From 79e1958ac617eb19640b8e617c2300aa0fdc8047 Mon Sep 17 00:00:00 2001
From: GaitlynMalone-NOAA
 <115589280+GaitlynMalone-NOAA@users.noreply.github.com>
Date: Wed, 24 Sep 2025 09:48:23 -0400
Subject: [PATCH 1/3] Move previous closure script

---
 .../Closures/Closure_Reopening.R              | 313 ------------------
 1 file changed, 313 deletions(-)
 delete mode 100644 ODM-MH-Analysis_ready/Closures/Closure_Reopening.R

diff --git a/ODM-MH-Analysis_ready/Closures/Closure_Reopening.R b/ODM-MH-Analysis_ready/Closures/Closure_Reopening.R
deleted file mode 100644
index b79c4c4..0000000
--- a/ODM-MH-Analysis_ready/Closures/Closure_Reopening.R
+++ /dev/null
@@ -1,313 +0,0 @@
-# MH Closure processing
-
-#Load packages ####
-librarian::shelf(here, tidyverse, lubridate, dplyr, tidyr, neatRanges, splitstackshape)
-
-# Read in MH Data log ####
-# mh_data_log <- readRDS(here("ODM-MH-Data_log", "data", "results", "MH_DL_2023Nov13.RDS"))
-# 
-# # Select species and region ####
-# spp <- 'PORGY, RED'
-# region <- 'SOUTH ATLANTIC'
-
-# Seasonal Closures ####
-# Filter dataset to seasonal closure regulations for the specified species and region
-Seasonal_closures <- mh_spp_closure %>%
-  filter(MANAGEMENT_TYPE_USE == 'CLOSURE', MANAGEMENT_STATUS_USE == 'SEASONAL', NEVER_IMPLEMENTED == 0, REG_REMOVED == 0)
-
-# Create date_sequence to expand dates between EFFECTIVE_DATE and END_DATE2 
-expand_seasonal <- Seasonal_closures %>%
-  mutate(date_sequence = map2(EFFECTIVE_DATE, END_DATE2, seq, by = "days")) %>%
-  unnest(date_sequence)
-
-# Add START_YEAR_expand and END_YEAR_expand to extract the year from the date_sequence field
-expand_seasonal_year <- expand_seasonal %>%
-  #rowwise() %>%
-  mutate(START_YEAR_expand = year(date_sequence),
-         END_YEAR_expand = year(date_sequence))
-
-# Create START_DATE_EXPAND and END_DATE_EXPAND using start/end day and month information and the expanded year
-expand_seasonal_date <- expand_seasonal_year %>%
-  mutate(START_DATE_EXPAND = as.Date(paste(START_YEAR_expand, START_MONTH, START_DAY_USE, sep = "-")),
-         END_DATE_EXPAND = as.Date(paste(END_YEAR_expand, END_MONTH_USE, END_DAY_USE, sep = "-")))
-
-# Remove records where date_sequence is prior to the START_DATE_EXPAND and date_sequence is after the END_DATE_EXPAND
-remove_seasonal_date <- expand_seasonal_date %>%
-  filter(date_sequence >= START_DATE_EXPAND,
-         END_DATE_EXPAND >= date_sequence)
-
-# Create CLOSE_OPEN field to indicate that expanded records are related to Closures or Openings
-add_closure_value_seasonal <- remove_seasonal_date %>%
-  mutate(CLOSE_OPEN = VALUE)
-
-# Monthly Closures ####
-monthly_closures <-  mh_spp_closure %>%
-  filter(MANAGEMENT_TYPE_USE == 'CLOSURE', MANAGEMENT_STATUS_USE == 'MONTHLY RECURRING', NEVER_IMPLEMENTED == 0, REG_REMOVED == 0)
-
-# Expand dates between EFFECTIVE_DATE and END_DATE2
-expand_monthly <- monthly_closures %>%
-  mutate(date_sequence = map2(EFFECTIVE_DATE, END_DATE2, seq, by = "days")) %>%
-  unnest(date_sequence)
-
-# Add START_YEAR_expand and END_YEAR_expand to extract the year from the date_sequence field
-# Add START_MONTH_expand and END_MONTH_expand to extract the month from the date_sequence field
-# Takes 20 mins to run!!!!
-start <- Sys.time()
-expand_monthly_year <- expand_monthly %>%
-  #rowwise() %>%
-  mutate(START_YEAR_expand = year(date_sequence),
-         END_YEAR_expand = year(date_sequence),
-         # case where the monthly recurring goes from the 15th to the 1st of the following month
-         START_MONTH_expand = case_when(END_DAY_USE < START_DAY_USE & day(date_sequence) < START_DAY_USE & month(date_sequence) != 1 ~ month(date_sequence) - 1,
-                                        END_DAY_USE < START_DAY_USE & day(date_sequence) < START_DAY_USE & month(date_sequence) == 1 ~ 12,
-                                        TRUE ~ month(date_sequence)),
-         END_MONTH_expand = case_when(END_DAY_USE < START_DAY_USE & day(date_sequence) < START_DAY_USE & month(date_sequence) != 1 ~ month(date_sequence),
-                                      END_DAY_USE < START_DAY_USE & day(date_sequence) >= START_DAY_USE & month(date_sequence) + 1 <= 12 ~ month(date_sequence) + 1,
-                                      END_DAY_USE < START_DAY_USE & day(date_sequence) >= START_DAY_USE & month(date_sequence) + 1 == 13 ~ 1,
-                                      TRUE ~ month(date_sequence))) %>%
-  # only include months within the start/end month range
-  # when monthly recurring, the start/end month range refers to the first and last month where the recurring closure applies
-  # i.e. start month = 2 and end month = 11
-      # mean recurring closures start in Feb and end in Nov
-      # can't use END_MONTH_expand in filter for cases when the recurring closures spans 2 months (i.e. 15th -1st)
-  filter(START_MONTH_expand >= START_MONTH &
-         START_MONTH_expand <= END_MONTH)
-Sys.time()-start
-
-# Create Expand_days to indicate the day of the week for the closure/reopening
-expand_monthly_date <- expand_monthly_year %>%
-  mutate(START_DATE_EXPAND = as.Date(paste(START_YEAR_expand, START_MONTH_expand, START_DAY_USE, sep = "-")),
-         END_DATE_EXPAND = as.Date(paste(END_YEAR_expand, END_MONTH_expand, END_DAY_USE, sep = "-")))
-
-# Remove records where date_sequence is prior to the START_DATE_EXPAND and date_sequence is after the END_DATE_EXPAND
-remove_monthly_date <- expand_monthly_date %>%
-  filter(date_sequence >= START_DATE_EXPAND,
-         date_sequence <= END_DATE_EXPAND)
-
-# Create CLOSE_OPEN field to indicate that expanded records are related to Closures or Openings
-add_closure_value_monthly <- remove_monthly_date %>%
-  mutate(CLOSE_OPEN = VALUE)
-
-# Weekly Closures ####
-# Filter dataset to Weekly Recurring regulations
-Weekly_closures <-  mh_spp_closure %>%
-  filter(MANAGEMENT_TYPE_USE == 'CLOSURE', MANAGEMENT_STATUS_USE == 'WEEKLY RECURRING', NEVER_IMPLEMENTED == 0, REG_REMOVED == 0)
-
-# Expand dates between EFFECTIVE_DATE and END_DATE2
-expand_weekly <- Weekly_closures %>%
-  mutate(date_sequence = map2(EFFECTIVE_DATE, END_DATE2, seq, by = "days")) %>%
-  unnest(date_sequence)
-
-# Add START_YEAR_expand and END_YEAR_expand to extract the year from the date_sequence field
-expand_weekly_year <- expand_weekly %>%
-  #rowwise() %>%
-  mutate(START_YEAR_expand = year(date_sequence),
-         END_YEAR_expand = year(date_sequence))
-
-# Create Expand_day_of_week to indicate the day of the week for the closure/reopening
-expand_weekly_day_of_week <- expand_weekly_year %>%
-  mutate(Expand_day_of_week = wday(date_sequence, label = TRUE))
-
-# Create START_DAY_OF_WEEK_EXPAND and END_DAY_OF_WEEK_EXPAND to format the days of the week to align with Expand_day_of_week
-expand_weekly_day_format <- expand_weekly_day_of_week %>%
-  mutate(START_DAY_OF_WEEK_EXPAND = case_when(
-    START_DAY_OF_WEEK_USE == "MONDAY" ~ "Mon",
-    START_DAY_OF_WEEK_USE == "TUESDAY" ~ "Tue",
-    START_DAY_OF_WEEK_USE == "WEDNESDAY" ~ "Wed",
-    START_DAY_OF_WEEK_USE == "THURSDAY" ~ "Thu",
-    START_DAY_OF_WEEK_USE == "FRIDAY" ~ "Fri",
-    START_DAY_OF_WEEK_USE == "SATURDAY" ~ "Sat",
-    START_DAY_OF_WEEK_USE == "SUNDAY" ~ "Sun",
-    TRUE ~ START_DAY_OF_WEEK_USE),
-    END_DAY_OF_WEEK_EXPAND = case_when(
-      END_DAY_OF_WEEK_USE == "MONDAY" ~ "Mon",
-      END_DAY_OF_WEEK_USE == "TUESDAY" ~ "Tue",
-      END_DAY_OF_WEEK_USE == "WEDNESDAY" ~ "Wed",
-      END_DAY_OF_WEEK_USE == "THURSDAY" ~ "Thu",
-      END_DAY_OF_WEEK_USE == "FRIDAY" ~ "Fri",
-      END_DAY_OF_WEEK_USE == "SATURDAY" ~ "Sat",
-      END_DAY_OF_WEEK_USE == "SUNDAY" ~ "Sun",
-      TRUE ~ END_DAY_OF_WEEK_USE))
-
-# Keep any records where Expand_day_of_week is between START_DAY_OF_WEEK_EXPAND and END_DAY_OF_WEEK_EXPAND
-expand_weekly_day_keep <- expand_weekly_day_format %>%
-  filter(Expand_day_of_week >= START_DAY_OF_WEEK_EXPAND |
-           Expand_day_of_week <= END_DAY_OF_WEEK_EXPAND)
-
-# Create CLOSE_OPEN field to indicate that expanded records are related to Closures or Openings
-add_closure_value_weekly <- expand_weekly_day_keep %>%
-  mutate(CLOSE_OPEN = VALUE)
-
-# One Time Closures ####
-# Filter dataset to One Time regulations
-One_closures <- mh_spp_closure %>%
-  filter(MANAGEMENT_TYPE_USE == 'CLOSURE', MANAGEMENT_STATUS_USE == 'ONCE', NEVER_IMPLEMENTED == 0, REG_REMOVED == 0)
-
-# Expand dates between EFFECTIVE_DATE and END_DATE2
-expand_one <- One_closures %>%
-  filter(END_DATE2 >= START_DATE2) %>%
-  mutate(date_sequence = map2(START_DATE2, END_DATE2, seq, by = "1 day")) %>%
-  unnest(date_sequence)
-
-# Create CLOSE_OPEN field to indicate that expanded records are related to Closures or Openings
-add_closure_value_one <- expand_one %>%
-  mutate(CLOSE_OPEN = VALUE)
-
-# Join in Wide Form
-# First clean up duplicates
-# Then limit variables and rename
-# Then create final OPEN/CLOSED field based on all closure events
-# Takes an 1.5 hours to run if use rowwise!!!
-# Instead remove rowwise and pmax for faster runtime
-Sys.time()
-closure_all <- add_closure_value_one %>%
-  select(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, MANAGEMENT_TYPE_USE, date_sequence, 
-         CLUSTER, REGULATION_ID, FR_CITATION, CLOSE_OPEN) %>%
-  arrange(date_sequence, desc(FR_CITATION)) %>%
-  group_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, MANAGEMENT_TYPE_USE, date_sequence) %>%
-  # Retain only most recent FR if there are 2 records with the same date_sequence
-  slice(1) %>%
-  rename(CLUSTER_one = "CLUSTER",
-         REGULATION_ID_one = "REGULATION_ID",
-         FR_CITATION_one = "FR_CITATION",
-         CLOSE_OPEN_one = "CLOSE_OPEN") %>%
-  full_join(add_closure_value_weekly %>%
-              select(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, MANAGEMENT_TYPE_USE, date_sequence,  
-                     CLUSTER, REGULATION_ID, FR_CITATION, CLOSE_OPEN) %>%
-              arrange(date_sequence, desc(FR_CITATION)) %>%
-              group_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, MANAGEMENT_TYPE_USE, date_sequence) %>%
-              # Retain only most recent FR if there are 2 records with the same date_sequence
-              slice(1) %>%
-              rename(CLUSTER_weekly = "CLUSTER",
-                     REGULATION_ID_weekly = "REGULATION_ID",
-                     FR_CITATION_weekly = "FR_CITATION",
-                     CLOSE_OPEN_weekly = "CLOSE_OPEN"),
-            by = join_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, MANAGEMENT_TYPE_USE, date_sequence)) %>%
-  full_join(add_closure_value_seasonal %>%
-              select(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, MANAGEMENT_TYPE_USE, date_sequence,  
-                     CLUSTER, REGULATION_ID, FR_CITATION, CLOSE_OPEN) %>%
-              arrange(date_sequence, desc(FR_CITATION)) %>%
-              group_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, MANAGEMENT_TYPE_USE, date_sequence) %>%
-              # Retain only most recent FR if there are 2 records with the same date_sequence
-              slice(1) %>%
-              rename(CLUSTER_seasonal = "CLUSTER",
-                     REGULATION_ID_seasonal = "REGULATION_ID",
-                     FR_CITATION_seasonal = "FR_CITATION",
-                     CLOSE_OPEN_seasonal = "CLOSE_OPEN"),
-            by = join_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, MANAGEMENT_TYPE_USE, date_sequence)) %>%
-  full_join(add_closure_value_monthly %>%
-              select(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, MANAGEMENT_TYPE_USE, date_sequence,  
-                     CLUSTER, REGULATION_ID, FR_CITATION, CLOSE_OPEN) %>%
-              arrange(date_sequence, desc(FR_CITATION)) %>%
-              group_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, MANAGEMENT_TYPE_USE, date_sequence) %>%
-              # Retain only most recent FR if there are 2 records with the same date_sequence
-              slice(1) %>%
-              rename(CLUSTER_monthly = "CLUSTER",
-                     REGULATION_ID_monthly = "REGULATION_ID",
-                     FR_CITATION_monthly = "FR_CITATION",
-                     CLOSE_OPEN_monthly = "CLOSE_OPEN"),
-            by = join_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, MANAGEMENT_TYPE_USE,  date_sequence)) %>%
-  # create final OPEN_CLOSE field 
-  ungroup() %>% 
-  #rowwise() %>%
-  mutate(final_FR = pmax(FR_CITATION_one, FR_CITATION_seasonal, FR_CITATION_weekly, FR_CITATION_monthly, na.rm = T)) %>%
-  mutate(final_CLOSE_OPEN = case_when(final_FR == FR_CITATION_one ~ CLOSE_OPEN_one,
-                                      final_FR == FR_CITATION_weekly ~ CLOSE_OPEN_weekly,
-                                      final_FR == FR_CITATION_seasonal ~ CLOSE_OPEN_seasonal,
-                                      final_FR == FR_CITATION_monthly ~ CLOSE_OPEN_monthly))
-Sys.time()
-
-# Chck
-chk <- closure_all %>% 
-  mutate(YEAR = (year(date_sequence))) %>%
-  group_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, YEAR, final_FR, final_CLOSE_OPEN) %>% 
-  summarize(ndays = n())
-
-#########################################################
-# Join in Long Form
-# Join Closure Types Together ####
-# Combine closure data frames
-Combined_closures <- bind_rows(add_closure_value_seasonal, add_closure_value_weekly, add_closure_value_one)
-
-# Sort the data frame by date_sequence
-sort_closures <- Combined_closures %>%
-  arrange(date_sequence)
-
-# Create Multi_expand to indicate whether records have the same date_sequence 
-sort_closures_multi <- sort_closures %>%
-  group_by(date_sequence) %>%
-  mutate(Multi_expand = ifelse(n() > 1, 1, 0)) %>%
-  ungroup()
-
-# Keep information from newest FR 
-closures_remove_multi <- sort_closures_multi %>%
-  arrange(date_sequence, desc(FR_CITATION)) %>%
-  group_by(ZONE_USE, date_sequence) %>%
-  slice(1) %>%
-  ungroup()
-
-# Remove extra processing fields from dataset
-closures_remove_fields <- closures_remove_multi %>%
-  select(-START_YEAR_expand, -END_YEAR_expand, -START_DATE_EXPAND, -END_DATE_EXPAND,
-         -START_DAY_OF_WEEK_EXPAND, -END_DAY_OF_WEEK_EXPAND, -Multi_expand)
-
-# Create EFFECTIVE_YEAR field to indicate the effective year of the closure
-closures_effective_year <- closures_remove_fields %>%
-  mutate(EFFECTIVE_YEAR = year(date_sequence))
-
-# Create EFFECTIVE_MONTH field to indicate the effective month of the closure
-closures_effective_month <- closures_effective_year %>%
-  mutate(EFFECTIVE_MONTH = month(date_sequence))
-
-# Create data frame with only REGION, ZONE_USE, date_sequence, CLOSE_OPEN, EFFECTIVE_YEAR, and EFFECTIVE_MONTH
-select_closures <- closures_effective_month %>%
-  select(REGION, ZONE_USE, date_sequence, CLOSE_OPEN, EFFECTIVE_YEAR, EFFECTIVE_MONTH)
-
-# Fill in missing date_sequence dates
-# Create a data frame with all dates for the specified EFFECTIVE_YEAR
-all_dates <- select_closures %>%
-  group_by(REGION, ZONE_USE, EFFECTIVE_YEAR) %>%
-  summarize(start_date = as.Date(paste(EFFECTIVE_YEAR, "01-01", sep = "-")),
-            end_date = as.Date(paste(EFFECTIVE_YEAR, "12-31", sep = "-"))) %>%
-  rowwise() %>%
-  mutate(date_sequence = list(seq.Date(start_date, end_date, by = "1 day"))) %>%
-  ungroup() %>%
-  select(-start_date, -end_date)
-
-# Expand the date_sequence list into separate rows
-# LOSE ZONES HERE!!!
-all_dates_expand <- all_dates %>%
-  rowwise() %>%
-  mutate(date_sequence = list(unlist(date_sequence))) %>%
-  unnest(date_sequence)
-
-# Merge the expanded data frame with the original select_closures data frame
-merged_dates <- all_dates %>%
-  left_join(select_closures, by = c("REGION", "ZONE_USE", "EFFECTIVE_YEAR", "date_sequence"))
-
-# Remove any duplicated dates
-duplicated_data <- merged_dates %>%
-  group_by(REGION, ZONE_USE, date_sequence) %>%
-  summarize(CLOSE_OPEN = ifelse(all(is.na(CLOSE_OPEN)), "OPEN", first(na.omit(CLOSE_OPEN)))) %>%
-  ungroup()
-
-# Extract EFFECTIVE_YEAR from date_sequence
-effective_year <- duplicated_data %>%
-  mutate(EFFECTIVE_YEAR1 = year(date_sequence))
-
-# Extract EFFECTIVE_MONTH1 from date_sequence
-effective_month <- effective_year %>%
-  mutate(EFFECTIVE_MONTH1 = month(date_sequence))
-
-# Create MONTH_TOTAL field to indicate how many days the fishery was closed for each month
-month_total <- effective_month %>%
-  group_by(REGION, ZONE_USE, EFFECTIVE_YEAR1, EFFECTIVE_MONTH1) %>%
-  summarise(MONTH_TOTAL = sum(CLOSE_OPEN == "CLOSE", na.rm = TRUE))
-
-# Create SEASON field to indicate if the fishery is opened or closed for the EFFECTIVE_MONTH1 of the EFFECTIVE_YEAR1
-# The fishery is considered closed if more than 15 days within a month are closed
-final_closure <- month_total %>%
-  group_by(EFFECTIVE_YEAR1, EFFECTIVE_MONTH1) %>%
-  mutate(SEASON = ifelse(MONTH_TOTAL > 15, "CLOSED", "OPEN")) %>%
-  slice(1) %>%
-  ungroup()

From 110b635b304a2b709b78de65bd6c0331c430b585 Mon Sep 17 00:00:00 2001
From: GaitlynMalone-NOAA
 <115589280+GaitlynMalone-NOAA@users.noreply.github.com>
Date: Wed, 24 Sep 2025 09:48:54 -0400
Subject: [PATCH 2/3] Update notes for closure script and tables script. Update
 file structure

---
 .../Closure_SNAPPER, RED2025Sep22.RDS         | Bin 0 -> 1212 bytes
 .../Closure_SNAPPER, RED22025Sep22.RDS        | Bin 0 -> 2173 bytes
 .../Closure_gmgraytrigger2025Feb05.RDS        | Bin 0 -> 2957 bytes
 .../Closure_hogfishgulf2025May08.RDS          | Bin 0 -> 1762 bytes
 .../Closure_hogfishgulf2025May29.RDS          | Bin 0 -> 1728 bytes
 .../Closure_hogfishgulf22025May12.RDS         | Bin 0 -> 3784 bytes
 ...Closure_hogfishsouthatlantic22025May12.RDS | Bin 0 -> 1864 bytes
 .../Closure_kingmackerelgulf2025May29.RDS     | Bin 0 -> 1728 bytes
 .../Closure_kingmackerelgulf22025May29.RDS    | Bin 0 -> 4701 bytes
 .../Closure_saredsnapper2025Jan27.RDS         | Bin 0 -> 2141 bytes
 .../Closure_outputs/open_by_year2025May08.RDS | Bin 0 -> 1762 bytes
 .../Closures/Fishing_season.R                 | 121 --------------
 .../Closures/Main_closure_prep.R              | 155 ++++++++++--------
 .../Closures/SATL_Red_Porgy_closure_example.R | 104 ------------
 14 files changed, 88 insertions(+), 292 deletions(-)
 create mode 100644 ODM-MH-Analysis_ready/Closures/Closure_outputs/Closure_SNAPPER, RED2025Sep22.RDS
 create mode 100644 ODM-MH-Analysis_ready/Closures/Closure_outputs/Closure_SNAPPER, RED22025Sep22.RDS
 create mode 100644 ODM-MH-Analysis_ready/Closures/Closure_outputs/Closure_gmgraytrigger2025Feb05.RDS
 create mode 100644 ODM-MH-Analysis_ready/Closures/Closure_outputs/Closure_hogfishgulf2025May08.RDS
 create mode 100644 ODM-MH-Analysis_ready/Closures/Closure_outputs/Closure_hogfishgulf2025May29.RDS
 create mode 100644 ODM-MH-Analysis_ready/Closures/Closure_outputs/Closure_hogfishgulf22025May12.RDS
 create mode 100644 ODM-MH-Analysis_ready/Closures/Closure_outputs/Closure_hogfishsouthatlantic22025May12.RDS
 create mode 100644 ODM-MH-Analysis_ready/Closures/Closure_outputs/Closure_kingmackerelgulf2025May29.RDS
 create mode 100644 ODM-MH-Analysis_ready/Closures/Closure_outputs/Closure_kingmackerelgulf22025May29.RDS
 create mode 100644 ODM-MH-Analysis_ready/Closures/Closure_outputs/Closure_saredsnapper2025Jan27.RDS
 create mode 100644 ODM-MH-Analysis_ready/Closures/Closure_outputs/open_by_year2025May08.RDS
 delete mode 100644 ODM-MH-Analysis_ready/Closures/Fishing_season.R
 delete mode 100644 ODM-MH-Analysis_ready/Closures/SATL_Red_Porgy_closure_example.R

diff --git a/ODM-MH-Analysis_ready/Closures/Closure_outputs/Closure_SNAPPER, RED2025Sep22.RDS b/ODM-MH-Analysis_ready/Closures/Closure_outputs/Closure_SNAPPER, RED2025Sep22.RDS
new file mode 100644
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diff --git a/ODM-MH-Analysis_ready/Closures/Fishing_season.R b/ODM-MH-Analysis_ready/Closures/Fishing_season.R
deleted file mode 100644
index 4bf03f3..0000000
--- a/ODM-MH-Analysis_ready/Closures/Fishing_season.R
+++ /dev/null
@@ -1,121 +0,0 @@
-# FISHING SEASON MH PROCESSING 
-### NOT DONE, STILL NEED SOME THOUGHT ###
-
-# Filter for only fishing season management type
-# Include REG_REMOVED == 0 because this helps us in getting an end date for fishing years that end, but we do not want to keep the record that "turns off" the regulation
-mh_fs <- mh_data_log %>%
-  filter(MANAGEMENT_TYPE_USE == 'FISHING SEASON', REG_REMOVED == 0)
-
-# Identify cases that have a split fishing season - yes, we have split fishing season 
-mh_fs_chk1 <- mh_fs %>% ungroup() %>%
-  group_by(FR_CITATION, FMP, REGION, COMMON_NAME_USE, SECTOR_USE, SUBSECTOR_USE, ZONE_USE, MANAGEMENT_STATUS_USE, EFFECTIVE_DATE) %>%
-  summarise(N_seasons = n()) 
-# Join to data
-mh_fs2 <- mh_fs %>% 
-  left_join(mh_fs_chk1, by = c("FR_CITATION", "REGION", "FMP", "EFFECTIVE_DATE", "SECTOR_USE", 
-                               "COMMON_NAME_USE", "ZONE_USE", "SUBSECTOR_USE", "MANAGEMENT_STATUS_USE"))
-# Check date fields
-chk_dates <- mh_fs2 %>%
-  select(REGULATION_ID, FR_CITATION, FMP, REGION, COMMON_NAME_USE, SECTOR_USE, SUBSECTOR_USE, ZONE_USE, MANAGEMENT_STATUS_USE, EFFECTIVE_DATE,
-         INEFFECTIVE_DATE, START_MONTH, START_DAY_USE, START_YEAR, START_DAY_OF_WEEK_USE, 
-         END_MONTH_USE, END_DAY_USE, END_YEAR_USE, END_DAY_OF_WEEK_USE, 
-         START_DATE2, END_DATE2)
-
-# Check other weekly recurring events
-chk_weekly <- mh_data_log %>% filter(MANAGEMENT_STATUS_USE == 'WEEKLY RECURRING')
-# Check REG ID 11548
-chk <- mh_data_log %>% filter(REGULATION_ID == 11548) %>% 
-  select(REGULATION_ID, FR_CITATION, FMP, REGION, COMMON_NAME_USE, SECTOR_USE, SUBSECTOR_USE, ZONE_USE, MANAGEMENT_STATUS_USE, EFFECTIVE_DATE,
-         INEFFECTIVE_DATE, START_MONTH, START_DAY_USE, START_YEAR, START_DAY_OF_WEEK_USE, 
-         END_MONTH_USE, END_DAY_USE, END_YEAR_USE, END_DAY_OF_WEEK_USE, 
-         START_DATE2, END_DATE2, EFFECTIVE_DATE_FY_1, FY_1, EFFECTIVE_DATE_FY_2, FY_2, EFFECTIVE_DATE_FY_3, FY_3)
-
-# Check spiny lobster - had no fishing year because has fishing season?
-# Include fishing season in "CLOSURE" story and process there
-chk <- mh_data_log %>% 
-  filter(COMMON_NAME_USE == 'LOBSTER, CARIBBEAN SPINY', is.na(FY_1),
-         MANAGEMENT_TYPE_USE == 'FISHING SEASON') %>%
-  select(CLUSTER, FR_CITATION, MANAGEMENT_TYPE_USE, MANAGEMENT_STATUS_USE, COMMON_NAME_USE,
-         FMP, REGION, SECTOR_USE, SUBSECTOR_USE, ZONE_USE,
-         EFFECTIVE_DATE, INEFFECTIVE_DATE, START_DATE2, END_DATE2, 
-         START_MONTH, START_DAY_USE, START_DAY_OF_WEEK_USE, START_TIME_USE, START_YEAR,
-         END_MONTH_USE, END_DAY_USE, END_DAY_OF_WEEK_USE, END_TIME_USE, END_YEAR) %>%
-  arrange(REGION, SECTOR_USE, SUBSECTOR_USE, START_DATE2)
-
-# Check red snapper that has 2 fishing seasons
-# Because these are multi-regs (same FR) the logic for start date2 and end date2 is not working
-chk <- mh_data_log %>% 
-  filter(COMMON_NAME_USE == 'SNAPPER, RED', REGION == 'GULF OF MEXICO',
-         MANAGEMENT_TYPE_USE == 'FISHING SEASON') %>%
-  select(CLUSTER, FR_CITATION, MANAGEMENT_TYPE_USE, MANAGEMENT_STATUS_USE, COMMON_NAME_USE,
-         FMP, REGION, SECTOR_USE, SUBSECTOR_USE, ZONE_USE,
-         EFFECTIVE_DATE, INEFFECTIVE_DATE, START_DATE2, END_DATE2, 
-         START_MONTH, START_DAY_USE, START_DAY_OF_WEEK_USE, START_TIME_USE, START_YEAR,
-         END_MONTH_USE, END_DAY_USE, END_DAY_OF_WEEK_USE, END_TIME_USE, END_YEAR,
-         EFFECTIVE_DATE_FY_1, EFFECTIVE_DATE_FY_2, EFFECTIVE_DATE_FY_3,
-         FY_1, FY_2, FY_3) %>%
-  arrange(REGION, SECTOR_USE, SUBSECTOR_USE, START_DATE2)
-
-# Identify number of days within a fishing season 
-mh_fs_ck2 <- mh_fs2 %>% 
-  # Create as date field to subtract (the year is not important here)
-  mutate(START_FS = case_when(!is.na(START_MONTH) & !is.na(START_DAY_USE) & is.na(START_YEAR) ~ as.Date(paste0("2023-", START_MONTH, "-", START_DAY_USE), "%Y-%m-%d"),
-                              !is.na(START_MONTH) & !is.na(START_DAY_USE) & !is.na(START_YEAR) ~ as.Date(paste0(START_YEAR, "-", START_MONTH, "-", START_DAY_USE), "%Y-%m-%d")),
-         END_FS = case_when(!is.na(END_MONTH_USE) & !is.na(END_DAY_USE) & is.na(END_YEAR_USE) ~ as.Date(paste0("2023-", END_MONTH_USE, "-", END_DAY_USE), "%Y-%m-%d"),
-                            !is.na(END_MONTH_USE) & !is.na(END_DAY_USE) & !is.na(END_YEAR_USE) ~ as.Date(paste0(END_YEAR_USE, "-", END_MONTH_USE, "-", END_DAY_USE), "%Y-%m-%d")),
-         DIFF = case_when(END_MONTH_USE >= START_MONTH ~ END_FS - START_FS,
-                          END_MONTH_USE < START_MONTH ~ START_FS - END_FS)) %>%
-  filter(N_seasons == 1)
-
-# Check fishing season for specific areas
-chk <- mh_fs_ck2 %>% group_by(SECTOR_USE, SUBSECTOR_USE, ZONE_USE) %>% summarize(n = n())
-
-# Recode fishing season records as fishing year if 365 days
-fs_to_fy <- mh_fs_ck2 %>% ungroup() %>%
-  filter(abs(DIFF) %in% c(364, 365, 1)) #%>%
-  #select(REGULATION_ID) %>% pull()
-
-# Check for cases where there is a single fishing season and its less than 365 days
-# These are cases where the inverse is then considered a closure
-fs_lt365 <- mh_fs_ck2 %>%
-  filter(abs(DIFF) < 365, !REGULATION_ID %in% fs_to_fy) %>% 
-  select(CLUSTER, REGULATION_ID, FR_CITATION, FR_URL, 
-         REGION, ZONE_USE, COMMON_NAME_USE, SECTOR_USE, SUBSECTOR_USE,
-         START_DATE2, END_DATE2, START_FS, END_FS, DIFF) %>% 
-  distinct()
-
-# Fishing season more than 365 days
-# Only one regulation - Gulf red drum and that is how the FR is worded
-fs_gt365 <- mh_fs_ck2 %>%
-  filter(abs(DIFF) > 365, !REGULATION_ID %in% fs_to_fy) %>% 
-  select(CLUSTER, REGULATION_ID, FR_CITATION, FR_URL, 
-         REGION, ZONE_USE, COMMON_NAME_USE, SECTOR_USE, SUBSECTOR_USE,
-         START_DATE2, END_DATE2, START_FS, END_FS, DIFF) %>% 
-  distinct()
-chk <- mh_data_log %>% filter(REGULATION_ID == 3486)
-
-# Fishing season missing diff days
-# Some just have start fishing season 
-# For the red snapper fishing season, that means it ends at the end of the fishing year
-# Others don't have fishing year and are missing key date fields because they are certain days of the week
-# All of those are rec Caribbean Spiny Lobster in the Gulf and SATL
-fs_nodiff <- mh_fs_ck2 %>%
-  filter(is.na(DIFF)) %>% 
-  select(CLUSTER, REGULATION_ID, FR_CITATION, FR_URL, 
-         REGION, ZONE_USE, COMMON_NAME_USE, SECTOR_USE, SUBSECTOR_USE,
-         EFFECTIVE_DATE, INEFFECTIVE_DATE, START_DATE2, END_DATE2, 
-         START_DAY, START_DAY_USE, START_MONTH, START_TIME_USE, START_YEAR, START_DAY_OF_WEEK_USE, START_FS, 
-         END_DAY, END_DAY_USE, END_MONTH, END_MONTH_USE, END_TIME, END_TIME_USE, END_YEAR, END_YEAR_USE, END_DAY_OF_WEEK_USE, END_FS, DIFF,
-         EFFECTIVE_DATE_FY_1, EFFECTIVE_DATE_FY_2, EFFECTIVE_DATE_FY_3,
-         FY_1, FY_2, FY_3) %>% 
-  distinct()
-
-# Remove these FRs since already added to fishing year
-mh_fs3 <- mh_fs2 %>%
-  filter(!REGULATION_ID %in% fs_to_fy)
-
-# Filter for species of interest
-fs_spp <- mh_fs3 %>%
-  filter(COMMON_NAME_USE == spp, REGION == region)
-
-# Format
diff --git a/ODM-MH-Analysis_ready/Closures/Main_closure_prep.R b/ODM-MH-Analysis_ready/Closures/Main_closure_prep.R
index aaf92ef..03490b4 100644
--- a/ODM-MH-Analysis_ready/Closures/Main_closure_prep.R
+++ b/ODM-MH-Analysis_ready/Closures/Main_closure_prep.R
@@ -1,20 +1,34 @@
-# Process all regulations related to closures
+#### Closures ####
+# Code creates a comprehensive, expanded time series of fishery status (OPEN/CLOSE)
+# by incorporating all closure-related regulations.
+# The first output from this code indicates the days open per year by fishery (commercial/recreational)
+# The second output from this code displays the continuous OPEN/CLOSE periods with start/end dates
 
 # Load packages ####
 librarian::shelf(here, tidyverse, lubridate, dplyr, tidyr, neatRanges, splitstackshape)
 
-# Read in MH Data Log
-mh_data_log <- readRDS(here("ODM-MH-Data_log", "data", "results", "MH_DL_2025Mar28.RDS"))
+# Read in MH Data Log ####
+mh_data_log <- readRDS(here("ODM-MH-Data_log", "data", "results", "MH_DL_2025Sep10.RDS"))
 
-# Select species and region
+# Select species and region of interest for the closure time series ####
 spp <- 'SNAPPER, RED'
-region <- 'GULF OF MEXICO'
+region <- 'SOUTH ATLANTIC'
 
-# Function to expand dates based on management status
+# Function to expand dates based on management status ####
+# Function expands records for a given MANAGEMENT_TYPE_USE into a daily date sequence with
+# the status of the fishery (OPEN/CLOSED) indicated for each day based on the MANAGEMENT_STATUS_USE 
+# (ONCE/SEASONAL/RECURRING) of the applicable regulations
 source(here("ODM-MH-Analysis_ready", "Closures", "func_expand_status.R"))
 
-# filter for species and region (all closure related records)
-# Recode management types to have a value
+# Filter and reclassify closure-related records ####
+# Retain only species and region of interest and management types that impact the status of the fishery
+# Provide management types that impact fishery status with a VALUE of OPEN or CLOSE
+# FISHING SEASON/FISHING YEAR implies that the fishery is OPEN during the defined window
+# PROHIBITED SALE AND PURCHASE 
+#    - If FLAG = YES -> OPEN (sale is allowed)
+#    - If FLAG = NO -> CLOSE (sale is not allowed)
+# PROHIBITED SPECIES implies the fishery is closed as species cannot be retained
+# CLOSURE VALUE already indicates fishery status
 mh_spp_closure <- mh_data_log %>%
   filter(COMMON_NAME_USE == spp,
          REGION == region,
@@ -28,39 +42,46 @@ mh_spp_closure <- mh_data_log %>%
                            TRUE ~ VALUE)) %>%
   arrange(SECTOR_USE, START_DATE2)
 
+# Check to see which MANAGEMENT_TYPE_USE are present after filtering
 unique(mh_spp_closure$MANAGEMENT_TYPE_USE)
 
-# Fishing Year
-chk <- filter(mh_spp_closure, MANAGEMENT_TYPE_USE == "FISHING YEAR")
+# Date expansion by MANAGEMENT_TYPE_USE ####
+# Each section expands the raw rules for the applicable MANAGEMENT_TYPE_USE into a 
+# series by date with the associated status (OPEN/CLOSE) and START/END TIME
+
+# Fishing Year expansion
+unique(select(filter(mh_spp_closure, MANAGEMENT_TYPE_USE == "FISHING YEAR"), MANAGEMENT_STATUS_USE))
 spp_year <- expand_status(mh_spp_closure, "FISHING YEAR")
 
-# Fishing Season - still needs some work if not entered consistently
-chk <- filter(mh_spp_closure, MANAGEMENT_TYPE_USE == "FISHING SEASON")
+# Fishing Season expansion 
+# Portion may require some adjustment if fishing year has not been documented consistently in the raw data
+unique(select(filter(mh_spp_closure, MANAGEMENT_TYPE_USE == "FISHING SEASON"), MANAGEMENT_STATUS_USE))
 spp_season <- expand_status(mh_spp_closure, "FISHING SEASON")
 
-# Closure
+# Closure expansion
 unique(select(filter(mh_spp_closure, MANAGEMENT_TYPE_USE == "CLOSURE"), MANAGEMENT_STATUS_USE))
 spp_closures <- expand_status(mh_spp_closure, "CLOSURE")
 
-# Prohibited sale and purchase
+# Prohibited Sale and Purchase expansion
 unique(select(filter(mh_spp_closure, MANAGEMENT_TYPE_USE == "PROHIBITED SALE AND PURCHASE"), MANAGEMENT_STATUS_USE))
 spp_prohibited_sale <- expand_status(mh_spp_closure, "PROHIBITED SALE AND PURCHASE")
 
-# Prohibited species
+# Prohibited Species expansion
 unique(select(filter(mh_spp_closure, MANAGEMENT_TYPE_USE == "PROHIBITED SPECIES"), MANAGEMENT_STATUS_USE))
 spp_prohibited_spp <- expand_status(mh_spp_closure, "PROHIBITED SPECIES")
 
-# Combine all management types that refer to closures
-# For now just comment out mtypes that do not apply
+# Join all expanded series into one time series ####
+# For days with multiple statuses based on multiple regulations, select the most recent FR Citation
 spp_closure_story <- spp_year %>%
   rename(FR_CITATION_year = "FR_CITATION",
          TIME_year = "TIME",
          VALUE_year = "VALUE") %>%
   select(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, date_sequence, FR_CITATION_year, TIME_year, VALUE_year) %>%
+  # Comment out MANAGEMENT_TYPE_USE that do not apply to your filter criteria
   full_join(spp_season %>%
-              rename(FR_CITATION_season = "FR_CITATION",
+            rename(FR_CITATION_season = "FR_CITATION",
                      TIME_season = "TIME",
-                     VALUE_season = "VALUE") %>%
+                    VALUE_season = "VALUE") %>%
               select(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, date_sequence, FR_CITATION_season, TIME_season, VALUE_season),
             by = join_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE,  date_sequence)) %>%
   full_join(spp_closures %>%
@@ -69,57 +90,67 @@ spp_closure_story <- spp_year %>%
                      VALUE_close = "VALUE") %>%
               select(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, date_sequence, FR_CITATION_close, TIME_close, VALUE_close),
             by = join_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE,  date_sequence)) %>%
- # full_join(spp_prohibited_sale %>%
-   #          rename(FR_CITATION_sale = "FR_CITATION",
-    #                TIME_sale = "TIME", 
-    #                 VALUE_sale = "VALUE") %>%
-     #         select(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, date_sequence, FR_CITATION_sale, VALUE_sale),
-      #      by = join_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE,  date_sequence)) %>%
- # full_join(spp_prohibited_spp %>%
-  #            rename(FR_CITATION_spp = "FR_CITATION",
-  #                   TIME_spp = "TIME",
-   #                  VALUE_spp = "VALUE") %>%
-    #          select(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, date_sequence, FR_CITATION_spp, VALUE_spp),
-     #         by = join_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, date_sequence)) %>%
+  full_join(spp_prohibited_sale %>%
+             rename(FR_CITATION_sale = "FR_CITATION",
+                    TIME_sale = "TIME", 
+                     VALUE_sale = "VALUE") %>%
+              select(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, date_sequence, FR_CITATION_sale, VALUE_sale),
+            by = join_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE,  date_sequence)) %>%
+  full_join(spp_prohibited_spp %>%
+              rename(FR_CITATION_spp = "FR_CITATION",
+                     TIME_spp = "TIME",
+                     VALUE_spp = "VALUE") %>%
+              select(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, date_sequence, FR_CITATION_spp, VALUE_spp),
+              by = join_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, date_sequence)) %>%
   arrange(SECTOR_USE, SUBSECTOR_USE, ZONE_USE, date_sequence) %>%
-  # Select the most recent FR CITATION
+  # Select the most recent FR CITATION and retain its TIME/VALUE
   mutate(FR_CITATION = pmax(FR_CITATION_year, 
                             FR_CITATION_season, 
                             FR_CITATION_close, 
-                           # FR_CITATION_sale, 
-                            #FR_CITATION_spp,
+                            FR_CITATION_sale, 
+                            FR_CITATION_spp,
                             na.rm = T),
          TIME = case_when(FR_CITATION == FR_CITATION_close ~ TIME_close,
-                          #FR_CITATION == FR_CITATION_sale ~ TIME_sale,
-                          #FR_CITATION == FR_CITATION_spp ~ TIME_spp,
-                          FR_CITATION == FR_CITATION_season ~ TIME_season,
+                          FR_CITATION == FR_CITATION_sale ~ TIME_sale,
+                          FR_CITATION == FR_CITATION_spp ~ TIME_spp,
+                         FR_CITATION == FR_CITATION_season ~ TIME_season,
                           FR_CITATION == FR_CITATION_year ~ TIME_year)) %>%
-  # Select the fishery status (open/closed) that applies to the most recent FR
   mutate(VALUE = case_when(FR_CITATION == FR_CITATION_close ~ VALUE_close,
-                           #FR_CITATION == FR_CITATION_sale ~ VALUE_sale,
-                           #FR_CITATION == FR_CITATION_spp ~ VALUE_spp,
+                           FR_CITATION == FR_CITATION_sale ~ VALUE_sale,
+                           FR_CITATION == FR_CITATION_spp ~ VALUE_spp,
                            FR_CITATION == FR_CITATION_season ~ VALUE_season,
                            FR_CITATION == FR_CITATION_year ~ VALUE_year)) %>%
-  select(-FR_CITATION_year, -TIME_year, -VALUE_year, -FR_CITATION_close, -TIME_close, -VALUE_close, -FR_CITATION_season, -TIME_season, -VALUE_season) %>%
-  # Create day_calc to indicate cases where the start/end time is midday 
-  # midday start/end times should be counted toward both open and closed periods
+  # Remove management type-specific columns now that a single FR/TIME/VALUE has been selected for each day
+  select(-FR_CITATION_year, -TIME_year, -VALUE_year, -FR_CITATION_close, -TIME_close, -VALUE_close, -FR_CITATION_sale, -VALUE_sale, -TIME_sale, -FR_CITATION_season, -VALUE_season, -TIME_season) %>%
+  # How to handle half days
+  # Some FR notices take effect midday or later. To correctly count days for annual tallies, treat noon/6pm
+  # TIMEs as half-days) (0.5)
+  # This ensures that a midday start/end time contributes to both OPEN and CLOSED period calculations
   mutate(day_calc = case_when(TIME == "06:00:00 PM" ~ 0.5,
                               TIME == "12:00:00 PM" ~ 0.5,
                               TRUE ~ 1))
 
-# Summarize the open and closed periods for each year
+# Summarize the open and closed periods for each year ####
+# Fill in any mission VALUE as OPEN (assumes that any window without an applicable rule is open)
 summ_spp_closures <- spp_closure_story %>%
   mutate(YEAR = year(date_sequence),
          VALUE = as.character(VALUE),
          VALUE = if_else(is.na(VALUE), "OPEN", VALUE)) %>%
   filter(VALUE %in% c("OPEN", "CLOSE"))
 
+# Create half-day complements ####
+# When a day is counted as 0.5 under one status (OPEN/CLOSED), add a complementary 0.5 day to 
+# the opposite status so that the calendar day sums to 1 acorss OPEN/CLOSE
 summ_spp_closures2 <- summ_spp_closures %>%
   filter(day_calc == 0.5) %>%
   mutate(VALUE = if_else(VALUE == "OPEN", "CLOSE", "OPEN"))
 
+# Combine whole-day and half-day rows for final daily time series
 spp_closure_combined <- bind_rows(summ_spp_closures, summ_spp_closures2)
 
+# Collapse individual days into continuous periods within each year ####
+# Group consecutive days with the same VALUE into periods and compute total days within the period (ndays)
+# and list the start/end as the first/last days within the period
 spp_close_sum <- spp_closure_combined %>%
   group_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, YEAR) %>% 
   arrange(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, date_sequence, TIME) %>%
@@ -134,25 +165,10 @@ spp_close_sum <- spp_closure_combined %>%
   arrange(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, YEAR, start)
 
 
-# Summarize the open and closed periods for each year
-#summ_spp_closures <- spp_closure_story %>%
- # mutate(YEAR = year(date_sequence)) %>%
-  #group_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, YEAR) %>% 
-  #arrange(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, date_sequence, TIME) %>%
-  #mutate(change = VALUE != lag(VALUE, default = first(VALUE)) |
-   #        YEAR != lag(YEAR, default = first(YEAR))) %>%
-  #mutate(group = cumsum(change)) %>%
-  #group_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, YEAR, VALUE, group) %>%
-  #summarize(ndays = sum(day_calc, na.rm = TRUE),
-   #         start = min(date_sequence),
-    #        end = max(date_sequence),
-     #       .groups = 'drop') %>%
-  #mutate(VALUE = case_when(is.na(VALUE) ~ "OPEN",
-   #                        TRUE ~ VALUE)) %>%
-  #arrange(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, YEAR)
-
-# Identify full range of years per group
-all_years_by_group <- spp_close %>%
+# Identify full range of years per group ####
+# Determine the min/max year of regulations documented and expand to all years between so that even years with zero regulations 
+# are accounted for in the expansion
+all_years_by_group <- spp_close_sum %>%
   distinct(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, YEAR, VALUE) %>%
   group_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE) %>%
   summarize(min_year = min(YEAR), max_year = max(YEAR), .groups = "drop") %>%
@@ -161,18 +177,23 @@ all_years_by_group <- spp_close %>%
   unnest(YEAR) %>%
   select(-min_year, -max_year)
 
-# Summarize the number of days open per year
-summ_days_open <- spp_close %>%
+# Summarize the number of days open per year ####
+# Collapse to annual totals of OPEN days per fishery grouping
+summ_days_open <- spp_close_sum %>%
   filter(VALUE == "OPEN") %>%
   group_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, YEAR) %>%
   summarize(Days_Open = sum(ndays), .groups = "drop") %>%
   arrange(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, YEAR)
 
+# Ensure every year in the full span is represented
+# Fill in missing years as zero
 open_by_year <- all_years_by_group %>%
   left_join(summ_days_open, by = c("FMP", "COMMON_NAME_USE", "REGION", "ZONE_USE", "SECTOR_USE", "SUBSECTOR_USE", "YEAR")) %>%
   mutate(Days_Open = replace_na(Days_Open, 0)) %>%
   arrange(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, YEAR)
 
-# Save closure output as RDS file
-saveRDS(open_by_year, here("ODM-MH-Analysis_ready", "Closures", "Closure_outputs", paste0('open_by_year', format(Sys.Date(), "%Y%b%d"), '.RDS')))
+# Save annual open days as RDS file
+saveRDS(open_by_year, here("ODM-MH-Analysis_ready", "Closures", "Closure_outputs", paste0('Open_', spp, format(Sys.Date(), "%Y%b%d"), '.RDS')))
 
+# Save closure periods output as RDS file
+saveRDS(spp_close_sum, here("ODM-MH-Analysis_ready", "Closures", "Closure_outputs", paste0('Close_', spp, format(Sys.Date(), "%Y%b%d"), '.RDS')))
diff --git a/ODM-MH-Analysis_ready/Closures/SATL_Red_Porgy_closure_example.R b/ODM-MH-Analysis_ready/Closures/SATL_Red_Porgy_closure_example.R
deleted file mode 100644
index 2d7ec99..0000000
--- a/ODM-MH-Analysis_ready/Closures/SATL_Red_Porgy_closure_example.R
+++ /dev/null
@@ -1,104 +0,0 @@
-# Process all regulations related to closures
-
-# Load packages ####
-#install.packages("librarian")
-librarian::shelf(here, tidyverse, lubridate, openxlsx, neatRanges, splitstackshape)
-
-# Read in MH Data Log
-mh_data_log <- readRDS(here("ODM-MH-Data_log", "data", "results", "MH_DL_2024Feb08.RDS"))
-
-# Select species and region
-spp <- 'PORGY, RED'
-region <- 'SOUTH ATLANTIC'
-
-# Function to expand dates based on management status
-source(here("ODM-MH-Analysis_ready", "func_expand_status.R"))
-
-# filter for species and region (all closure related records)
-# Recode management types to have a value
-mh_spp_closure <- mh_data_log %>%
-  filter(COMMON_NAME_USE == spp & REGION == region &
-         (MANAGEMENT_TYPE_USE %in% c('CLOSURE', 'FISHING SEASON', 'FISHING YEAR', 'PROHIBITED SPECIES') |
-            # Flag == NO means fishery wide prohibited sale and purchase (ie closure)
-            MANAGEMENT_TYPE_USE == 'PROHIBITED SALE AND PURCHASE' & FLAG == 'NO')) %>%
-  mutate(VALUE = case_when(MANAGEMENT_TYPE_USE %in% c('FISHING SEASON', 'FISHING YEAR') ~ 'OPEN',
-                           MANAGEMENT_TYPE_USE %in% c('PROHIBITED SALE AND PURCHASE', 'PROHIBITED SPECIES') ~ 'CLOSE',
-                           TRUE ~ VALUE)) %>%
-  arrange(SECTOR_USE, START_DATE2)
-
-unique(mh_spp_closure$MANAGEMENT_TYPE_USE)
-
-# Fishing Year
-unique(select(filter(mh_spp_closure, MANAGEMENT_TYPE_USE == "FISHING YEAR"), MANAGEMENT_STATUS_USE))
-spp_year <- expand_status(mh_spp_closure, "FISHING YEAR")
-
-# Fishing Season - still needs some work if not entered consistently
-unique(select(filter(mh_spp_closure, MANAGEMENT_TYPE_USE == "FISHING SEASON"), MANAGEMENT_STATUS_USE))
-spp_season <- expand_status(mh_spp_closure, "FISHING SEASON")
-
-# Closure
-unique(select(filter(mh_spp_closure, MANAGEMENT_TYPE_USE == "CLOSURE"), MANAGEMENT_STATUS_USE))
-spp_closures <- expand_status(mh_spp_closure, "CLOSURE")
-
-# Prohibited sale and purchase
-unique(select(filter(mh_spp_closure, MANAGEMENT_TYPE_USE == "PROHIBITED SALE AND PURCHASE"), MANAGEMENT_STATUS_USE))
-spp_prohibited_sale <- expand_status(mh_spp_closure, "PROHIBITED SALE AND PURCHASE")
-
-# Prohibited species
-unique(select(filter(mh_spp_closure, MANAGEMENT_TYPE_USE == "PROHIBITED SPECIES"), MANAGEMENT_STATUS_USE))
-spp_prohibited_spp <- expand_status(mh_spp_closure, "PROHIBITED SPECIES")
-
-# Combine all management types that refer to closures
-spp_closure_story <- spp_year %>%
-  rename(FR_CITATION_year = "FR_CITATION",
-         VALUE_year = "VALUE") %>%
-  select(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, date_sequence, FR_CITATION_year, VALUE_year) %>%
-  full_join(spp_season %>%
-              rename(FR_CITATION_season = "FR_CITATION",
-                     VALUE_season = "VALUE") %>%
-              select(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, date_sequence, FR_CITATION_season, VALUE_season),
-            by = join_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE,  date_sequence)) %>%
-  full_join(spp_closures %>%
-              rename(FR_CITATION_close = "FR_CITATION",
-                     VALUE_close = "VALUE") %>%
-              select(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, date_sequence, FR_CITATION_close, VALUE_close),
-            by = join_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE,  date_sequence)) %>%
-  full_join(spp_prohibited_sale %>%
-             rename(FR_CITATION_sale = "FR_CITATION",
-                     VALUE_sale = "VALUE") %>%
-              select(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, date_sequence, FR_CITATION_sale, VALUE_sale),
-            by = join_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE,  date_sequence)) %>%
-  full_join(spp_prohibited_spp %>%
-              rename(FR_CITATION_spp = "FR_CITATION",
-                     VALUE_spp = "VALUE") %>%
-              select(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, date_sequence, FR_CITATION_spp, VALUE_spp),
-            by = join_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, date_sequence)) %>%
-  arrange(SECTOR_USE, SUBSECTOR_USE, ZONE_USE, date_sequence) %>%
-  # Select the most recent FR CITATION
-  mutate(FR_CITATION = pmax(FR_CITATION_year, 
-                            FR_CITATION_season, 
-                            FR_CITATION_close, 
-                            FR_CITATION_sale, 
-                            FR_CITATION_spp,
-                            na.rm = T)) %>%
-  # Select the fishery status (open/closed) that applies to the most recent FR
-  mutate(VALUE = case_when(FR_CITATION == FR_CITATION_close ~ VALUE_close,
-                           FR_CITATION == FR_CITATION_sale ~ VALUE_sale,
-                           FR_CITATION == FR_CITATION_spp ~ VALUE_spp,
-                           FR_CITATION == FR_CITATION_season ~ VALUE_season,
-                           FR_CITATION == FR_CITATION_year ~ VALUE_year))
-
-summ_spp_closures <- spp_closure_story %>%
-  mutate(YEAR = year(date_sequence)) %>%
-  group_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, YEAR, FR_CITATION, VALUE) %>% 
-  summarize(ndays = n(),
-            start = min(date_sequence),
-            end = max(date_sequence)) %>%
-  arrange(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, YEAR, start) %>%
-  # Add back FR_URL for reference
-  left_join(distinct(select(filter(ungroup(mh_data_log), FMP == 'SNAPPER-GROUPER FISHERY OF THE SOUTH ATLANTIC REGION'), FR_CITATION, FR_URL)),
-            by = join_by(FR_CITATION))
-
-# Save as Excel to compare to SEDAR MH file
-write.xlsx(summ_spp_closures, "C:/Users/sarina.atkinson/Documents/Data/MH_closures/SA_RP_closures_08Feb24.xlsx")
-

From e961d40798cfed4dadd00b330b9304cdb7a40d9e Mon Sep 17 00:00:00 2001
From: GaitlynMalone-NOAA
 <115589280+GaitlynMalone-NOAA@users.noreply.github.com>
Date: Wed, 24 Sep 2025 09:49:20 -0400
Subject: [PATCH 3/3] Move previous closures script

---
 .../Closure_tests/Closure_Reopening.R         | 314 ++++++++++++++++++
 .../Closures/Closure_tests/Fishing_season.R   | 121 +++++++
 .../SATL_Red_Porgy_closure_example.R          | 104 ++++++
 3 files changed, 539 insertions(+)
 create mode 100644 ODM-MH-Analysis_ready/Closures/Closure_tests/Closure_Reopening.R
 create mode 100644 ODM-MH-Analysis_ready/Closures/Closure_tests/Fishing_season.R
 create mode 100644 ODM-MH-Analysis_ready/Closures/Closure_tests/SATL_Red_Porgy_closure_example.R

diff --git a/ODM-MH-Analysis_ready/Closures/Closure_tests/Closure_Reopening.R b/ODM-MH-Analysis_ready/Closures/Closure_tests/Closure_Reopening.R
new file mode 100644
index 0000000..a8e9579
--- /dev/null
+++ b/ODM-MH-Analysis_ready/Closures/Closure_tests/Closure_Reopening.R
@@ -0,0 +1,314 @@
+# MH Closure processing
+
+#Load packages ####
+librarian::shelf(here, tidyverse, lubridate, dplyr, tidyr, neatRanges, splitstackshape)
+
+# Read in MH Data log ####
+ mh_data_log <- readRDS(here("ODM-MH-Data_log", "data", "results", "MH_DL_2024Jul12.RDS"))
+# 
+# # Select species and region ####
+ spp <- 'SNAPPER, RED'
+ region <- 'GULF OF MEXICO'
+
+# Seasonal Closures ####
+# Filter dataset to seasonal closure regulations for the specified species and region
+Seasonal_closures <- mh_data_log %>%
+  filter(MANAGEMENT_TYPE_USE == 'CLOSURE', MANAGEMENT_STATUS_USE == 'SEASONAL', NEVER_IMPLEMENTED == 0, REG_REMOVED == 0)
+
+# Create date_sequence to expand dates between EFFECTIVE_DATE and END_DATE2 
+expand_seasonal <- Seasonal_closures %>%
+  filter(EFFECTIVE_DATE <= END_DATE2) %>%
+  mutate(date_sequence = map2(EFFECTIVE_DATE, END_DATE2, seq, by = "days")) %>%
+  unnest(date_sequence)
+
+# Add START_YEAR_expand and END_YEAR_expand to extract the year from the date_sequence field
+expand_seasonal_year <- expand_seasonal %>%
+  rowwise() %>%
+  mutate(START_YEAR_expand = year(date_sequence),
+         END_YEAR_expand = year(date_sequence))
+
+# Create START_DATE_EXPAND and END_DATE_EXPAND using start/end day and month information and the expanded year
+expand_seasonal_date <- expand_seasonal_year %>%
+  mutate(START_DATE_EXPAND = as.Date(paste(START_YEAR_expand, START_MONTH, START_DAY_USE, sep = "-")),
+         END_DATE_EXPAND = as.Date(paste(END_YEAR_expand, END_MONTH_USE, END_DAY_USE, sep = "-")))
+
+# Remove records where date_sequence is prior to the START_DATE_EXPAND and date_sequence is after the END_DATE_EXPAND
+remove_seasonal_date <- expand_seasonal_date %>%
+  filter(date_sequence >= START_DATE_EXPAND,
+         END_DATE_EXPAND >= date_sequence)
+
+# Create CLOSE_OPEN field to indicate that expanded records are related to Closures or Openings
+add_closure_value_seasonal <- remove_seasonal_date %>%
+  mutate(CLOSE_OPEN = VALUE)
+
+# Monthly Closures ####
+monthly_closures <-  mh_spp_closure %>%
+  filter(MANAGEMENT_TYPE_USE == 'CLOSURE', MANAGEMENT_STATUS_USE == 'MONTHLY RECURRING', NEVER_IMPLEMENTED == 0, REG_REMOVED == 0)
+
+# Expand dates between EFFECTIVE_DATE and END_DATE2
+expand_monthly <- monthly_closures %>%
+  mutate(date_sequence = map2(EFFECTIVE_DATE, END_DATE2, seq, by = "days")) %>%
+  unnest(date_sequence)
+
+# Add START_YEAR_expand and END_YEAR_expand to extract the year from the date_sequence field
+# Add START_MONTH_expand and END_MONTH_expand to extract the month from the date_sequence field
+# Takes 20 mins to run!!!!
+start <- Sys.time()
+expand_monthly_year <- expand_monthly %>%
+  #rowwise() %>%
+  mutate(START_YEAR_expand = year(date_sequence),
+         END_YEAR_expand = year(date_sequence),
+         # case where the monthly recurring goes from the 15th to the 1st of the following month
+         START_MONTH_expand = case_when(END_DAY_USE < START_DAY_USE & day(date_sequence) < START_DAY_USE & month(date_sequence) != 1 ~ month(date_sequence) - 1,
+                                        END_DAY_USE < START_DAY_USE & day(date_sequence) < START_DAY_USE & month(date_sequence) == 1 ~ 12,
+                                        TRUE ~ month(date_sequence)),
+         END_MONTH_expand = case_when(END_DAY_USE < START_DAY_USE & day(date_sequence) < START_DAY_USE & month(date_sequence) != 1 ~ month(date_sequence),
+                                      END_DAY_USE < START_DAY_USE & day(date_sequence) >= START_DAY_USE & month(date_sequence) + 1 <= 12 ~ month(date_sequence) + 1,
+                                      END_DAY_USE < START_DAY_USE & day(date_sequence) >= START_DAY_USE & month(date_sequence) + 1 == 13 ~ 1,
+                                      TRUE ~ month(date_sequence))) %>%
+  # only include months within the start/end month range
+  # when monthly recurring, the start/end month range refers to the first and last month where the recurring closure applies
+  # i.e. start month = 2 and end month = 11
+      # mean recurring closures start in Feb and end in Nov
+      # can't use END_MONTH_expand in filter for cases when the recurring closures spans 2 months (i.e. 15th -1st)
+  filter(START_MONTH_expand >= START_MONTH &
+         START_MONTH_expand <= END_MONTH)
+Sys.time()-start
+
+# Create Expand_days to indicate the day of the week for the closure/reopening
+expand_monthly_date <- expand_monthly_year %>%
+  mutate(START_DATE_EXPAND = as.Date(paste(START_YEAR_expand, START_MONTH_expand, START_DAY_USE, sep = "-")),
+         END_DATE_EXPAND = as.Date(paste(END_YEAR_expand, END_MONTH_expand, END_DAY_USE, sep = "-")))
+
+# Remove records where date_sequence is prior to the START_DATE_EXPAND and date_sequence is after the END_DATE_EXPAND
+remove_monthly_date <- expand_monthly_date %>%
+  filter(date_sequence >= START_DATE_EXPAND,
+         date_sequence <= END_DATE_EXPAND)
+
+# Create CLOSE_OPEN field to indicate that expanded records are related to Closures or Openings
+add_closure_value_monthly <- remove_monthly_date %>%
+  mutate(CLOSE_OPEN = VALUE)
+
+# Weekly Closures ####
+# Filter dataset to Weekly Recurring regulations
+Weekly_closures <-  mh_spp_closure %>%
+  filter(MANAGEMENT_TYPE_USE == 'CLOSURE', MANAGEMENT_STATUS_USE == 'WEEKLY RECURRING', NEVER_IMPLEMENTED == 0, REG_REMOVED == 0)
+
+# Expand dates between EFFECTIVE_DATE and END_DATE2
+expand_weekly <- Weekly_closures %>%
+  mutate(date_sequence = map2(EFFECTIVE_DATE, END_DATE2, seq, by = "days")) %>%
+  unnest(date_sequence)
+
+# Add START_YEAR_expand and END_YEAR_expand to extract the year from the date_sequence field
+expand_weekly_year <- expand_weekly %>%
+  #rowwise() %>%
+  mutate(START_YEAR_expand = year(date_sequence),
+         END_YEAR_expand = year(date_sequence))
+
+# Create Expand_day_of_week to indicate the day of the week for the closure/reopening
+expand_weekly_day_of_week <- expand_weekly_year %>%
+  mutate(Expand_day_of_week = wday(date_sequence, label = TRUE))
+
+# Create START_DAY_OF_WEEK_EXPAND and END_DAY_OF_WEEK_EXPAND to format the days of the week to align with Expand_day_of_week
+expand_weekly_day_format <- expand_weekly_day_of_week %>%
+  mutate(START_DAY_OF_WEEK_EXPAND = case_when(
+    START_DAY_OF_WEEK_USE == "MONDAY" ~ "Mon",
+    START_DAY_OF_WEEK_USE == "TUESDAY" ~ "Tue",
+    START_DAY_OF_WEEK_USE == "WEDNESDAY" ~ "Wed",
+    START_DAY_OF_WEEK_USE == "THURSDAY" ~ "Thu",
+    START_DAY_OF_WEEK_USE == "FRIDAY" ~ "Fri",
+    START_DAY_OF_WEEK_USE == "SATURDAY" ~ "Sat",
+    START_DAY_OF_WEEK_USE == "SUNDAY" ~ "Sun",
+    TRUE ~ START_DAY_OF_WEEK_USE),
+    END_DAY_OF_WEEK_EXPAND = case_when(
+      END_DAY_OF_WEEK_USE == "MONDAY" ~ "Mon",
+      END_DAY_OF_WEEK_USE == "TUESDAY" ~ "Tue",
+      END_DAY_OF_WEEK_USE == "WEDNESDAY" ~ "Wed",
+      END_DAY_OF_WEEK_USE == "THURSDAY" ~ "Thu",
+      END_DAY_OF_WEEK_USE == "FRIDAY" ~ "Fri",
+      END_DAY_OF_WEEK_USE == "SATURDAY" ~ "Sat",
+      END_DAY_OF_WEEK_USE == "SUNDAY" ~ "Sun",
+      TRUE ~ END_DAY_OF_WEEK_USE))
+
+# Keep any records where Expand_day_of_week is between START_DAY_OF_WEEK_EXPAND and END_DAY_OF_WEEK_EXPAND
+expand_weekly_day_keep <- expand_weekly_day_format %>%
+  filter(Expand_day_of_week >= START_DAY_OF_WEEK_EXPAND |
+           Expand_day_of_week <= END_DAY_OF_WEEK_EXPAND)
+
+# Create CLOSE_OPEN field to indicate that expanded records are related to Closures or Openings
+add_closure_value_weekly <- expand_weekly_day_keep %>%
+  mutate(CLOSE_OPEN = VALUE)
+
+# One Time Closures ####
+# Filter dataset to One Time regulations
+One_closures <- mh_spp_closure %>%
+  filter(MANAGEMENT_TYPE_USE == 'CLOSURE', MANAGEMENT_STATUS_USE == 'ONCE', NEVER_IMPLEMENTED == 0, REG_REMOVED == 0)
+
+# Expand dates between EFFECTIVE_DATE and END_DATE2
+expand_one <- One_closures %>%
+  filter(END_DATE2 >= START_DATE2) %>%
+  mutate(date_sequence = map2(START_DATE2, END_DATE2, seq, by = "1 day")) %>%
+  unnest(date_sequence)
+
+# Create CLOSE_OPEN field to indicate that expanded records are related to Closures or Openings
+add_closure_value_one <- expand_one %>%
+  mutate(CLOSE_OPEN = VALUE)
+
+# Join in Wide Form
+# First clean up duplicates
+# Then limit variables and rename
+# Then create final OPEN/CLOSED field based on all closure events
+# Takes an 1.5 hours to run if use rowwise!!!
+# Instead remove rowwise and pmax for faster runtime
+Sys.time()
+closure_all <- add_closure_value_one %>%
+  select(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, MANAGEMENT_TYPE_USE, date_sequence, 
+         CLUSTER, REGULATION_ID, FR_CITATION, CLOSE_OPEN) %>%
+  arrange(date_sequence, desc(FR_CITATION)) %>%
+  group_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, MANAGEMENT_TYPE_USE, date_sequence) %>%
+  # Retain only most recent FR if there are 2 records with the same date_sequence
+  slice(1) %>%
+  rename(CLUSTER_one = "CLUSTER",
+         REGULATION_ID_one = "REGULATION_ID",
+         FR_CITATION_one = "FR_CITATION",
+         CLOSE_OPEN_one = "CLOSE_OPEN") %>%
+  full_join(add_closure_value_weekly %>%
+              select(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, MANAGEMENT_TYPE_USE, date_sequence,  
+                     CLUSTER, REGULATION_ID, FR_CITATION, CLOSE_OPEN) %>%
+              arrange(date_sequence, desc(FR_CITATION)) %>%
+              group_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, MANAGEMENT_TYPE_USE, date_sequence) %>%
+              # Retain only most recent FR if there are 2 records with the same date_sequence
+              slice(1) %>%
+              rename(CLUSTER_weekly = "CLUSTER",
+                     REGULATION_ID_weekly = "REGULATION_ID",
+                     FR_CITATION_weekly = "FR_CITATION",
+                     CLOSE_OPEN_weekly = "CLOSE_OPEN"),
+            by = join_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, MANAGEMENT_TYPE_USE, date_sequence)) %>%
+  full_join(add_closure_value_seasonal %>%
+              select(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, MANAGEMENT_TYPE_USE, date_sequence,  
+                     CLUSTER, REGULATION_ID, FR_CITATION, CLOSE_OPEN) %>%
+              arrange(date_sequence, desc(FR_CITATION)) %>%
+              group_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, MANAGEMENT_TYPE_USE, date_sequence) %>%
+              # Retain only most recent FR if there are 2 records with the same date_sequence
+              slice(1) %>%
+              rename(CLUSTER_seasonal = "CLUSTER",
+                     REGULATION_ID_seasonal = "REGULATION_ID",
+                     FR_CITATION_seasonal = "FR_CITATION",
+                     CLOSE_OPEN_seasonal = "CLOSE_OPEN"),
+            by = join_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, MANAGEMENT_TYPE_USE, date_sequence)) %>%
+  full_join(add_closure_value_monthly %>%
+              select(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, MANAGEMENT_TYPE_USE, date_sequence,  
+                     CLUSTER, REGULATION_ID, FR_CITATION, CLOSE_OPEN) %>%
+              arrange(date_sequence, desc(FR_CITATION)) %>%
+              group_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, MANAGEMENT_TYPE_USE, date_sequence) %>%
+              # Retain only most recent FR if there are 2 records with the same date_sequence
+              slice(1) %>%
+              rename(CLUSTER_monthly = "CLUSTER",
+                     REGULATION_ID_monthly = "REGULATION_ID",
+                     FR_CITATION_monthly = "FR_CITATION",
+                     CLOSE_OPEN_monthly = "CLOSE_OPEN"),
+            by = join_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, MANAGEMENT_TYPE_USE,  date_sequence)) %>%
+  # create final OPEN_CLOSE field 
+  ungroup() %>% 
+  #rowwise() %>%
+  mutate(final_FR = pmax(FR_CITATION_one, FR_CITATION_seasonal, FR_CITATION_weekly, FR_CITATION_monthly, na.rm = T)) %>%
+  mutate(final_CLOSE_OPEN = case_when(final_FR == FR_CITATION_one ~ CLOSE_OPEN_one,
+                                      final_FR == FR_CITATION_weekly ~ CLOSE_OPEN_weekly,
+                                      final_FR == FR_CITATION_seasonal ~ CLOSE_OPEN_seasonal,
+                                      final_FR == FR_CITATION_monthly ~ CLOSE_OPEN_monthly))
+Sys.time()
+
+# Chck
+chk <- closure_all %>% 
+  mutate(YEAR = (year(date_sequence))) %>%
+  group_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, YEAR, final_FR, final_CLOSE_OPEN) %>% 
+  summarize(ndays = n())
+
+#########################################################
+# Join in Long Form
+# Join Closure Types Together ####
+# Combine closure data frames
+Combined_closures <- bind_rows(add_closure_value_seasonal, add_closure_value_monthly, add_closure_value_one)
+
+# Sort the data frame by date_sequence
+sort_closures <- Combined_closures %>%
+  arrange(date_sequence)
+
+# Create Multi_expand to indicate whether records have the same date_sequence 
+sort_closures_multi <- sort_closures %>%
+  group_by(date_sequence) %>%
+  mutate(Multi_expand = ifelse(n() > 1, 1, 0)) %>%
+  ungroup()
+
+# Keep information from newest FR 
+closures_remove_multi <- sort_closures_multi %>%
+  arrange(date_sequence, desc(FR_CITATION)) %>%
+  group_by(ZONE_USE, date_sequence) %>%
+  slice(1) %>%
+  ungroup()
+
+# Remove extra processing fields from dataset
+closures_remove_fields <- closures_remove_multi %>%
+  select(-START_YEAR_expand, -END_YEAR_expand, -START_DATE_EXPAND, -END_DATE_EXPAND,
+         -START_DAY_OF_WEEK_EXPAND, -END_DAY_OF_WEEK_EXPAND, -Multi_expand)
+
+# Create EFFECTIVE_YEAR field to indicate the effective year of the closure
+closures_effective_year <- closures_remove_multi %>%
+  mutate(EFFECTIVE_YEAR = year(date_sequence))
+
+# Create EFFECTIVE_MONTH field to indicate the effective month of the closure
+closures_effective_month <- closures_effective_year %>%
+  mutate(EFFECTIVE_MONTH = month(date_sequence))
+
+# Create data frame with only REGION, ZONE_USE, date_sequence, CLOSE_OPEN, EFFECTIVE_YEAR, and EFFECTIVE_MONTH
+select_closures <- closures_effective_month %>%
+  select(REGION, ZONE_USE, date_sequence, CLOSE_OPEN, EFFECTIVE_YEAR, EFFECTIVE_MONTH)
+
+# Fill in missing date_sequence dates
+# Create a data frame with all dates for the specified EFFECTIVE_YEAR
+all_dates <- select_closures %>%
+  group_by(REGION, ZONE_USE, EFFECTIVE_YEAR) %>%
+  summarize(start_date = as.Date(paste(EFFECTIVE_YEAR, "01-01", sep = "-")),
+            end_date = as.Date(paste(EFFECTIVE_YEAR, "12-31", sep = "-"))) %>%
+  rowwise() %>%
+  mutate(date_sequence = list(seq.Date(start_date, end_date, by = "1 day"))) %>%
+  ungroup() %>%
+  select(-start_date, -end_date)
+
+# Expand the date_sequence list into separate rows
+# LOSE ZONES HERE!!!
+all_dates_expand <- all_dates %>%
+  rowwise() %>%
+  mutate(date_sequence = list(unlist(date_sequence))) %>%
+  unnest(date_sequence)
+
+# Merge the expanded data frame with the original select_closures data frame
+merged_dates <- all_dates %>%
+  left_join(select_closures, by = c("REGION", "ZONE_USE", "EFFECTIVE_YEAR", "date_sequence"))
+
+# Remove any duplicated dates
+duplicated_data <- merged_dates %>%
+  group_by(REGION, ZONE_USE, date_sequence) %>%
+  summarize(CLOSE_OPEN = ifelse(all(is.na(CLOSE_OPEN)), "OPEN", first(na.omit(CLOSE_OPEN)))) %>%
+  ungroup()
+
+# Extract EFFECTIVE_YEAR from date_sequence
+effective_year <- duplicated_data %>%
+  mutate(EFFECTIVE_YEAR1 = year(date_sequence))
+
+# Extract EFFECTIVE_MONTH1 from date_sequence
+effective_month <- effective_year %>%
+  mutate(EFFECTIVE_MONTH1 = month(date_sequence))
+
+# Create MONTH_TOTAL field to indicate how many days the fishery was closed for each month
+month_total <- effective_month %>%
+  group_by(REGION, ZONE_USE, EFFECTIVE_YEAR1, EFFECTIVE_MONTH1) %>%
+  summarise(MONTH_TOTAL = sum(CLOSE_OPEN == "CLOSE", na.rm = TRUE))
+
+# Create SEASON field to indicate if the fishery is opened or closed for the EFFECTIVE_MONTH1 of the EFFECTIVE_YEAR1
+# The fishery is considered closed if more than 15 days within a month are closed
+final_closure <- month_total %>%
+  group_by(EFFECTIVE_YEAR1, EFFECTIVE_MONTH1) %>%
+  mutate(SEASON = ifelse(MONTH_TOTAL > 15, "CLOSED", "OPEN")) %>%
+  slice(1) %>%
+  ungroup()
diff --git a/ODM-MH-Analysis_ready/Closures/Closure_tests/Fishing_season.R b/ODM-MH-Analysis_ready/Closures/Closure_tests/Fishing_season.R
new file mode 100644
index 0000000..4bf03f3
--- /dev/null
+++ b/ODM-MH-Analysis_ready/Closures/Closure_tests/Fishing_season.R
@@ -0,0 +1,121 @@
+# FISHING SEASON MH PROCESSING 
+### NOT DONE, STILL NEED SOME THOUGHT ###
+
+# Filter for only fishing season management type
+# Include REG_REMOVED == 0 because this helps us in getting an end date for fishing years that end, but we do not want to keep the record that "turns off" the regulation
+mh_fs <- mh_data_log %>%
+  filter(MANAGEMENT_TYPE_USE == 'FISHING SEASON', REG_REMOVED == 0)
+
+# Identify cases that have a split fishing season - yes, we have split fishing season 
+mh_fs_chk1 <- mh_fs %>% ungroup() %>%
+  group_by(FR_CITATION, FMP, REGION, COMMON_NAME_USE, SECTOR_USE, SUBSECTOR_USE, ZONE_USE, MANAGEMENT_STATUS_USE, EFFECTIVE_DATE) %>%
+  summarise(N_seasons = n()) 
+# Join to data
+mh_fs2 <- mh_fs %>% 
+  left_join(mh_fs_chk1, by = c("FR_CITATION", "REGION", "FMP", "EFFECTIVE_DATE", "SECTOR_USE", 
+                               "COMMON_NAME_USE", "ZONE_USE", "SUBSECTOR_USE", "MANAGEMENT_STATUS_USE"))
+# Check date fields
+chk_dates <- mh_fs2 %>%
+  select(REGULATION_ID, FR_CITATION, FMP, REGION, COMMON_NAME_USE, SECTOR_USE, SUBSECTOR_USE, ZONE_USE, MANAGEMENT_STATUS_USE, EFFECTIVE_DATE,
+         INEFFECTIVE_DATE, START_MONTH, START_DAY_USE, START_YEAR, START_DAY_OF_WEEK_USE, 
+         END_MONTH_USE, END_DAY_USE, END_YEAR_USE, END_DAY_OF_WEEK_USE, 
+         START_DATE2, END_DATE2)
+
+# Check other weekly recurring events
+chk_weekly <- mh_data_log %>% filter(MANAGEMENT_STATUS_USE == 'WEEKLY RECURRING')
+# Check REG ID 11548
+chk <- mh_data_log %>% filter(REGULATION_ID == 11548) %>% 
+  select(REGULATION_ID, FR_CITATION, FMP, REGION, COMMON_NAME_USE, SECTOR_USE, SUBSECTOR_USE, ZONE_USE, MANAGEMENT_STATUS_USE, EFFECTIVE_DATE,
+         INEFFECTIVE_DATE, START_MONTH, START_DAY_USE, START_YEAR, START_DAY_OF_WEEK_USE, 
+         END_MONTH_USE, END_DAY_USE, END_YEAR_USE, END_DAY_OF_WEEK_USE, 
+         START_DATE2, END_DATE2, EFFECTIVE_DATE_FY_1, FY_1, EFFECTIVE_DATE_FY_2, FY_2, EFFECTIVE_DATE_FY_3, FY_3)
+
+# Check spiny lobster - had no fishing year because has fishing season?
+# Include fishing season in "CLOSURE" story and process there
+chk <- mh_data_log %>% 
+  filter(COMMON_NAME_USE == 'LOBSTER, CARIBBEAN SPINY', is.na(FY_1),
+         MANAGEMENT_TYPE_USE == 'FISHING SEASON') %>%
+  select(CLUSTER, FR_CITATION, MANAGEMENT_TYPE_USE, MANAGEMENT_STATUS_USE, COMMON_NAME_USE,
+         FMP, REGION, SECTOR_USE, SUBSECTOR_USE, ZONE_USE,
+         EFFECTIVE_DATE, INEFFECTIVE_DATE, START_DATE2, END_DATE2, 
+         START_MONTH, START_DAY_USE, START_DAY_OF_WEEK_USE, START_TIME_USE, START_YEAR,
+         END_MONTH_USE, END_DAY_USE, END_DAY_OF_WEEK_USE, END_TIME_USE, END_YEAR) %>%
+  arrange(REGION, SECTOR_USE, SUBSECTOR_USE, START_DATE2)
+
+# Check red snapper that has 2 fishing seasons
+# Because these are multi-regs (same FR) the logic for start date2 and end date2 is not working
+chk <- mh_data_log %>% 
+  filter(COMMON_NAME_USE == 'SNAPPER, RED', REGION == 'GULF OF MEXICO',
+         MANAGEMENT_TYPE_USE == 'FISHING SEASON') %>%
+  select(CLUSTER, FR_CITATION, MANAGEMENT_TYPE_USE, MANAGEMENT_STATUS_USE, COMMON_NAME_USE,
+         FMP, REGION, SECTOR_USE, SUBSECTOR_USE, ZONE_USE,
+         EFFECTIVE_DATE, INEFFECTIVE_DATE, START_DATE2, END_DATE2, 
+         START_MONTH, START_DAY_USE, START_DAY_OF_WEEK_USE, START_TIME_USE, START_YEAR,
+         END_MONTH_USE, END_DAY_USE, END_DAY_OF_WEEK_USE, END_TIME_USE, END_YEAR,
+         EFFECTIVE_DATE_FY_1, EFFECTIVE_DATE_FY_2, EFFECTIVE_DATE_FY_3,
+         FY_1, FY_2, FY_3) %>%
+  arrange(REGION, SECTOR_USE, SUBSECTOR_USE, START_DATE2)
+
+# Identify number of days within a fishing season 
+mh_fs_ck2 <- mh_fs2 %>% 
+  # Create as date field to subtract (the year is not important here)
+  mutate(START_FS = case_when(!is.na(START_MONTH) & !is.na(START_DAY_USE) & is.na(START_YEAR) ~ as.Date(paste0("2023-", START_MONTH, "-", START_DAY_USE), "%Y-%m-%d"),
+                              !is.na(START_MONTH) & !is.na(START_DAY_USE) & !is.na(START_YEAR) ~ as.Date(paste0(START_YEAR, "-", START_MONTH, "-", START_DAY_USE), "%Y-%m-%d")),
+         END_FS = case_when(!is.na(END_MONTH_USE) & !is.na(END_DAY_USE) & is.na(END_YEAR_USE) ~ as.Date(paste0("2023-", END_MONTH_USE, "-", END_DAY_USE), "%Y-%m-%d"),
+                            !is.na(END_MONTH_USE) & !is.na(END_DAY_USE) & !is.na(END_YEAR_USE) ~ as.Date(paste0(END_YEAR_USE, "-", END_MONTH_USE, "-", END_DAY_USE), "%Y-%m-%d")),
+         DIFF = case_when(END_MONTH_USE >= START_MONTH ~ END_FS - START_FS,
+                          END_MONTH_USE < START_MONTH ~ START_FS - END_FS)) %>%
+  filter(N_seasons == 1)
+
+# Check fishing season for specific areas
+chk <- mh_fs_ck2 %>% group_by(SECTOR_USE, SUBSECTOR_USE, ZONE_USE) %>% summarize(n = n())
+
+# Recode fishing season records as fishing year if 365 days
+fs_to_fy <- mh_fs_ck2 %>% ungroup() %>%
+  filter(abs(DIFF) %in% c(364, 365, 1)) #%>%
+  #select(REGULATION_ID) %>% pull()
+
+# Check for cases where there is a single fishing season and its less than 365 days
+# These are cases where the inverse is then considered a closure
+fs_lt365 <- mh_fs_ck2 %>%
+  filter(abs(DIFF) < 365, !REGULATION_ID %in% fs_to_fy) %>% 
+  select(CLUSTER, REGULATION_ID, FR_CITATION, FR_URL, 
+         REGION, ZONE_USE, COMMON_NAME_USE, SECTOR_USE, SUBSECTOR_USE,
+         START_DATE2, END_DATE2, START_FS, END_FS, DIFF) %>% 
+  distinct()
+
+# Fishing season more than 365 days
+# Only one regulation - Gulf red drum and that is how the FR is worded
+fs_gt365 <- mh_fs_ck2 %>%
+  filter(abs(DIFF) > 365, !REGULATION_ID %in% fs_to_fy) %>% 
+  select(CLUSTER, REGULATION_ID, FR_CITATION, FR_URL, 
+         REGION, ZONE_USE, COMMON_NAME_USE, SECTOR_USE, SUBSECTOR_USE,
+         START_DATE2, END_DATE2, START_FS, END_FS, DIFF) %>% 
+  distinct()
+chk <- mh_data_log %>% filter(REGULATION_ID == 3486)
+
+# Fishing season missing diff days
+# Some just have start fishing season 
+# For the red snapper fishing season, that means it ends at the end of the fishing year
+# Others don't have fishing year and are missing key date fields because they are certain days of the week
+# All of those are rec Caribbean Spiny Lobster in the Gulf and SATL
+fs_nodiff <- mh_fs_ck2 %>%
+  filter(is.na(DIFF)) %>% 
+  select(CLUSTER, REGULATION_ID, FR_CITATION, FR_URL, 
+         REGION, ZONE_USE, COMMON_NAME_USE, SECTOR_USE, SUBSECTOR_USE,
+         EFFECTIVE_DATE, INEFFECTIVE_DATE, START_DATE2, END_DATE2, 
+         START_DAY, START_DAY_USE, START_MONTH, START_TIME_USE, START_YEAR, START_DAY_OF_WEEK_USE, START_FS, 
+         END_DAY, END_DAY_USE, END_MONTH, END_MONTH_USE, END_TIME, END_TIME_USE, END_YEAR, END_YEAR_USE, END_DAY_OF_WEEK_USE, END_FS, DIFF,
+         EFFECTIVE_DATE_FY_1, EFFECTIVE_DATE_FY_2, EFFECTIVE_DATE_FY_3,
+         FY_1, FY_2, FY_3) %>% 
+  distinct()
+
+# Remove these FRs since already added to fishing year
+mh_fs3 <- mh_fs2 %>%
+  filter(!REGULATION_ID %in% fs_to_fy)
+
+# Filter for species of interest
+fs_spp <- mh_fs3 %>%
+  filter(COMMON_NAME_USE == spp, REGION == region)
+
+# Format
diff --git a/ODM-MH-Analysis_ready/Closures/Closure_tests/SATL_Red_Porgy_closure_example.R b/ODM-MH-Analysis_ready/Closures/Closure_tests/SATL_Red_Porgy_closure_example.R
new file mode 100644
index 0000000..2d7ec99
--- /dev/null
+++ b/ODM-MH-Analysis_ready/Closures/Closure_tests/SATL_Red_Porgy_closure_example.R
@@ -0,0 +1,104 @@
+# Process all regulations related to closures
+
+# Load packages ####
+#install.packages("librarian")
+librarian::shelf(here, tidyverse, lubridate, openxlsx, neatRanges, splitstackshape)
+
+# Read in MH Data Log
+mh_data_log <- readRDS(here("ODM-MH-Data_log", "data", "results", "MH_DL_2024Feb08.RDS"))
+
+# Select species and region
+spp <- 'PORGY, RED'
+region <- 'SOUTH ATLANTIC'
+
+# Function to expand dates based on management status
+source(here("ODM-MH-Analysis_ready", "func_expand_status.R"))
+
+# filter for species and region (all closure related records)
+# Recode management types to have a value
+mh_spp_closure <- mh_data_log %>%
+  filter(COMMON_NAME_USE == spp & REGION == region &
+         (MANAGEMENT_TYPE_USE %in% c('CLOSURE', 'FISHING SEASON', 'FISHING YEAR', 'PROHIBITED SPECIES') |
+            # Flag == NO means fishery wide prohibited sale and purchase (ie closure)
+            MANAGEMENT_TYPE_USE == 'PROHIBITED SALE AND PURCHASE' & FLAG == 'NO')) %>%
+  mutate(VALUE = case_when(MANAGEMENT_TYPE_USE %in% c('FISHING SEASON', 'FISHING YEAR') ~ 'OPEN',
+                           MANAGEMENT_TYPE_USE %in% c('PROHIBITED SALE AND PURCHASE', 'PROHIBITED SPECIES') ~ 'CLOSE',
+                           TRUE ~ VALUE)) %>%
+  arrange(SECTOR_USE, START_DATE2)
+
+unique(mh_spp_closure$MANAGEMENT_TYPE_USE)
+
+# Fishing Year
+unique(select(filter(mh_spp_closure, MANAGEMENT_TYPE_USE == "FISHING YEAR"), MANAGEMENT_STATUS_USE))
+spp_year <- expand_status(mh_spp_closure, "FISHING YEAR")
+
+# Fishing Season - still needs some work if not entered consistently
+unique(select(filter(mh_spp_closure, MANAGEMENT_TYPE_USE == "FISHING SEASON"), MANAGEMENT_STATUS_USE))
+spp_season <- expand_status(mh_spp_closure, "FISHING SEASON")
+
+# Closure
+unique(select(filter(mh_spp_closure, MANAGEMENT_TYPE_USE == "CLOSURE"), MANAGEMENT_STATUS_USE))
+spp_closures <- expand_status(mh_spp_closure, "CLOSURE")
+
+# Prohibited sale and purchase
+unique(select(filter(mh_spp_closure, MANAGEMENT_TYPE_USE == "PROHIBITED SALE AND PURCHASE"), MANAGEMENT_STATUS_USE))
+spp_prohibited_sale <- expand_status(mh_spp_closure, "PROHIBITED SALE AND PURCHASE")
+
+# Prohibited species
+unique(select(filter(mh_spp_closure, MANAGEMENT_TYPE_USE == "PROHIBITED SPECIES"), MANAGEMENT_STATUS_USE))
+spp_prohibited_spp <- expand_status(mh_spp_closure, "PROHIBITED SPECIES")
+
+# Combine all management types that refer to closures
+spp_closure_story <- spp_year %>%
+  rename(FR_CITATION_year = "FR_CITATION",
+         VALUE_year = "VALUE") %>%
+  select(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, date_sequence, FR_CITATION_year, VALUE_year) %>%
+  full_join(spp_season %>%
+              rename(FR_CITATION_season = "FR_CITATION",
+                     VALUE_season = "VALUE") %>%
+              select(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, date_sequence, FR_CITATION_season, VALUE_season),
+            by = join_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE,  date_sequence)) %>%
+  full_join(spp_closures %>%
+              rename(FR_CITATION_close = "FR_CITATION",
+                     VALUE_close = "VALUE") %>%
+              select(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, date_sequence, FR_CITATION_close, VALUE_close),
+            by = join_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE,  date_sequence)) %>%
+  full_join(spp_prohibited_sale %>%
+             rename(FR_CITATION_sale = "FR_CITATION",
+                     VALUE_sale = "VALUE") %>%
+              select(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, date_sequence, FR_CITATION_sale, VALUE_sale),
+            by = join_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE,  date_sequence)) %>%
+  full_join(spp_prohibited_spp %>%
+              rename(FR_CITATION_spp = "FR_CITATION",
+                     VALUE_spp = "VALUE") %>%
+              select(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, date_sequence, FR_CITATION_spp, VALUE_spp),
+            by = join_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, date_sequence)) %>%
+  arrange(SECTOR_USE, SUBSECTOR_USE, ZONE_USE, date_sequence) %>%
+  # Select the most recent FR CITATION
+  mutate(FR_CITATION = pmax(FR_CITATION_year, 
+                            FR_CITATION_season, 
+                            FR_CITATION_close, 
+                            FR_CITATION_sale, 
+                            FR_CITATION_spp,
+                            na.rm = T)) %>%
+  # Select the fishery status (open/closed) that applies to the most recent FR
+  mutate(VALUE = case_when(FR_CITATION == FR_CITATION_close ~ VALUE_close,
+                           FR_CITATION == FR_CITATION_sale ~ VALUE_sale,
+                           FR_CITATION == FR_CITATION_spp ~ VALUE_spp,
+                           FR_CITATION == FR_CITATION_season ~ VALUE_season,
+                           FR_CITATION == FR_CITATION_year ~ VALUE_year))
+
+summ_spp_closures <- spp_closure_story %>%
+  mutate(YEAR = year(date_sequence)) %>%
+  group_by(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, YEAR, FR_CITATION, VALUE) %>% 
+  summarize(ndays = n(),
+            start = min(date_sequence),
+            end = max(date_sequence)) %>%
+  arrange(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, YEAR, start) %>%
+  # Add back FR_URL for reference
+  left_join(distinct(select(filter(ungroup(mh_data_log), FMP == 'SNAPPER-GROUPER FISHERY OF THE SOUTH ATLANTIC REGION'), FR_CITATION, FR_URL)),
+            by = join_by(FR_CITATION))
+
+# Save as Excel to compare to SEDAR MH file
+write.xlsx(summ_spp_closures, "C:/Users/sarina.atkinson/Documents/Data/MH_closures/SA_RP_closures_08Feb24.xlsx")
+