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Merge branch 'GaitlynMalone-NOAA-patch-1' of https://github.com/SEFSC…
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…/SEFSC-ODM-Management-History into GaitlynMalone-NOAA-patch-1
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@SarinaAtkinson-NOAA
SarinaAtkinson-NOAA committed Jan 24, 2025
2 parents cf8bdcf + 076a9e5 commit 67f2778
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40 changes: 24 additions & 16 deletions ODM-MH-Analysis_ready/Closures/Main_closure_prep.R
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@
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_2024Jun28.RDS"))
mh_data_log <- readRDS(here("ODM-MH-Data_log", "data", "results", "MH_DL_2024Dec16.RDS"))

# Select species and region
spp <- 'SNAPPER, RED'
Expand All @@ -14,6 +14,7 @@ region <- 'GULF OF MEXICO'
# Function to expand dates based on management status
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
mh_spp_closure <- mh_data_log %>%
Expand Down Expand Up @@ -66,16 +67,16 @@ 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, 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)) %>%
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,
Expand All @@ -86,17 +87,24 @@ spp_closure_story <- spp_year %>%
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_sale ~ VALUE_sale,
FR_CITATION == FR_CITATION_spp ~ VALUE_spp,
FR_CITATION == FR_CITATION_season ~ VALUE_season,
FR_CITATION == FR_CITATION_year ~ VALUE_year))
FR_CITATION == FR_CITATION_year ~ VALUE_year)) #%>%
select(-FR_CITATION_year, -VALUE_year, -FR_CITATION_season, -VALUE_season, -FR_CITATION_close, -VALUE_close)

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) %>%
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) %>%
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 = n(),
start = min(date_sequence),
end = max(date_sequence)) %>%
end = max(date_sequence),
.groups = 'drop') %>%
arrange(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, YEAR, start)

# Save as Excel to compare to SEDAR MH file
Expand Down
103 changes: 74 additions & 29 deletions ODM-MH-Analysis_ready/Closures/func_expand_status.R
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Original file line number Diff line number Diff line change
Expand Up @@ -22,18 +22,24 @@ expand_status <- function(x, y) {
mutate(date_sequence = map2(START_DATE2, END_DATE2, seq, by = "1 day")) %>%
unnest(date_sequence) %>%
select(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, MANAGEMENT_TYPE_USE, date_sequence,
CLUSTER, REGULATION_ID, FR_CITATION, VALUE, VALUE_UNITS, VALUE_TYPE, VALUE_RATE) %>%
CLUSTER, REGULATION_ID, FR_CITATION, VALUE, VALUE_UNITS, VALUE_TYPE, VALUE_RATE, MULTI_REG_VALUE) %>%
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) %>%
mutate(num_records = n()) %>%
mutate(action_value = case_when(num_records > 1 & MULTI_REG_VALUE != 1 & COMMON_NAME_USE == lag(COMMON_NAME_USE) ~ "slice",
num_records > 1 & MULTI_REG_VALUE == 1 ~ "keep",
num_records > 1 & MULTI_REG_VALUE != 1 & COMMON_NAME_USE != lag(COMMON_NAME_USE) ~ "keep",
TRUE ~ "other")) %>%
filter(!(action_value == "slice")) %>%
rename(CLUSTER_one = "CLUSTER",
REGULATION_ID_one = "REGULATION_ID",
FR_CITATION_one = "FR_CITATION",
VALUE_one = "VALUE",
VALUE_UNITS_one = "VALUE_UNITS",
VALUE_TYPE_one = "VALUE_TYPE",
VALUE_RATE_one = "VALUE_RATE")
VALUE_RATE_one = "VALUE_RATE") %>%
select(-num_records, -action_value)
} else {
df_one <- data.frame(FMP = character(), COMMON_NAME_USE = character(), REGION = character(), ZONE_USE = character(),
SECTOR_USE = character(), SUBSECTOR_USE = character(), MANAGEMENT_TYPE_USE = character(),
Expand All @@ -45,7 +51,7 @@ expand_status <- function(x, y) {
if('SEASONAL' %in% mstats){
# Seasonal
df_seasonal <- filter(df, MANAGEMENT_STATUS_USE == 'SEASONAL') %>%
mutate(date_sequence = map2(EFFECTIVE_DATE, END_DATE2, seq, by = "days")) %>%
mutate(date_sequence = map2(START_DATE2, END_DATE2, seq, by = "days")) %>%
unnest(date_sequence) %>%
mutate(START_YEAR_expand = year(date_sequence),
END_YEAR_expand = year(date_sequence)) %>%
Expand Down Expand Up @@ -88,30 +94,53 @@ expand_status <- function(x, y) {
if('MONTHLY RECURRING' %in% mstats){
# Monthly
df_monthly <- filter(df, MANAGEMENT_STATUS == 'MONTHLY RECURRING') %>%
mutate(date_sequence = map2(EFFECTIVE_DATE, END_DATE2, seq, by = "days")) %>%
mutate(date_sequence = map2(START_DATE2, END_DATE2, seq, by = "days")) %>%
unnest(date_sequence) %>%
# Extract year from date_sequence
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
# Create START_MONTH_expand and END_MONTH_expand to eventually create the dates that are the bounds of which expansion dates should still be included
# The conditions address cases where the monthly recurring goes from the middle of one month to the start of next and eventually crosses year boundaries (Gulf red snapper - 15th to the 1st of the following month or 10th to the 1st of the following month)
# If the END_DAY_USE is a smaller value than the START_DAY_USE and the day of the date_sequence is a smaller value than the START_DAY_USE and the month of the date_sequence is not January,
# then the START_MONTH_expand should be one month less than the month of the date_sequence
# If the END_DAY_USE is a smaller value than the START_DAY_USE and the day of the date_sequence is a smaller value than the START_DAY_USE but the month of the date_sequence is January,
# then the START_MONTH_expand should be December
# Otherwise, the START_MONTH_EXPAND should reflect the month of the date_sequence
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)),
# The conditions address cases where the monthly recurring goes from the middle of one month to the start of the next and eventually crosses year boundaries (Gulf red snapper - 15th to the 1st of the following month or 10th to the 1st of the following month)
# If the END_DAY_USE is a smaller value than the START_DAY_USE and the day of the date_sequence is a smaller value than the START_DAY_USE and the month of the date_sequence is not January
# then the END_MONTH_expand should be the month of the date_sequence
# If the END_DAY_USE is a smaller value than the START_DAY_USE and the day of the date_sequence is greater than or equal to the START_DAY_USE value and the month of the date_sequence + 1 month is less than or equal to 12 (December),
# then the END_MONTH_expand should be the month of the date_sequence plus one month
# If the END_MONTH_expand value is smaller than the START_DAY_USE and the day of the date_sequence is greater than or equal to the START_DAY_USE and the month of the date_sequence + 1 is equal to 13,
# then the END_MONTH_expand should be January
# Otherwise, the month of the date_sequence should be used
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))) %>%
TRUE ~ month(date_sequence)),
# Adjust END_YEAR_expand to address cases where the monthly recurring goes from the middle of one month to the start of the next and eventually crosses year boundaries (Gulf red snapper - 15th to the 1st of the following month or 10th to the 1st of the following month)
# If the day of the date_sequence is greater than or equal to the START_DAY_USE and the END_MONTH is January & the month of the date_sequence is December, then the END_YEAR_expand should be changed to the following year
# If the day of the date_sequence is less than the START_DAY_USE and the END_MONTH is January and the month of the date_sequence is December, then use the START_YEAR_expand
# Otherwise, use the END_YEAR_expand
END_YEAR_expand = case_when(day(date_sequence) >= START_DAY_USE & END_MONTH == 1 & month(date_sequence) == 12 ~ END_YEAR_expand + 1,
day(date_sequence) < START_DAY_USE & END_MONTH == 1 & month(date_sequence) == 12 ~ START_YEAR_expand,
TRUE ~ END_YEAR_expand)) %>%
# 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) %>%
#filter(START_MONTH_expand >= START_MONTH &
# START_MONTH_expand <= END_MONTH) %>%
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 date_sequence records outside expand range
filter(date_sequence >= START_DATE_EXPAND,
date_sequence <= END_DATE_EXPAND) %>%
filter(date_sequence == START_DATE_EXPAND |
date_sequence == END_DATE_EXPAND |
date_sequence > START_DATE_EXPAND & date_sequence < END_DATE_EXPAND) %>%
select(FMP, COMMON_NAME_USE, REGION, ZONE_USE, SECTOR_USE, SUBSECTOR_USE, MANAGEMENT_TYPE_USE, date_sequence,
CLUSTER, REGULATION_ID, FR_CITATION, VALUE, VALUE_UNITS, VALUE_TYPE, VALUE_RATE) %>%
arrange(date_sequence, desc(FR_CITATION)) %>%
Expand All @@ -136,7 +165,7 @@ expand_status <- function(x, y) {
if('WEEKLY RECURRING' %in% mstats){
# Weekly
df_weekly <- filter(df, MANAGEMENT_STATUS_USE == 'WEEKLY RECURRING') %>%
mutate(date_sequence = map2(EFFECTIVE_DATE, END_DATE2, seq, by = "days")) %>%
mutate(date_sequence = map2(START_DATE2, END_DATE2, seq, by = "days")) %>%
unnest(date_sequence) %>%
mutate(START_YEAR_expand = year(date_sequence),
END_YEAR_expand = year(date_sequence)) %>%
Expand Down Expand Up @@ -202,23 +231,39 @@ expand_status <- function(x, y) {
filter(!is.na(FMP)) %>%
# create final VALUE fields
ungroup() %>%
mutate(FR_CITATION = pmax(FR_CITATION_one, FR_CITATION_seasonal, FR_CITATION_weekly, FR_CITATION_monthly, na.rm = T)) %>%
mutate(VALUE = case_when(FR_CITATION == FR_CITATION_one ~ VALUE_one,
FR_CITATION == FR_CITATION_weekly ~ VALUE_weekly,
FR_CITATION == FR_CITATION_seasonal ~ VALUE_seasonal,
FR_CITATION == FR_CITATION_monthly ~ VALUE_monthly),
VALUE_UNITS = case_when(FR_CITATION == FR_CITATION_one ~ VALUE_UNITS_one,
FR_CITATION == FR_CITATION_weekly ~ VALUE_UNITS_weekly,
FR_CITATION == FR_CITATION_seasonal ~ VALUE_UNITS_seasonal,
FR_CITATION == FR_CITATION_monthly ~ VALUE_UNITS_monthly),
VALUE_TYPE = case_when(FR_CITATION == FR_CITATION_one ~ VALUE_TYPE_one,
FR_CITATION == FR_CITATION_weekly ~ VALUE_TYPE_weekly,
FR_CITATION == FR_CITATION_seasonal ~ VALUE_TYPE_seasonal,
FR_CITATION == FR_CITATION_monthly ~ VALUE_TYPE_monthly),
VALUE_RATE = case_when(FR_CITATION == FR_CITATION_one ~ VALUE_RATE_one,
FR_CITATION == FR_CITATION_weekly ~ VALUE_RATE_weekly,
FR_CITATION == FR_CITATION_seasonal ~ VALUE_RATE_seasonal,
FR_CITATION == FR_CITATION_monthly ~ VALUE_RATE_monthly))
rowwise() %>%
# For MANAGEMENT_TYPE_USE == "CLOSURE": On a specific date_sequence, if the most recent FR_CITATION applies to multiple closure types (i.e., one time closure and monthly recurring)
# Then maintain the more restrictive value (CLOSE)
# Otherwise, the most recent FR_CITATION should be maintained with associated value fields
mutate(max_citation = pmax(FR_CITATION_one, FR_CITATION_seasonal, FR_CITATION_weekly, FR_CITATION_monthly, na.rm = TRUE),
matched_values = list(c(
if (isTRUE(FR_CITATION_one == max_citation)) VALUE_one else NULL,
if (isTRUE(FR_CITATION_seasonal == max_citation)) VALUE_seasonal else NULL,
if (isTRUE(FR_CITATION_weekly == max_citation)) VALUE_weekly else NULL,
if (isTRUE(FR_CITATION_monthly == max_citation)) VALUE_monthly else NULL)),
final_value = case_when(
MANAGEMENT_TYPE_USE == "CLOSURE" & "CLOSE" %in% unlist(matched_values) ~ "CLOSE",
TRUE ~ unlist(matched_values)[1]),
FR_CITATION = case_when(
final_value == VALUE_one ~ FR_CITATION_one,
final_value == VALUE_seasonal ~ FR_CITATION_seasonal,
final_value == VALUE_weekly ~ FR_CITATION_weekly,
final_value == VALUE_monthly ~ FR_CITATION_monthly,
TRUE ~ max_citation),
VALUE = final_value,
VALUE_UNITS = case_when(FR_CITATION == FR_CITATION_one ~ VALUE_UNITS_one,
FR_CITATION == FR_CITATION_weekly ~ VALUE_UNITS_weekly,
FR_CITATION == FR_CITATION_seasonal ~ VALUE_UNITS_seasonal,
FR_CITATION == FR_CITATION_monthly ~ VALUE_UNITS_monthly),
VALUE_TYPE = case_when(FR_CITATION == FR_CITATION_one ~ VALUE_TYPE_one,
FR_CITATION == FR_CITATION_weekly ~ VALUE_TYPE_weekly,
FR_CITATION == FR_CITATION_seasonal ~ VALUE_TYPE_seasonal,
FR_CITATION == FR_CITATION_monthly ~ VALUE_TYPE_monthly),
VALUE_RATE = case_when(FR_CITATION == FR_CITATION_one ~ VALUE_RATE_one,
FR_CITATION == FR_CITATION_weekly ~ VALUE_RATE_weekly,
FR_CITATION == FR_CITATION_seasonal ~ VALUE_RATE_seasonal,
FR_CITATION == FR_CITATION_monthly ~ VALUE_RATE_monthly)) %>%
ungroup()

return(combine)

Expand Down
2 changes: 1 addition & 1 deletion ODM-MH-Data_log/MH00_data_formatting.R
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@
# Data reformatting for R

# Read in data
mh <- read.csv(here('ODM-MH-Data_log', 'data/raw', "MH_DOWNLOAD_AUG_23_2024.csv"),
mh <- read.csv(here('ODM-MH-Data_log', 'data/raw', "MH_DOWNLOAD_DEC_16_2024.csv"),
stringsAsFactors = FALSE,
colClasses=c("REGULATION_ID" = "numeric",
"START_DAY" = "numeric",
Expand Down
2 changes: 2 additions & 0 deletions ODM-MH-Data_log/MH02_sector_ids.R
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Original file line number Diff line number Diff line change
Expand Up @@ -74,8 +74,10 @@ new_sector_clusters <- mh_newvar %>%
# Create variables to outline how SUBSECTOR expands ####
# Process SECTOR_ID that have multi-SUBSECTOR
# Filter records to include cases where SECTOR has more than one SUBSECTOR for DETAILED MANAGEMENT_TYPE
# and records where MANAGEMENT_CATEGORY is not equal to CATCH LIMITS
multi_subsector <- mh_sector_id %>%
filter(DETAILED == "YES") %>%
filter(MANAGEMENT_CATEGORY != "CATCH LIMITS") %>%
select(FMP, SECTOR_USE, SECTOR_ID, SUBSECTOR) %>%
distinct() %>%
group_by(FMP, SECTOR_USE, SECTOR_ID) %>%
Expand Down
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