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FIS.R
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## Script name: Abalone Fishery Independant Survey (FIS) Analysis
## Purpose of script: Compilation and analysis of Abalone Recuitment Module (ARM) and Length
## Evaluation and Growth (LEG) survey data.
## Author: Jaime McAllister and Craig Mundy
## Date Created: 2020-05-11
## Copyright (c) Jaime McAllister, 2020
## Email: [email protected]
##--------------------------------------------------------------------------------------##
## set working directory
setwd('C:/CloudStor/R_Stuff/FIS')
##--------------------------------------------------------------------------------------##
## Load libraries ####
## load library packages
suppressPackageStartupMessages({
library(dplyr)
library(ggplot2)
library(scales)
library(tidyr)
library(gdata)
library(openxlsx)
library(lubridate)
library(reshape)
library(gridExtra)
library(ggpubr)
library(readxl)
library(tibble)
library(data.table)
library(ggpmisc)
})
##--------------------------------------------------------------------------------------##
## Load functions ####
source("C:/GitCode/AbResearch/getSeason.r")
source("C:/GitCode/AbResearch/errorUpper2.r")
source("C:/GitCode/AbResearch/stderr.r")
##--------------------------------------------------------------------------------------##
## LEG data ####
## load LEG data
xl_data <- 'R:/TAFI/TAFI_MRL_Sections/Abalone/Section Shared/Abalone_databases/Data/Data for Transfer/2018/Ab_pop_bio_Lenght_density_2016.xlsx'
##--------------------------------------------------------------------------------------##
## LEG compile and clean data ####
## LEG data is first compiled into a single dataframe which enables some cleaning of data
## to occur prior to being seperated into abundance and size structure data for analysis.
## identify sheets in excel workbook
tab_names <- excel_sheets(path = xl_data)
## create list from seperate sheets
list_all <- lapply(tab_names, function(x) read_excel(path = xl_data, sheet = x))
## create dataframe from seperate sheets
legs.df <- rbindlist(list_all, fill = T)
## convert varible names to lower case and compile data for estimates and comments
## columns, removing additional columns from the Excel import (i.e. each sheet contained
## different column names for these variables)
colnames(legs.df) <- tolower(colnames(legs.df))
names(legs.df) <- gsub('/', '', names(legs.df), fixed = T)
names(legs.df) <- gsub(' ', '', names(legs.df), fixed = T)
names(legs.df) <- gsub('=', '', names(legs.df), fixed = T)
names(legs.df) <- gsub('comments', 'comments.1', names(legs.df), fixed = T)
names(legs.df) <- gsub('...8', 'comments.2', names(legs.df), fixed = T)
names(legs.df) <- gsub('...9', 'comments.3', names(legs.df), fixed = T)
names(legs.df) <- gsub('...10', 'comments.4', names(legs.df), fixed = T)
names(legs.df) <- gsub('eestimate', 'comments.5', names(legs.df), fixed = T)
colnames(legs.df) <- make.unique(names(legs.df))
legs.df <- dplyr::rename(legs.df, survdate = date)
legs.df <- dplyr::rename(legs.df, sllength = length)
legs.df$string <- as.factor(legs.df$string)
legs.df.2 <- legs.df %>%
# select(-comments.3) %>%
unite('all_comments', 'comments.1','comments.2', 'comments.4',
'comments.5', sep = ',') %>%
mutate(all_comments = gsub('NA', '', all_comments),
all_comments = gsub(',', '', all_comments),
all_comments = gsub('^$', NA, all_comments)) %>%
mutate(estimate.2 = estimate) %>%
mutate(estimate = if_else(is.na(all_comments) & estimate.2 %in% c('E', 'e'), estimate.2,
if_else(all_comments %in% c('E', 'e'),
all_comments, NA_character_))) %>%
mutate(comments = if_else(is.na(estimate), all_comments, NA_character_),
estimate = gsub('e', 'E', estimate)) %>%
dplyr::select(-c(estimate.2, all_comments)) %>%
as.data.frame()
## remove any characters or obvious errors from shell length (e.g. where 'estimate (E)'
## or 0 has been entered in the raw data)
str(legs.df.2$sllength) #check
legs.df.2 <- legs.df.2 %>%
mutate(estimate = if_else(grepl('E', sllength), 'E', estimate),
sllength = as.numeric(gsub('E', '', sllength))) %>%
filter(sllength != 0)
# str(legs.df.2$sllength) #check
## remove data with no site name or shell length
legs.df.2 <- filter(legs.df.2, !is.na(site))
legs.df.2 <- filter(legs.df.2, !is.na(sllength))
## remove characters from site names and rename sites to a three letter acronym
# unique(bigabs$site)
legs.df.2$site <- gsub(' ', '', legs.df.2$site)
legs.df.2$site <- gsub('_', '', legs.df.2$site)
legs.df.2$site <- gsub('Telopea', 'TEL', legs.df.2$site)
legs.df.2$site <- gsub('SP', 'SEY', legs.df.2$site)
legs.df.2$site <- gsub('\\bT\\b', 'THU', legs.df.2$site)
legs.df.2$site <- gsub('BI', 'BET', legs.df.2$site)
legs.df.2$site <- gsub('TG', 'GAR', legs.df.2$site)
legs.df.2$site <- gsub('GIII', 'GEO', legs.df.2$site)
legs.df.2$site <- gsub('MB', 'MUN', legs.df.2$site)
legs.df.2$site <- gsub('MP', 'INN', legs.df.2$site)
legs.df.2$site <- gsub('LB', 'LOU', legs.df.2$site)
legs.df.2$site <- gsub('OB', 'OUT', legs.df.2$site)
## rename string names from earlier sampling periods
# table(legs.df.2$site, legs.df.2$string)
legs.df.2$string <- gsub( "Kar", "1", legs.df.2$string )
legs.df.2$string <- gsub( "Juv", "2", legs.df.2$string )
legs.df.2$string <- gsub( "N", "1", legs.df.2$string )
legs.df.2$string <- gsub( "S", "2", legs.df.2$string )
legs.df.2$string <- gsub( "North", "1", legs.df.2$string )
legs.df.2$string <- gsub( "South", "2", legs.df.2$string )
## rename east and west transect directions
# unique(legs.df.2$eastwest)
legs.df.2$eastwest <- gsub('w', 'W', legs.df.2$eastwest)
legs.df.2$eastwest <- gsub('N', 'E', legs.df.2$eastwest)
legs.df.2$eastwest <- gsub('S', 'W', legs.df.2$eastwest)
legs.df.2$eastwest <- gsub('L', 'W', legs.df.2$eastwest)
legs.df.2$eastwest <- gsub('R', 'E', legs.df.2$eastwest)
## add unique identifier for each measurement
legs.df.2$survindex <- as.factor(paste(legs.df.2$site, legs.df.2$survdate, legs.df.2$string,
legs.df.2$transect, sep="_"))
##--------------------------------------------------------------------------------------##
## LEG density data ####
## To generate density estimates for LEG data, individual length data first need to be
## converted to counts then density.
## LEG ALL count data ##
## filter for ALL abalone
legs.dat <- legs.df.2 %>%
group_by(survindex) %>%
summarise(ab_n = n()) %>% #as.data.frame()
complete(survindex, fill = list(ab_n = 0)) %>%
as.data.frame()
## calculate abs per square metre
legs.dat$absm <- legs.dat$ab_n / 15
## unpack survindex variables and create new dataframe
leg.counts <- data.frame(separate(legs.dat, survindex, sep = "_",
into = c("site", "survdate",
"string","transect"), convert = TRUE),
legs.dat$survindex, legs.dat$ab_n, legs.dat$absm)
## set string as a factor
leg.counts$string <- as.factor(leg.counts$string)
## construct date and season variables
leg.counts$survdate <- as.Date(strptime(leg.counts$survdate, "%Y-%m-%d"))
leg.counts$sampyear <- year(leg.counts$survdate)
leg.counts$season <- getSeason(leg.counts$survdate)
## recode autumn samples as summer
leg.counts$season <- gsub( "Autumn", "Summer", leg.counts$season)
## create year.season variable and arrange in order (i.e. summer, winter, spring)
leg.counts$season <- as.factor(leg.counts$season)
leg.counts$season <- ordered(leg.counts$season, levels=c("Summer","Winter","Spring"))
leg.counts$yr.season <- interaction(leg.counts$sampyear,leg.counts$season)
# sort(unique(leg.counts$yr.season))
leg.counts$yr.season <-
ordered(leg.counts$yr.season, levels = c("2015.Summer", "2015.Winter", "2015.Spring",
"2016.Summer", "2016.Winter", "2016.Spring",
"2017.Summer", "2017.Winter", "2017.Spring",
"2018.Summer", "2018.Winter", "2018.Spring",
"2019.Summer", '2019.Winter', '2019.Spring',
'2020.Summer', "2020.Spring", "2021.Summer", '2021.Spring',
'2022.Summer'))
## adjust misclassified seasons for The Gardens
pick <- which(leg.counts$site == "GAR")
leg.counts$yr.season[pick] <- gsub( "2015.Summer", "2015.Spring",
leg.counts$yr.season[pick])
leg.counts$yr.season <- droplevels(leg.counts$yr.season)
## save a copy of the R files
saveRDS(leg.counts, 'C:/CloudStor/R_Stuff/FIS/leg.counts.RDS')
##--------------------------------------------------------------------------------------##
## OPTION: the dataframe can also be seperated into different size classes to generate
##plots of legal and sub-legal abalone counts (see LEG plots)
## LEG SIZE count data
## Filter for sub-legal abalone
legs.dat.sub <- legs.df.2 %>%
filter(sllength <= 137) %>%
# bigabdat <- bigabs %>%
group_by(survindex) %>%
summarise(ab_n = n()) %>% #as.data.frame()
complete(survindex, fill = list(ab_n = 0)) %>%
as.data.frame()
## Filter for legal abalone
legs.dat.leg <- legs.df.2 %>%
filter(sllength >= 138) %>%
group_by(survindex) %>%
summarise(ab_n = n()) %>% #as.data.frame()
complete(survindex, fill = list(ab_n = 0)) %>%
as.data.frame()
## Filter for sub-legal abalone <10 mm
legs.dat.sub.ten <- legs.df.2 %>%
filter(sllength >= 128 & sllength < 138) %>%
group_by(survindex) %>%
summarise(ab_n = n()) %>% #as.data.frame()
complete(survindex, fill = list(ab_n = 0)) %>%
as.data.frame()
## Filter for legal abalone +10 mm
legs.dat.leg.ten <- legs.df.2 %>%
filter(sllength >= 139 & sllength <= 148) %>%
group_by(survindex) %>%
summarise(ab_n = n()) %>% #as.data.frame()
complete(survindex, fill = list(ab_n = 0)) %>%
as.data.frame()
## join dataframes created above for survindex
legs.dat.join <- left_join(legs.dat.sub, legs.dat.leg, by = 'survindex') %>%
left_join(., legs.dat.sub.ten, by = 'survindex') %>%
left_join(., legs.dat.leg.ten, by = 'survindex') %>%
left_join(., legs.dat, by = 'survindex')
## rename variables to identify abcounts from each size class
legs.dat.join <- dplyr::rename(legs.dat.join, ab_n_leg = ab_n.y)
legs.dat.join <- dplyr::rename(legs.dat.join, ab_n_sub = ab_n.x)
legs.dat.join <- dplyr::rename(legs.dat.join, ab_n_sub_ten = ab_n.x.x)
legs.dat.join <- dplyr::rename(legs.dat.join, ab_n_leg_ten = ab_n.y.y)
## calculate abs per square metre for joint dataframe
legs.dat.join$absm_sub <- legs.dat.join$ab_n_sub /15
legs.dat.join$absm_leg <- legs.dat.join$ab_n_leg /15
legs.dat.join$absm_sub_ten <- legs.dat.join$ab_n_sub_ten /15
legs.dat.join$absm_leg_ten <- legs.dat.join$ab_n_leg_ten /15
legs.dat.join$absm <- legs.dat.join$ab_n /15
## unpack survindex variables and create new dataframe for the joint dataframe
legs.counts.join <- data.frame(separate(legs.dat.join, survindex, sep = "_",
into = c("site", "survdate", "string","transect"),
convert = TRUE),
legs.dat.join$survindex, legs.dat.join$ab_n,
legs.dat.join$absm, legs.dat.join$ab_n_sub,
legs.dat.join$absm_sub, legs.dat.join$ab_n_leg,
legs.dat.join$absm_leg,
legs.dat.join$ab_n_sub_ten, legs.dat.join$absm_sub_ten,
legs.dat.join$ab_n_leg_ten, legs.dat.join$absm_leg_ten)
## set string as a factor
legs.counts.join$string <- as.factor(legs.counts.join$string)
## construct date and season variables
legs.counts.join$survdate <- as.Date(strptime(legs.counts.join$survdate, "%Y-%m-%d"))
legs.counts.join$sampyear <- year(legs.counts.join$survdate)
legs.counts.join$season <- getSeason(legs.counts.join$survdate)
## recode autumn samples as summer
legs.counts.join$season <- gsub( "Autumn", "Summer", legs.counts.join$season)
## create year.season variable and arrange in order (i.e. summer, winter, spring)
legs.counts.join$season <- as.factor(legs.counts.join$season)
legs.counts.join$season <- ordered(legs.counts.join$season,
levels=c("Summer","Winter","Spring"))
legs.counts.join$yr.season <- interaction(legs.counts.join$sampyear,
legs.counts.join$season)
# unique(legs.counts.join$yr.season)
legs.counts.join$yr.season <-
ordered(legs.counts.join$yr.season,
levels = c("2015.Summer", "2015.Winter", "2015.Spring",
"2016.Summer", "2016.Winter", "2016.Spring",
"2017.Summer", "2017.Winter", "2017.Spring",
"2018.Summer", "2018.Winter", "2018.Spring",
"2019.Summer", '2019.Winter', '2019.Spring',
'2020.Summer', "2020.Spring", "2021.Summer"))
## adjust misclassified seasons
pick <- which(legs.counts.join$site == "GAR")
legs.counts.join$yr.season[pick] <- gsub( "2015.Summer", "2015.Spring",
legs.counts.join$yr.season[pick])
legs.counts.join$yr.season <- droplevels(legs.counts.join$yr.season)
## save a copy of the R files
saveRDS(legs.counts.join, 'C:/CloudStor/R_Stuff/FIS/legs.counts.join.RDS')
##--------------------------------------------------------------------------------------##
## OPTION: the dataframe can also be seperated into individual LEG sampling sites
## LEG SITE count data
## subset data to individual LEG sampling sites
list_legscounts.site <- split(legs.counts.join, legs.counts.join$site)
names(list_legscounts.site)
legscounts.sites <- c("legscounts.BET", "legscounts.BRB", "legscounts.BRS",
"legscounts.GAR", "legscounts.GEO", "legscounts.INN",
"legscounts.LOU", "legscounts.MUN", "legscounts.OUT",
"legscounts.SEY", "legscounts.TEL", "legscounts.THU")
for (i in 1:length(list_legscounts.site)) {
assign(legscounts.sites[i], list_legscounts.site[[i]])
}
## save a copy of the R files
saveRDS(list_legscounts.site, 'C:/CloudStor/R_Stuff/FIS/list_legscounts.site.RDS')
##--------------------------------------------------------------------------------------##
## LEG size data ####
## create a copy of the original LEG dataframe
legs.sl <- legs.df.2
## construct date and season variables
legs.sl$sampyear <- year(legs.sl$survdate)
legs.sl$season <- getSeason(legs.sl$survdate)
## recode autumn samples as summer
legs.sl$season <- gsub( "Autumn", "Summer", legs.sl$season)
## create year.season variable and arrange in order (i.e. summer, winter, spring)
legs.sl$season <- as.factor(legs.sl$season)
legs.sl$season <- ordered(legs.sl$season, levels=c("Summer","Winter","Spring"))
legs.sl$yr.season <- interaction(legs.sl$sampyear,legs.sl$season)
legs.sl$yr.season <-
ordered(legs.sl$yr.season, levels = c("2015.Summer", "2015.Winter", "2015.Spring",
"2016.Summer", "2016.Winter", "2016.Spring",
"2017.Summer", "2017.Winter", "2017.Spring",
"2018.Summer", "2018.Winter", "2018.Spring",
"2019.Summer", '2019.Winter', '2019.Spring',
'2020.Summer', '2020.Spring', '2021.Summer', '2021.Spring',
'2022.Summer'))
## adjust misclassified seasons for The Gardens
pick <- which(legs.sl$site == "GAR")
legs.sl$yr.season[pick] <- gsub( "2015.Summer", "2015.Spring", legs.sl$yr.season[pick])
legs.sl$yr.season <- droplevels(legs.sl$yr.season)
## save a copy of the R files
saveRDS(legs.sl, 'C:/CloudStor/R_Stuff/FIS/legs.sl.RDS')
##--------------------------------------------------------------------------------------##
## OPTION: the dataframe can also be seperated into individual LEG sampling sites
## LEG size SITE frequency data
## subset data into sites
list_legs.sl.site <- split(legs.sl, legs.sl$site)
names(list_legs.sl.site)
legs.sl.sites <- c("legs.sl.BET", "legs.sl.BRB", "legs.sl.BRS", "legs.sl.GAR",
"legs.sl.GEO", "legs.sl.INN", "legs.sl.LOU", "legs.sl.MUN",
"legs.sl.OUT", "legs.sl.SEY", "legs.sl.TEL", "legs.sl.THU")
for (i in 1:length(list_legs.sl.site)) {
assign(legs.sl.sites[i], list_legs.sl.site[[i]])
}
## save a copy of the R files
saveRDS(list_legs.sl.site, 'C:/CloudStor/R_Stuff/FIS/list_legs.sl.site.RDS')
##--------------------------------------------------------------------------------------##
## ARM data ####
## ARM data is loaded from one Excel file containing seperate sheets for each site.
## Load ARM data
xl_arm_data <- 'R:/TAFI/TAFI_MRL_Sections/Abalone/Section Shared/Abalone_databases/Data/Data for Transfer/2018/Juvenile_data_2016.xlsx'
##--------------------------------------------------------------------------------------##
## ARM compile and clean data ####
## identify sheets in excel workbook
tab_names_arms <- excel_sheets(path = xl_arm_data)
## create list from seperate sheets
list_all_arms <- lapply(tab_names_arms, function(x) read_excel(path = xl_arm_data,
sheet = x, guess_max = 10000))
## remove list items (i.e. sheets) that are irrelevant
list_all_arms_2 <- list_all_arms[-c(2, 10)]
## create dataframe from seperate sheets
arms.df <- rbindlist(list_all_arms_2, fill = T)
## convert varible names to lower case, compile data comments columns and clean data,
## removing additional columns from the Excel import (i.e. each sheet contained
## different column names for these variables)
names(arms.df) <- gsub('/', '', names(arms.df), fixed = T)
names(arms.df) <- gsub(' ', '', names(arms.df), fixed = T)
arms.df <- dplyr::rename(arms.df, rock.plate.3 = RockorPlate)
colnames(arms.df) <- tolower(colnames(arms.df))
colnames(arms.df) <- make.unique(names(arms.df))
names(arms.df) <- gsub('comments', 'comments.1', names(arms.df), fixed = T)
names(arms.df) <- gsub('...8', 'comments.2', names(arms.df), fixed = T)
arms.df <- dplyr::rename(arms.df, site = location)
arms.df <- dplyr::rename(arms.df, survdate = date)
arms.df <- dplyr::rename(arms.df, sllength = ab_sl)
arms.df <- dplyr::rename(arms.df, rock.plate.1 = rp)
arms.df <- dplyr::rename(arms.df, rock.plate.2 = rockorplate)
arms.df <- dplyr::rename(arms.df, tag.col = tagcolour)
arms.df <- dplyr::rename(arms.df, tag.id.col = printcolour)
arms.df <- dplyr::rename(arms.df, tag.id = tag_id)
arms.df <- dplyr::rename(arms.df, tag.recap = tr)
arms.df$string <- as.factor(arms.df$string)
arms.df$rock.plate.3 <- gsub('r', 'R', arms.df$rock.plate.3)
arms.df$string = gsub('North', 1, arms.df$string)
arms.df$plate = gsub(8.5, 8, arms.df$plate)
arms.df <- arms.df %>%
mutate(comments.1 = if_else(sllength == 'OFF', 'plate off', comments.1),
sllength = gsub('OFF', NA, sllength))
## combine duplicate columns and compile dataframe
arms.df.2 <- arms.df %>%
unite('comments', 'comments.1', 'comments.2', sep = ',') %>%
mutate(comments = gsub('NA', '', comments),
comments = gsub(',', '', comments),
comments = gsub('^$', NA, comments)) %>%
unite('rock.plate', 'rock.plate.1', 'rock.plate.2', 'rock.plate.3', sep = ',') %>%
mutate(rock.plate = gsub('NA', '', rock.plate),
rock.plate = gsub(',', '', rock.plate),
rock.plate = gsub('^$', NA, rock.plate))
## remove data with no site name or shell length
arms.df.2 <- filter(arms.df.2, !is.na(site))
arms.df.2 <- filter(arms.df.2, !is.na(sllength))
## remove characters from site names and rename sites to a three letter acronym
# unique(arms.df.2$site)
arms.df.2$site <- gsub(' ', '', arms.df.2$site)
arms.df.2$site <- gsub('_', '', arms.df.2$site)
arms.df.2$site <- gsub('Telopea', 'TEL', arms.df.2$site)
arms.df.2$site <- gsub('SP', 'SEY', arms.df.2$site)
arms.df.2$site <- gsub('\\bT\\b', 'THU', arms.df.2$site)
arms.df.2$site <- gsub('BI', 'BET', arms.df.2$site)
arms.df.2$site <- gsub('TG', 'GAR', arms.df.2$site)
arms.df.2$site <- gsub('GIII', 'GEO', arms.df.2$site)
arms.df.2$site <- gsub('MB', 'MUN', arms.df.2$site)
arms.df.2$site <- gsub('SB', 'INN', arms.df.2$site)
arms.df.2$site <- gsub('LB', 'LOU', arms.df.2$site)
arms.df.2$site <- gsub('OS', 'OUT', arms.df.2$site)
arms.df.2$site <- gsub('G3', 'GEO', arms.df.2$site)
arms.df.2$site <- gsub('G4', 'GEO', arms.df.2$site)
arms.df.2$site <- gsub('G5', 'GEO', arms.df.2$site)
arms.df.2$site <- gsub('G6', 'GEO', arms.df.2$site)
arms.df.2$site <- gsub('G7', 'GEO', arms.df.2$site)
##--------------------------------------------------------------------------------------##
## ARM size data ####
## A. Extract records with abs for length frequency analysis
arms.sl <- arms.df.2 %>%
mutate(sllength = as.numeric(as.character(sllength))) %>%
filter(is.nan(sllength) | !is.na(sllength))
## construct date and season variables
arms.sl$sampyear <- as.factor(year(arms.sl$survdate))
arms.sl$season <- getSeason(arms.sl$survdate)
## recode autumn samples as summer
arms.sl$season <- gsub( "Autumn", "Summer", arms.sl$season)
arms.sl$season <- as.factor(arms.sl$season)
arms.sl$season <- ordered(arms.sl$season, levels=c("Summer","Winter","Spring"))
## extract year.season and arrange in order (i.e. summer, winter, spring)
arms.sl$yr.season <- interaction(arms.sl$sampyear,arms.sl$season)
# levels(arms.sl$yr.season)
arms.sl$yr.season <-
ordered(arms.sl$yr.season, levels = c("2015.Summer", "2015.Winter", "2015.Spring",
"2016.Summer", "2016.Winter", "2016.Spring",
"2017.Summer", "2017.Winter", "2017.Spring",
"2018.Summer", "2018.Winter", "2018.Spring",
"2019.Summer", "2019.Winter", "2019.Spring",
'2020.Summer', '2020.Spring', '2021.Summer',
'2021.Spring', '2022.Summer'))
## recode Gardens 2015.summer samples as 2015.spring
pick <- which(arms.sl$site == "GAR")
arms.sl$yr.season[pick] <- gsub("2015.Summer", "2015.Spring", arms.sl$yr.season[pick])
arms.sl$yr.season <- droplevels(arms.sl$yr.season)
## save a copy of the R files
saveRDS(arms.sl, 'C:/CloudStor/R_Stuff/FIS/arms.sl.RDS')
##--------------------------------------------------------------------------------------##
## OPTION: the dataframe can also be seperated into individual ARM sampling sites
## ARM size SITE frequency data
## subset data into ARM sampling sites
list_arms.sl.site <- split(arms.sl, arms.sl$site)
names(list_arms.sl.site)
arms.sl.sites <- c("arms.sl.BET", "arms.sl.BRB", "arms.sl.BRS", "arms.sl.GAR",
"arms.sl.GEO", "arms.sl.INN", "arms.sl.OUT", "arms.sl.SEY")
for (i in 1:length(list_arms.sl.site)) {
assign(arms.sl.sites[i], list_arms.sl.site[[i]])
}
## save a copy of the R files
saveRDS(list_arms.sl.site, 'C:/CloudStor/R_Stuff/FIS/list_arms.sl.site.RDS')
##--------------------------------------------------------------------------------------##
## ARM density data ####
## determine the surface area of the ARM (diameter = 400 mm)
platearea <- pi*0.2^2
## create unique ID/index for each ARM and survdate combination
arms.df.2$survindex <- as.factor(paste(arms.df.2$site,
arms.df.2$survdate,
arms.df.2$string,
arms.df.2$plate, sep="_"))
## subset and count number of animals per ARM by survdate
dat <- arms.df.2 %>%
mutate(sllength = as.numeric(as.character(sllength))) %>%
filter(is.nan(sllength) | !is.na(sllength)) %>%
group_by(survindex) %>%
summarise(ab_n=n()) %>%
complete(survindex, fill = list(ab_n = 0)) %>%
as.data.frame()
## calculate abs per square metre
dat$absm <- dat$ab_n * (1/platearea)
## unpack survindex variables and create new dataframe
arm.counts <- data.frame(separate(dat, survindex, sep = "_",
into = c("site", "survdate", "string","plate"),
convert = TRUE), dat$survindex, dat$ab_n, dat$absm)
## format date variable and add year/season variables
arm.counts$survdate <- as.Date(strptime(arm.counts$survdate, "%Y-%m-%d"))
arm.counts$sampyear <- as.factor(year(arm.counts$survdate))
arm.counts$season <- getSeason(arm.counts$survdate)
## recode autumn samples as summer
arm.counts$season <- gsub( "Autumn", "Summer", arm.counts$season)
arm.counts$season <- as.factor(arm.counts$season)
arm.counts$season <- ordered(arm.counts$season, levels=c("Summer","Winter","Spring"))
## create variable identifying year and season
arm.counts$yr.season <- interaction(arm.counts$sampyear,arm.counts$season)
arm.counts$yr.season <-
ordered(arm.counts$yr.season, levels = c("2015.Summer", "2015.Winter", "2015.Spring",
"2016.Summer", "2016.Winter", "2016.Spring",
"2017.Summer", "2017.Winter", "2017.Spring",
"2018.Summer", "2018.Winter", "2018.Spring",
"2019.Summer", "2019.Winter", "2019.Spring",
'2020.Summer', '2020.Spring', '2021.Summer',
'2021.Spring', '2022.Summer'))
## recode Gardens 2015.summer samples as 2015.spring
pick <- which(arm.counts$site == "GAR")
arm.counts$yr.season[pick] <- gsub( "2015.Summer", "2015.Spring",
arm.counts$yr.season[pick])
arm.counts$yr.season <- droplevels(arm.counts$yr.season)
## save a copy of the R files
saveRDS(arm.counts, 'C:/CloudStor/R_Stuff/FIS/arm.counts.RDS')
##--------------------------------------------------------------------------------------##
## OPTION: the dataframe can also be seperated into different size classes to generate
## lagged association plots with adults abalone counts (see ARM and LEG plots)
## Legal and sub-legal leg counts 2
## Filter for juvenile abalone approx. 6-months old (i.e. <25 mm)
arms.dat.juv <- arms.df.2 %>%
filter(sllength <= 25) %>%
group_by(survindex) %>%
summarise(ab_n = n()) %>% #as.data.frame()
complete(survindex, fill = list(ab_n = 0)) %>%
as.data.frame()
## join dataframes created above for survindex
arms.counts.join <- dplyr::rename(arm.counts, survindex = dat.survindex) %>%
left_join(., arms.dat.juv, by = 'survindex')
## rename variables to identify abcounts from each size class
arms.counts.join <- dplyr::rename(arms.counts.join, ab_n = ab_n.x)
arms.counts.join <- dplyr::rename(arms.counts.join, ab_n_juv = ab_n.y)
## calculate abs per square metre for joint dataframe
arms.counts.join$absm_juv <- arms.counts.join$ab_n_juv/platearea
## save a copy of the R files
saveRDS(arms.counts.join, 'C:/CloudStor/R_Stuff/FIS/arms.counts.join.RDS')
##--------------------------------------------------------------------------------------##
## ARM:LEG combine data ####
## For the 2018 Abalone Stock Assessment Report ARM and LEG data were combined so that both
## sources of size data could be displayed on the one figure/plot.
## ARM:LEG density data combined ####
## load most recent juvenile and adult data sets
leg.counts <- readRDS('C:/CloudStor/R_Stuff/FIS/leg.counts.RDS')
arm.counts <- readRDS('C:/CloudStor/R_Stuff/FIS/arm.counts.RDS')
## add column to identify FIS and ARM data
leg.counts$sampmethod <- 'LEG'
arm.counts$sampmethod <- 'ARM'
## rename absm columns to make unique for ARM or LEG
arm.counts <- dplyr::rename(arm.counts, absm.arm = absm)
leg.counts <- dplyr::rename(leg.counts, absm.leg = absm)
## convert arm.counts survdate to POSIXct
arm.counts$survdate <- as.Date(strptime(arm.counts$survdate, "%Y-%m-%d"))
## convert sampyear to factor from leg.counts df
leg.counts$sampyear <- as.factor(leg.counts$sampyear)
arm.counts$string <- as.factor(arm.counts$string)
## join FIS and ARM data
leg.counts <- leg.counts %>%
dplyr::select(-yr.season)
arm.counts <- arm.counts %>%
dplyr::select(-yr.season)
arm.leg.counts <- dplyr::bind_rows(leg.counts, arm.counts)
arm.leg.counts <- arm.leg.counts %>%
mutate(yr.season = paste(sampyear, season, sep = '.'))
## save a copy of the R files
saveRDS(arm.leg.counts, 'C:/CloudStor/R_Stuff/FIS/arm.leg.counts.RDS')
##--------------------------------------------------------------------------------------##
## ARM:LEG size data combined ####
## load most recent ARM and LEG size frequency data sets
legs.sl <- readRDS('C:/CloudStor/R_Stuff/FIS/legs.sl.RDS')
arms.sl <- readRDS('C:/CloudStor/R_Stuff/FIS/arms.sl.RDS')
## convert sampyear to factor
arms.sl$sampyear <- as.factor(arms.sl$sampyear)
legs.sl$sampyear <- as.factor(legs.sl$sampyear)
# add column to identify ARM and LEG data
legs.sl$sampmethod <- 'LEG'
arms.sl$sampmethod <- 'ARM'
## rename shell length columns to make unique for ARM or LEG
arms.sl <- dplyr::rename(arms.sl, sllength.arm = sllength)
legs.sl <- dplyr::rename(legs.sl, sllength.leg = sllength)
## convert abcounts survdate to POSIXct
# legs.sl$survdate <- as.Date(strptime(legs.sl$survdate, "%Y-%m-%d"))
# legs.sl$survdate <- as.Date(strptime(legs.sl$survdate, "%Y-%m-%d"))
# join ARM and LEG data
legs.sl <- legs.sl %>%
dplyr::select(-yr.season)
arms.sl <- arms.sl %>%
dplyr::select(-yr.season)
arm.leg.sl <- bind_rows(legs.sl, arms.sl)
arm.leg.sl <- arm.leg.sl %>%
mutate(yr.season = paste(sampyear, season, sep = '.'))
## save a copy of the R files
saveRDS(arm.leg.sl, 'C:/CloudStor/R_Stuff/FIS/arm.leg.sl.RDS')
##--------------------------------------------------------------------------------------##
## Plots ####
##--------------------------------------------------------------------------------------##
## Create some abbreviated x-axis labels for plots where there
## is a long time series of data and potential for labels to appear squashed.
## create short label names for plot facets and axis
season_labels <- c("2015.Summer" = '2015.Su',
"2015.Winter" = '2015.Wi',
"2015.Spring" = '2015.Sp',
"2016.Summer" = '2016.Su',
"2016.Winter" = '2016.Wi',
"2016.Spring" = '2016.Sp',
"2017.Summer" = '2017.Su',
"2017.Winter" = '2017.Wi',
"2017.Spring" = '2017.Sp',
"2018.Summer" = '2018.Su',
"2018.Winter" = '2018.Wi',
"2018.Spring" = '2018.Sp',
"2019.Summer" = '2019.Su',
"2019.Winter" = '2019.Wi',
"2019.Spring" = '2019.Sp',
"2020.Summer" = '2020.Su',
"2020.Winter" = '2020.Wi',
"2020.Spring" = '2020.Sp',
"2021.Summer" = '2021.Su')
##--------------------------------------------------------------------------------------##
## Size freq plot ####
## load most recent combined ARM and LEG size frequency data set
arm.leg.sl <- readRDS('C:/CloudStor/R_Stuff/FIS/arm.leg.sl.RDS')
## exract unique sites and yr.seasons
arm.leg.sites <- unique(arm.leg.sl$site)
arm.leg.seasons <- data.frame(yr.season = unique(arm.leg.sl$yr.season)) %>%
add_row(yr.season = '2020.Winter')
plot.seasons <- c("2017.Spring", "2019.Winter",
"2018.Summer", "2019.Spring",
"2018.Winter", "2020.Summer",
"2018.Spring", "2020.Winter",
"2019.Summer", "2020.Spring")
plot.sites <- c("BRS", "BRB", "GEO", "BET")
## loop through sites to generate arm and leg plots autonomously
for (i in plot.sites){
## subset site data
arm.leg.site <- subset(arm.leg.sl, site == i &
yr.season %in% plot.seasons &
site %in% plot.sites)
## re-order data so that facet plots in vertical order of two columns
arm.leg.site$yr.season <- factor(arm.leg.site$yr.season,
levels = c("2017.Spring", "2019.Winter",
"2018.Summer", "2019.Spring",
"2018.Winter", "2020.Summer",
"2018.Spring", "2020.Winter",
"2019.Summer", "2020.Spring"))
## generate a summary table for chosen site to add counts to plots (i.e. n = xxx)
plot.n.LEG <- arm.leg.site %>%
filter(sampmethod == 'LEG') %>%
group_by(yr.season) %>%
summarise(n = paste('n =', n()))
plot.n.ARM <- arm.leg.site %>%
filter(sampmethod == 'ARM') %>%
group_by(yr.season) %>%
summarise(n = paste('n =', n()))
## generate dataframe to annotate 'no data' for missing seasons
arm.leg.summary <- arm.leg.site %>%
group_by(yr.season, sampmethod) %>%
summarise(n.sl = n()) %>%
as.data.frame()
ann_text <- left_join(arm.leg.seasons, arm.leg.summary, by = 'yr.season') %>%
filter(yr.season %in% plot.seasons) %>%
mutate(lab = if_else(is.na(n.sl), 'NO DATA', NA_character_),
x = 90,
y = 40) %>%
filter(!is.na(lab)) %>%
mutate(yr.season = factor(yr.season)) %>%
dplyr::select(c(yr.season, lab, x, y))
## re-order and convert yr.season to factor
ann_text$yr.season <- factor(ann_text$yr.season,
levels = c("2017.Spring", "2019.Winter",
"2018.Summer", "2019.Spring",
"2018.Winter", "2020.Summer",
"2018.Spring", "2020.Winter",
"2019.Summer", "2020.Spring"))
## generate plot using 'if...else' statement to determine plot type depending on
## whether a site has ARMs installed
arm.leg.plot <- if((nrow(plot.n.ARM) == 0 & length(names(plot.n.ARM)) == 0)){
ggplot(data = arm.leg.site)+
geom_histogram(aes(x = sllength.leg, y = ..count..), binwidth = 2, fill = 'blue')+
geom_histogram(aes(x = sllength.arm, y = -..count..), binwidth = 2, fill = 'red')+
facet_wrap(. ~ yr.season, ncol = 2, drop = F)+
theme_bw()+
ylab("Frequency") +
xlab("Shell Length (mm)")+
coord_cartesian(ylim = c(-25, 50), xlim = c(0, 180))+
geom_hline(yintercept = 0, size = 0.1)+
geom_text(data = ann_text, aes(x = x, y = y, label = lab))+
geom_text(data = plot.n.LEG, aes(x = 160, y = 50, label = n),
colour = 'black', inherit.aes = F, parse = F, size = 3.5)+
geom_vline(aes(xintercept = 138),colour = 'red', linetype = 'dashed', size = 0.5)+
theme(legend.position = 'none')
} else {
ggplot(data = arm.leg.site)+
geom_histogram(aes(x = sllength.leg, y = ..count..), binwidth = 2, fill = 'blue')+
geom_histogram(aes(x = sllength.arm, y = -..count..), binwidth = 2, fill = 'red')+
facet_wrap(. ~ yr.season, ncol = 2, drop = F)+
theme_bw()+
ylab("Frequency") +
xlab("Shell Length (mm)")+
coord_cartesian(ylim = c(-25, 50), xlim = c(0, 180))+
geom_hline(yintercept = 0, size = 0.1)+
geom_text(data = ann_text, aes(x = x, y = y, label = lab))+
geom_text(data = plot.n.LEG, aes(x = 160, y = 50, label = n),
colour = 'black', inherit.aes = F, parse = F, size = 3.5)+
geom_text(data = plot.n.ARM, aes(x = 10, y = -25, label = if_else(n == 'n = 0', '', n)),
colour = 'black', inherit.aes = F, parse = F, size = 3.5, na.rm = F)+
geom_vline(aes(xintercept = 138),colour = 'red', linetype = 'dashed', size = 0.5)+
theme(legend.position = 'none')
}
# print(arm.leg.plot)
setwd('C:/CloudStor/R_Stuff/FIS/FIS_2020')
ggsave(filename = paste('ARM_LEG_LF_', i, '_2mm', '.pdf', sep = ''),
plot = arm.leg.plot, units = 'mm', width = 190, height = 250)
ggsave(filename = paste('ARM_LEG_LF_', i, '_2mm', '.png', sep = ''),
plot = arm.leg.plot, units = 'mm', width = 190, height = 250)
}
##--------------------------------------------------------------------------------------##
## Size freq powerpoint ####
## load most recent combined ARM and LEG size frequency data set
arm.leg.sl <- readRDS('C:/CloudStor/R_Stuff/FIS/arm.leg.sl.RDS')
## subset chosen site and last four sampling seasons for powerpoint presentation
unique(arm.leg.sl$site)
selected.site <- 'BRS'
selected.season <- c('2019.Summer', '2019.Winter', '2019.Spring', '2020.Summer')
arm.leg.site.lastfour <- arm.leg.sl %>%
filter(site %in% selected.site & yr.season %in% selected.season)
## re-order data so that facet plots in vertical order of two columns
arm.leg.site.lastfour$yr.season <- factor(arm.leg.site.lastfour$yr.season,
levels = c("2019.Summer",
"2019.Winter",
"2019.Spring",
"2020.Summer"))
## generate a summary table for chosen site to add counts to plots (i.e. n = xxx)
plot.n.LEG.lastfour <- arm.leg.site.lastfour %>%
filter(sampmethod == 'LEG') %>%
group_by(yr.season) %>%
summarise(n = paste('n =', n()))
plot.n.ARM.lastfour <- arm.leg.site.lastfour %>%
filter(sampmethod == 'ARM') %>%
group_by(yr.season) %>%
summarise(n = paste('n =', n()))
## manually generate dataframe to annotate 'no data' for missing seasons
# ann_text <- data.frame(x = 90, y = 40,
# lab = 'NO DATA',
# yr.season = c('2018.Spring', '2019.Summer'))
arm.leg.plot.lastfour <- ggplot(data = arm.leg.site.lastfour)+
geom_histogram(aes(x = sllength.leg, y = ..count..), binwidth = 10, fill = 'blue')+
geom_histogram(aes(x = sllength.arm, y = -..count..), binwidth = 10, fill = 'red')+
facet_wrap(. ~ yr.season, ncol = 1, drop = F)+
theme_bw()+
ylab("Frequency") +
xlab("Shell Length (mm)")+
coord_cartesian(ylim = c(-50, 115), xlim = c(0, 180))+
geom_hline(yintercept = 0, size = 0.1)+
# geom_text(data = ann_text, aes(x = x, y = y, label = lab))+
geom_text(data = plot.n.LEG.lastfour, aes(x = 160, y = 50, label = n),
colour = 'black', inherit.aes = F, parse = F, size = 3.5)+
geom_text(data = plot.n.ARM.lastfour, aes(x = 10, y = -30, label = n),
colour = 'black', inherit.aes = F, parse = F, size = 3.5)+
geom_vline(aes(xintercept = 138),colour = 'red', linetype = 'dashed', size = 0.5)
# print(arm.leg.plot.lastfour)
#setwd('C:/CloudStor/R_Stuff/FIS/FIS_2020')
ggsave(filename = paste('ARM_LEG_LF_LASTFOUR_', selected.site, '.pdf', sep = ''),
plot = arm.leg.plot.lastfour, units = 'mm', width = 190, height = 250)
ggsave(filename = paste('ARM_LEG_LF_LASTFOUR_', selected.site, '.wmf', sep = ''),
plot = arm.leg.plot.lastfour, units = 'mm', width = 190, height = 250)
ggsave(filename = paste('ARM_LEG_LF_LASTFOUR_', selected.site, '.png', sep = ''),
plot = arm.leg.plot.lastfour, units = 'mm', width = 190, height = 250)
##--------------------------------------------------------------------------------------##
## Density plot ####
## load most recent combined ARM and LEG size frequency data set
arm.leg.counts <- readRDS('C:/CloudStor/R_Stuff/FIS/arm.leg.counts.RDS')
## exract unique sites and yr.seasons
arm.leg.sites <- unique(arm.leg.counts$site)
arm.leg.seasons <- data.frame(yr.season = unique(arm.leg.counts$yr.season))
## loop through sites to generate arm and leg plots autonomously
for (i in plot.sites){
## subset site data
arm.leg.site.den <- subset(arm.leg.counts, site == i)
# ## subset chosen site
# unique(arm.leg.counts$site)
# selected.site <- 'GAR'
# arm.leg.site.den <- subset(arm.leg.counts, site %in% selected.site)
## re-order data so that yr.season is in sequence
arm.leg.site.den$string <- factor(as.integer(arm.leg.site.den$string), levels = c(1,2))
arm.leg.site.den$yr.season <-
ordered(arm.leg.site.den$yr.season, levels = c("2015.Summer", "2015.Winter",
"2015.Spring", "2016.Summer",
"2016.Winter", "2016.Spring",
"2017.Summer", "2017.Winter",
"2017.Spring", "2018.Summer",
"2018.Winter", "2018.Spring",
"2019.Summer", "2019.Winter",
'2019.Spring', '2020.Summer',
'2020.Winter', '2020.Spring',
'2021.Summer'))
## summarise data for arm and leg density
leg.summ <- arm.leg.site.den %>%
filter(sampmethod == 'LEG') %>%
group_by(string, yr.season) %>%
summarise(leg_mean = mean(absm.leg),
leg_n = n(),
leg_se = sd(absm.leg)/sqrt(leg_n))
arm.summ <- arm.leg.site.den %>%
filter(sampmethod == 'ARM') %>%
group_by(string, yr.season) %>%
summarise(arm_mean = mean(absm.arm),
arm_n = n(),
arm_se = sd(absm.arm)/sqrt(arm_n))
## arm density plot
arm_den <- ggplot()+
geom_line(data = arm.summ, aes(x = yr.season, y = arm_mean, group = factor(string),
linetype = string), position = position_dodge(0.5), colour = 'red')+
geom_point(data = arm.summ, aes(x = yr.season, y = arm_mean, group = factor(string),
colour = string), size = 3, position = position_dodge(0.5),
colour = 'red')+
geom_errorbar(data = arm.summ, aes(x = yr.season,
ymin = arm_mean - arm_se, ymax = arm_mean + arm_se,
group = factor(string), colour = string),
position = position_dodge(0.5), width = 0.1, colour = 'red')+
# ylab(bquote('ARM Density ('*~m^2*')'))+
ylab(bquote('ARM Density (no. '*~m^-2*')')) +
scale_x_discrete(labels = season_labels, drop = F)+
scale_color_manual(values = c('red'))+
theme_bw()+
#theme(legend.position = c(0.1, 0.9), legend.direction = 'vertical')+
theme(legend.position = 'none')+
labs(col = 'String')+
#xlab("Season")+
xlab(NULL)+
coord_cartesian(ylim = c(0, 85))
## leg density plot
leg_den <- ggplot()+
geom_line(data = leg.summ, aes(x = yr.season, y = leg_mean, group = factor(string),
linetype = string), position = position_dodge(0.5), colour = 'blue')+
geom_point(data = leg.summ, aes(x = yr.season, y = leg_mean, group = factor(string),
colour = string), size = 3, position = position_dodge(0.5),
colour = 'blue')+
geom_errorbar(data = leg.summ, aes(x = yr.season,
ymin = leg_mean - leg_se, ymax = leg_mean + leg_se, group = factor(string), colour = string), position = position_dodge(0.5), width = 0.1, colour = 'blue')+
# ylab(bquote('LEG Density ('*~m^2*')'))+
ylab(bquote('LEG Density (no. '*~m^-2*')')) +
scale_x_discrete(labels = season_labels, drop = F)+
scale_color_manual(values = c('blue'))+
theme_bw()+
theme(legend.position = 'none')+
labs(col = 'String')+
xlab("Season")+
coord_cartesian(ylim = c(0, 3))+
theme(axis.text.x = element_text(angle = 30, hjust = 1))
## combine arm and leg plot on the same page
arm.leg.den <- grid.arrange(