Accidentally made change in Gaitlyn patch-1 branch

.gitignore

@@ -14,3 +14,5 @@ ODM-MH-Data_log/data/results/Archived_DataLogs/
 
 # Gaitlyn's Personal Exploratory Files
 Examples/Testfiles/
+
+*.env

ODM-MH-Analysis_ready/Catch Limits/CatchLimits.R

@@ -6,7 +6,7 @@ librarian::shelf(here, tidyverse, gt, flextable, officer, dplyr, gt, officer, lu
 
 #### Load data ####
 # Load the MH Data Log
-mh <- readRDS(here("ODM-MH-Data_log", "data", "results", "MH_DL_2025Sep10.RDS"))
+mh <- readRDS(here("ODM-MH-Data_log", "data", "results", "MH_DL_2025Nov18.RDS"))
 
 #### Define species, region, and sector of interest ####
 species = c('SNAPPER, CUBERA', 'AMBERJACK, GREATER', 'AMBERJACK, LESSER', 'JACK, ALMACO', 'SNAPPER, SILK', 'SNAPPER, QUEEN', 'SNAPPER, BLACKFIN', 'WENCHMAN',
@@ -24,9 +24,9 @@ ACLs <- mh %>%
          REGION %in% region,
          SECTOR == sector,
          DETAILED == "YES",
-         NEVER_IMPLEMENTED %in% c(0, NA),
+         #NEVER_IMPLEMENTED == 0 | is.na(NEVER_IMPLEMENTED),
          REG_REMOVED == 0,
-         MANAGEMENT_TYPE_USE == "ACL") %>%
+         MANAGEMENT_TYPE_USE == "ACL") #%>%
   arrange(CLUSTER, START_DATE2) %>%
   mutate(Species = case_when(str_detect(COMMON_NAME_USE, ",") ~ {parts <- str_split_fixed(COMMON_NAME_USE, ",", 2)
   str_to_title(str_trim(parts[,2])) %>% paste(str_to_title(str_trim(parts[,1])))},
@@ -62,7 +62,7 @@ Quotas <- mh %>%
          REGION %in% region,
          SECTOR == sector,
          DETAILED == "YES",
-         NEVER_IMPLEMENTED %in% c(0, NA),
+         NEVER_IMPLEMENTED == 0 | is.na(NEVER_IMPLEMENTED),
          REG_REMOVED == 0,
          MANAGEMENT_TYPE_USE == "QUOTA") %>%
   arrange(CLUSTER, START_DATE2) %>%
@@ -100,7 +100,7 @@ ACTs <- mh %>%
          REGION %in% region,
          SECTOR == sector,
          DETAILED == "YES",
-         NEVER_IMPLEMENTED %in% c(0, NA),
+         NEVER_IMPLEMENTED == 0 | is.na(NEVER_IMPLEMENTED),
          MANAGEMENT_TYPE_USE == "ACT") %>%
   arrange(CLUSTER, START_DATE2) %>%
   mutate(Species = case_when(str_detect(COMMON_NAME_USE, ",") ~ {parts <- str_split_fixed(COMMON_NAME_USE, ",", 2)
@@ -137,7 +137,7 @@ TACs <- mh %>%
          REGION %in% region,
          SECTOR == sector,
          DETAILED == "YES",
-         NEVER_IMPLEMENTED %in% c(0, NA),
+         NEVER_IMPLEMENTED == 0 | is.na(NEVER_IMPLEMENTED),
          REG_REMOVED == 0,
          MANAGEMENT_TYPE_USE == "TAC") %>%
   arrange(CLUSTER, START_DATE2) %>%

ODM-MH-Analysis_ready/Management History Comparison Files/S103CaribbeanVisualizations.R

@@ -0,0 +1,260 @@
+# Management History Visualizations for Caribbean (S103)
+
+library(tidyverse)
+library(here)
+library(lubridate)
+library(ggrepel)
+library(openxlsx)
+
+# MH dataset 
+mh <- readRDS(here("ODM-MH-Data_log", "data", "results", "MH_DL_2025Nov18.RDS"))
+
+mh <- mh %>%
+  mutate(COMMON_NAME_USE = case_when(REGULATION_ID == '1411' ~ "MALACANTHIDAE—TILEFISHES",
+                                     TRUE ~ COMMON_NAME_USE))
+
+# Subset for South Atlantic Snapper Grouper
+mh_carib <- mh %>% filter(FMP %in% c("REEF FISH FISHERY OF PUERTO RICO AND THE U.S. VIRGIN ISLANDS", 
+                                    "FMP FOR THE EEZ AROUND ST. THOMAS AND ST. JOHN",
+                                    "FMP FOR THE EEZ AROUND ST. CROIX",
+                                    "FMP FOR THE EEZ AROUND PUERTO RICO",
+                                    "SPINY LOBSTER FISHERY OF PUERTO RICO AND THE U.S. VIRGIN ISLANDS",
+                                    "QUEEN CONCH RESOURCES OF PUERTO RICO AND THE U.S. VIRGIN ISLANDS")) %>%
+  # Remove recs that were never implemented
+  filter(NEVER_IMPLEMENTED == 0)
+
+management_sector_counts <- mh_carib %>%
+  group_by(MANAGEMENT_CATEGORY, SECTOR_USE) %>%
+  summarise(total_records = n_distinct(REGULATION_ID))
+
+mh_carib_closure <- mh_carib %>%
+  filter(MANAGEMENT_TYPE_USE == "CLOSURE")
+total_records_by_sector <- mh_carib_closure %>%
+  filter(MANAGEMENT_TYPE_USE == "CLOSURE") %>%
+  group_by(SECTOR_USE) %>%
+  summarise(total_records = n())
+
+# Summarize management types by sector
+plot1 <- ggplot(mh_carib %>%
+                  group_by(SECTOR_USE, MANAGEMENT_CATEGORY) %>%
+                  summarise(nrecs = n_distinct(REGULATION_ID)) %>%
+                  group_by(SECTOR_USE) %>% 
+                  mutate(tot = sum(nrecs), perc = nrecs/tot, labels = paste0(round(perc * 100,1), "%")), aes(x = "" , y = perc, fill = MANAGEMENT_CATEGORY)) +
+  geom_col(width = 1, color = 1) +
+  coord_polar(theta = "y") +
+  geom_text(aes(x = 1.7, label = labels), size=4.5, 
+            position = position_stack(vjust = 0.5))+
+  facet_wrap(~SECTOR_USE) +
+  guides(fill = guide_legend(title = "Management Category")) +
+  theme_void()
+print(plot1)
+
+# Timeline - number of regulations by sector by year
+reg_yr <- mh_carib %>%
+  mutate(YEAR = year(EFFECTIVE_DATE)) %>%
+  group_by(SECTOR_USE, YEAR, MANAGEMENT_CATEGORY, MANAGEMENT_TYPE_USE) %>%
+  summarize(nFRs = n_distinct(FR_CITATION),
+            nregs = n()) 
+
+# Plot by management cat and sector
+plot2 <- ggplot(reg_yr) +
+  geom_bar(aes(y=nFRs, x=YEAR), stat = 'identity') +
+  labs(x = "Year", y = "Number of FRs Published") +
+  facet_grid(SECTOR_USE~MANAGEMENT_CATEGORY, scales = "free_y") +
+  theme_bw()
+print(plot2)
+
+# FRs published by year and species
+# Clean up species into removed and currently managed categories
+species_list <- read.csv(here("Examples", "Testfiles", "snappergrouper_specieslist.csv"))
+
+species_list <- species_list %>%
+  mutate(REMOVED_DT = case_when(REMOVED_DT == '' ~ NA,
+                                TRUE ~ REMOVED_DT))
+
+currently_managed <- species_list %>% filter(is.na(REMOVED_DT)) %>% select(COMMON_NAME) %>% pull()
+
+# Prep for join
+sp_list_all <- species_list %>%
+  select(COMMON_NAME, ADDED_DT, REMOVED_DT) %>%
+  mutate(COMMON_NAME_USE = 'ALL',
+  ) %>%
+  mutate(ADDED_DATE = as.Date(ADDED_DT, origin = "1899-12-30"),
+         REMOVED_DATE = as.Date(REMOVED_DT, origin = "1899-12-30"))
+
+
+# Aggregate species list
+species_aggs <- read.csv(here("Examples", "Testfiles", "snappergrouper_aggregatefinal.csv")) %>%
+  mutate(COMMON_NAME_USE = COMMON_NAME,
+         SPP_NAME = FMP_SPECIES_AGG_NAME,
+         ADDED_DATE = as.Date(ADDED_DT, origin = "1899-12-30"),
+         REMOVED_DATE = as.Date(REMOVED_DT, origin = "1899-12-30"),
+         # convert FMP agg name to uppercase
+         FMP_SPECIES_AGG_NAME = toupper(FMP_SPECIES_AGG_NAME))
+
+
+# Prep for join
+#species_ifq_agg <- species_aggs %>%
+# filter(FMP_SPECIES_AGG_NAME %in% c('IFQ: DEEP-WATER GROUPERS (DWG)',
+#                                   'IFQ: OTHER SHALLOW-WATER GROUPERS (OTHER SWG)',
+#                                   'IFQ: TILEFISHES')) %>%
+#select(FMP_SPECIES_AGG_NAME, COMMON_NAME, ADDED_DATE, REMOVED_DATE)
+
+mh_srgr_cleaned <- mh_srgr %>%
+  # Expand common name all species
+  full_join(sp_list_all, by = join_by(COMMON_NAME_USE), multiple = 'all') %>%
+  mutate(YEAR = year(EFFECTIVE_DATE),
+         SPECIES = case_when(COMMON_NAME_USE == 'ALL' ~ COMMON_NAME,
+                             TRUE ~ COMMON_NAME_USE),
+         flg_dt = case_when(COMMON_NAME_USE == 'ALL' & EFFECTIVE_DATE<ADDED_DATE ~ 1,
+                            TRUE ~ 0)) %>%
+  # Remove regs expanded for species before they were in the FMP
+  filter(flg_dt == 0) %>%
+  select(-c(ADDED_DATE, REMOVED_DATE, flg_dt)) %>%
+  # Fix effective dates
+  left_join(sp_list_all %>% select(-COMMON_NAME_USE), by=join_by(SPECIES == COMMON_NAME)) %>%
+  mutate(flg_dt = case_when(EFFECTIVE_DATE<ADDED_DATE ~ 1,
+                            EFFECTIVE_DATE>REMOVED_DATE ~ 1,
+                            END_DATE2 > REMOVED_DATE ~ 1,
+                            TRUE ~ 0)) %>%
+  filter(flg_dt == 0) %>%
+  select(-c(COMMON_NAME, ADDED_DATE, REMOVED_DATE, flg_dt)) %>%
+  full_join(species_aggs, by = join_by(SPECIES == FMP_SPECIES_AGG_NAME), multiple='all') %>%
+  filter(!is.na(FR_CITATION)) %>%
+  mutate(SPECIES = case_when(!is.na(COMMON_NAME) ~ COMMON_NAME,
+                             TRUE ~ SPECIES),
+         flg_dt = case_when(!is.na(ADDED_DATE) & EFFECTIVE_DATE<ADDED_DATE ~ 1,
+                            !is.na(REMOVED_DATE) & END_DATE2 > REMOVED_DATE ~ 1,
+                            TRUE ~ 0)) %>%
+  filter(flg_dt == 0) %>%
+  mutate(SPECIES = case_when(SPECIES == 'JEWFISH' ~ 'GROUPER, GOLIATH',
+                             TRUE ~ SPECIES),
+         #SPECIES == 'Caulolatilus' ~ 'TILEFISHES',
+         #                   
+         #                  SPECIES == 'Centropristis melana' ~ 'BASS, BLACK SEA',
+         #               TRUE ~ SPECIES),
+         managed = case_when(SPECIES %in% currently_managed ~ 'Currently managed', TRUE ~ 'Removed from FMP'))
+
+chk <- mh_srgr_cleaned %>% ungroup() %>% 
+  select(SPECIES, managed) %>% distinct()
+
+sp_yr  <- mh_srgr_cleaned %>%  group_by(managed, SPECIES, YEAR) %>%
+  summarize(nFRs = n_distinct(FR_CITATION))
+
+plot <- ggplot(sp_yr %>% filter(managed == 'Currently managed')
+               , aes(x=YEAR, y=SPECIES, fill=nFRs)) +
+  geom_tile() +
+  scale_fill_gradientn(colours = c("#44ce1b", "#f7e379", "#e51f1f"), values = c(0,0.5,1)) +
+  theme_classic()
+
+# Timeline - number of regulations by sector by year
+reg_yr <- mh_srgr %>%
+  mutate(YEAR = year(EFFECTIVE_DATE)) %>%
+  group_by(SECTOR_USE, YEAR, MANAGEMENT_CATEGORY, MANAGEMENT_TYPE_USE) %>%
+  summarize(nFRs = n_distinct(FR_CITATION),
+            nregs = n()) 
+
+# Plot by management cat and sector
+plot3 <- ggplot(reg_yr) +
+  geom_bar(aes(y=nFRs, x=YEAR), stat = 'identity') +
+  facet_grid(SECTOR_USE~MANAGEMENT_CATEGORY, scales = "free_y") +
+  theme_bw()
+
+
+# Type of closures - mostly once and seasonal
+mh_srgr_closure <- mh_srgr %>%
+  filter(MANAGEMENT_TYPE_USE == "CLOSURE")
+
+total_records <- mh_srgr_closure %>%
+  # distinct(REGULATION_ID) %>%
+  nrow()
+print(total_records)
+
+total_records_by_sector <- mh_srgr_closure %>%
+  filter(MANAGEMENT_TYPE_USE == "CLOSURE") %>%
+  #distinct(REGULATION_ID, .keep_all = TRUE) %>%
+  group_by(SECTOR_USE, MANAGEMENT_STATUS_USE) %>%
+  summarise(total_records = n())
+print(total_records_by_sector)
+
+plot4 <- mh_srgr %>%
+  filter(MANAGEMENT_TYPE_USE == 'CLOSURE') %>%
+  group_by(SECTOR_USE, MANAGEMENT_STATUS_USE) %>%
+  summarise(nrecs = n()) %>%
+  group_by(SECTOR_USE) %>%
+  mutate(totregs = sum(nrecs),
+         percentage = nrecs/totregs*100) %>%
+  ggplot(aes(x=1, y=nrecs, fill = MANAGEMENT_STATUS_USE)) +
+  geom_bar(stat="identity", width = .1, position = "fill") +
+  coord_polar("y", start = 0) +
+  facet_wrap(~SECTOR_USE) +
+  geom_text(aes(label = paste0(round(percentage), "%")), position = position_fill(vjust = 0.5), size = 5) +
+  theme_void() +
+  labs(fill = 'Type of Closure')
+
+# Heat map of commercial closures overtime by species when closure for all of Gulf
+# Read in management status function
+source("C:/Users/gaitlyn.malone/Documents/SEFSC-ODM-Management-History/ODM-MH-Analysis_ready/Closures/func_expand_status.R")
+
+# Expand closures
+closures <- expand_status(mh_srgr, "CLOSURE")
+
+# Filter to main closures for subsector all, zone all
+# Add IFQ
+sum_closures <- closures %>%
+  mutate(value = 1,
+         managed = case_when(COMMON_NAME_USE %in% currently_managed ~ 'Currently managed', TRUE ~ 'Removed from FMP'),
+         SECTOR_lab =  paste(SECTOR_USE, SUBSECTOR_USE, sep="-"),
+         type = 'Closure') %>%
+  filter(VALUE == 'CLOSE', SUBSECTOR_USE == 'ALL', ZONE_USE == 'ALL', managed == 'Currently managed') %>%
+  # Add IFQ
+  bind_rows(mh_srgr %>% filter(MANAGEMENT_TYPE_USE == "ITQ PROGRAM ESTABLISHED") %>% ungroup() %>%
+              select(REGULATION_ID, CLUSTER, FR_CITATION, SECTOR_USE, SUBSECTOR_USE, COMMON_NAME_USE, VALUE, 
+                     START_DATE2, END_DATE2) %>%
+              mutate(date_sequence = map2(START_DATE2, END_DATE2, seq, by = "days")) %>%
+              unnest(date_sequence) %>%
+              mutate(value = 2,
+                     type = "ITQ",
+                     SECTOR_lab =  paste(SECTOR_USE, SUBSECTOR_USE, sep="-")))
+
+# Plot using expand function
+plot5 <- ggplot(sum_closures, aes(x=date_sequence, y = COMMON_NAME_USE, fill=value)) +
+  geom_tile() +
+  facet_wrap(~SECTOR_lab, ncol = 1) +
+  scale_x_date(date_breaks = "1 year",date_labels = "%Y", 
+               limits = c(min(sum_closures$date_sequence), max = max(sum_closures$date_sequence)), expand=c(0,0)) +
+  #geom_vline(xintercept = as.Date("2010-01-01"), linetype = 'dashed', color = 'red') +
+  #geom_vline(xintercept = as.Date("2007-01-01"), linetype = 'dashed', color = 'red') +
+  labs(y = "Species", x = "Date") +
+  theme_minimal() +
+  theme(legend.position = "none",
+        axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
+
+# Get count of FRs by species, consol. mcat, and sector
+sum_sp_mtype_sect <- mh_srgr_cleaned %>%
+  filter(managed == "Currently managed") %>% ungroup() %>%
+  # Consolidate management category into 4 groups
+  mutate(MCAT_GRP = case_when(MANAGEMENT_CATEGORY %in% c('EFFORT LIMITS', 'GEAR REQUIREMENTS', 'HARVEST LIMITATIONS',
+                                                         'UNIVERSAL', 'SELECTIVITY CONTROLS') ~ 'EFFORT/GEAR LIMITS',
+                              TRUE ~ MANAGEMENT_CATEGORY)) %>%
+  filter(MANAGEMENT_CATEGORY != 'OTHER') %>%
+  group_by(SPECIES, managed, MANAGEMENT_CATEGORY) %>%
+  summarize(nFRs = n_distinct(FR_CITATION),
+            nregs = n_distinct(REGULATION_ID)) 
+
+plot_sms <- ggplot(sum_sp_mtype_sect) +
+  geom_bar(aes(x=nregs, y = SPECIES, fill=MANAGEMENT_CATEGORY), stat="identity") +
+  labs(fill="Management Category", x = "Number of Regulations")+
+  theme_classic()
+
+# Look into 'other' category
+other_reg_yr <- mh_srgr %>%
+  mutate(YEAR = year(EFFECTIVE_DATE)) %>%
+  filter(MANAGEMENT_CATEGORY == 'OTHER') %>%
+  group_by(YEAR, MANAGEMENT_TYPE_USE) %>%
+  summarize(nFRs = n_distinct(FR_CITATION),
+            nregs = n()) %>%
+  ggplot() +
+  geom_bar(aes(y=nFRs, x=YEAR), stat = 'identity') +
+  facet_wrap(~MANAGEMENT_TYPE_USE, ncol=2, scales = "free_y") +
+  theme_bw()