[1:11,2]=(unk.prop.combined[,1]*Table2$`In-Season`[12])+(Table2[1:11,2])
  Table2[1:11,3]=(unk.prop.combined[,2]*Table2$`Out of Season`[12])+(Table2[1:11,3])
  Table2[1:11,4]=(unk.prop.combined[,3]*Table2$`Unknown Month`[12])+(Table2[1:11,4])
  
  #Now remove MA 192 row
  Table2=Table2[-c(12),]
  
  ##Table2 Output (Total crabs and pounds and round decimals)
  Table2.output=Table2
  
  #Ensure all counts of crab are numeric not a character
  Table2.output[,2:4]=Table2.output[,2:4] %>%
    mutate_all(as.numeric)
  
  #Round numbers
  Table2.output[1:11,2]=round_any(Table2.output[1:11,2],1)
  Table2.output[1:11,3]=round_any(Table2.output[1:11,3],1)
  Table2.output[1:11,4]=round_any(Table2.output[1:11,4],1)
  
  ###Create a new row and column for total crab and pounds
  Table2.output$`Total Crab`=rowSums(Table2.output[,2:4])
  Table2.output$`Pounds (lbs.)`=Table2.output$`Total Crab`*1.8
  Total=c(colSums(Table2.output[1:11,2:6]))
  Total=c("Total",Total)
  Table2.output=as.data.frame(rbind(Table2.output,Total))
  
  #Round total pounds and update
  #Ensure all counts of crab are numeric not a character
  Table2.output[,2:6]=Table2.output[,2:6] %>%
    mutate_all(as.numeric)
  Table2.output[1:11,6]=round_any(Table2.output[1:11,6],1)
  
  #Update Total Pounds after rounding
  Table2.output[12,6]=sum(Table2.output$`Pounds (lbs.)`[1:11])
  
  
###########Create Table 3: Split Marine Area 9 into 25C and non-25C############
  
  Table3=Table2
  
  #Define MA9 total crab
  nine.catch=Table3[7,2:4]
  
  #Adjust MA9 crab to non 25c portion
  #Catch Area 25C represents 28% of catch from Marine Area 9
  Table3[7,2:4]=Table3[7,2:4]*(1-prop.25C)
  
  #Create MA9 25C portion
  Table3[nrow(Table3) + 1,1:4] = c("9 (25C)",nine.catch*prop.25C)
  
  #Re-order the data by Marine Area
  Table3=Table3 %>% arrange(factor(`Marine Area`, levels = c('4', '5', '6', '7', '81', '82','9','9 (25C)','10','11','12','13')))
  
  #Rename MA9 to MA 9 (non-25C)
  Table3[7,1]="9 (non-25C)"
  
  ##Table3 Output (Total crabs and pounds and round decimals)
  Table3.output=Table3
  
  #Ensure all counts of crab are numeric not a character
  Table3.output[,2:4]=Table3.output[,2:4] %>%
    mutate_all(as.numeric)
  
  #Round numbers
  Table3.output[1:12,2]=round_any(Table3.output[1:12,2],1)
  Table3.output[1:12,3]=round_any(Table3.output[1:12,3],1)
  Table3.output[1:12,4]=round_any(Table3.output[1:12,4],1)
  
  #Update total crabs and pounds
  Table3.output$`Total Crab`=rowSums(Table3.output[,2:4])
  Table3.output$`Pounds (lbs.)`=Table3.output$`Total Crab`*1.8
  Total=c(colSums(Table3.output[1:12,2:6]))
  Total=c("Total",Total)
  Table3.output=as.data.frame(rbind(Table3.output,Total))

  #Ensure all counts of crab are numeric not a character
  Table3.output[,2:6]=Table3.output[,2:6] %>%
    mutate_all(as.numeric)
  
  Table3.output[1:12,6]=round_any(Table3.output[1:12,6],1)
  
  #Update Total Pounds after rounding
  Table3.output[13,6]=sum(Table3.output$`Pounds (lbs.)`[1:12])
  
  
#########Create Table 4: Estimate non response catch (Apply bias correction)###############
  
  #Define total cards and reported cards based on the Year
  #Pull in data frame with CRC Reporting Rates by Year
  CRCs="CRC_Reporting_Rates.xlsx"
  current.directory3<-paste0(getwd(),"/CRC Data/CRC Estimate Function Inputs/")
  yr.data=read_excel(paste0(current.directory3,CRCs))
  yr.data$yr.vec=as.numeric(yr.data$yr.vec)
  total <- yr.data[yr.data$yr.vec==Year, paste0("total.crc.",Season)]
  reported=yr.data[yr.data$yr.vec==Year,paste0("reported.crc.",Season)]
  total=as.numeric(total)
  reported=as.numeric(reported)
  
  Total.crc=total
  Reported.crc=reported

  #Calculate response rate
  Resp.rate=Reported.crc/Total.crc
  
  #Using bias correction model, calculate the bias.correction based on the response rate
  Bias.corr=(-0.5*((Resp.rate)^2))+(1.39*(Resp.rate))+0.097
  
  #Calculate the expansion factor based on reporting rates
  Expansion.fact=Total.crc/Reported.crc
  
  #Rename data
  Table4=Table3
  
  #Calculate harvest with bias correction and expansion factor
  Table4[1:12,2:4]=(Table4[1:12,2:4])*(Expansion.fact)*(Bias.corr)
  
  ##Table4 Output (Total crabs and pounds and round decimals)
  Table4.output=Table4
  
  #Ensure all counts of crab are numeric not a character
  Table4.output[,2:4]=Table4.output[,2:4] %>%
    mutate_all(as.numeric)
  
  #Round numbers
  Table4.output[1:12,2]=round_any(Table4.output[1:12,2],1)
  Table4.output[1:12,3]=round_any(Table4.output[1:12,3],1)
  Table4.output[1:12,4]=round_any(Table4.output[1:12,4],1)
  
  #Update total crabs and pounds
  Table4.output$`Total Crab`=rowSums(Table4.output[,2:4])
  Table4.output$`Pounds (lbs.)`=Table4.output$`Total Crab`*1.8
  Total=c(colSums(Table4.output[1:12,2:6]))
  Total=c("Total",Total)
  Table4.output=as.data.frame(rbind(Table4.output,Total))
  
  #Ensure all counts of crab are numeric not a character
  Table4.output[,2:6]=Table4.output[,2:6] %>%
    mutate_all(as.numeric)
  
  Table4.output[1:12,6]=round_any(Table4.output[1:12,6],1)
  
  #Update Total Pounds after rounding
  Table4.output[13,6]=sum(Table4.output$`Pounds (lbs.)`[1:12])
  

#######Create Table 5: Add catch from 25C to Marine Area 12

  Table5.output=Table4.output
  Table5.output[11,2:6]=Table5.output[11,2:6]+Table5.output[8,2:6]
  Table5.output=Table5.output[-c(8),]
  Table5.output[10,1]="12 (w/ 25C)"
  
  
###### Create Table 6: Summary table of estimate
  
  crabs.corrected.PS=Table5.output[12,5]
  final.estiamte.PS=Table5.output[12,6]
  
  Table6.output=as.data.frame(cbind(crabs.reported.coastal,
                             crabs.reported.PS,
                             Bias.corr,
                             Resp.rate,
                             crabs.corrected.PS,
                             final.estiamte.PS))
  
  colnames(Table6.output) <- c("Coastal Crabs Reported", "Puget Sound Crabs Reported","Bias Correction", "CRC Response Rate",
                        "PS Bias Corrected Crabs","PS Final Rec Estimate (lbs.)")
  
#########Export as table############
  
  current.directory4<-paste0(getwd(),"/Estimate Output/All Year Summary")
  
  dataset_names <- list('Sheet1' = Table1.output, 'Sheet2' = Table2.output, 'Sheet3' = Table3.output, 
                        'Sheet4' = Table4.output , 'Sheet5' = Table5.output, 'Sheet6' = Table6.output)
  
  Output.directory=paste0(current.directory4,paste0("/",Year,"_",Season,"_CRC_Tables",".xlsx"))
  
  write_xlsx(dataset_names, Output.directory)
  
  
}


#!!!!!!!!!!!!!!#

#Manually adjust CRC year of interest and ensure input xlsx files are up to date

# 1) Ensure catch file is in correct format with columns ordered and name by: area, month, day, year, num_crab, ID
# 2) Ensure catch file name is: "Catch Data Season YEAR.xlsx"
# 3) Ensure "Seasons_Areas_Inputs.xlsx" file is up to date with the correct seasons by area for the correct year
# 4) Ensure "Closed_Areas_Inputs.xlsx" file is up to date with the correct closed areas listed as inputs for the correct year
# 5) Input year of interest below
# 6) Input season of interest below
# 7) Input proportion of catch in Marine Area 9 that occurs in the 25C portion

#!!!!!!!!!!!!!!#

##Define the CRC year of interest
Year=2024
Season="Summer" #("Summer" or "Winter")
prop.25C=0.28 #Proportion of catch in Marine Area 9 that occurs in 25C

#Define the CRC file name (Example: Catch Data Summer 2022.xlsx)
data.name=paste0("Catch Data"," ",Season," ",Year,".xlsx")

data.name="Summer_2024_Catch_Data.xlsx"


######RUN FUNCTION
CRC.Estimate.function(Year, Season, prop.25C, data.name)