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FIS_TimedSwim_DataCompile.R
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##---------------------------------------------------------------------------##
# clear console
rm(list = ls())
## 1. Load libraries ####
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(janitor)
library(anytime)
library(stringr)
library(broom)
library(purrr)
library(sf)
library(ggspatial)
library(tmap)
library(sf)
library(sp)
# library(rgdal)
library(RColorBrewer)
library(viridis)
library(ggpmisc)
library(arsenal)
library(fuzzyjoin)
library(tidytext)
})
source("C:/GitCode/AbResearch/getLegend.r")
source("C:/GitCode/AbResearch/StandardError_Functions.r")
##---------------------------------------------------------------------------##
## 2. Set sample year and file paths ####
# identify sampling year of interest
samp.year <- 2024
# identify associated sampling year folder path to save dataframes
# samp.year.folder <- file.path('C:', 'CloudStor', 'DiveFisheries',
# 'Abalone', 'FISdata',
# paste('FIS_TimedSwimSurveys', samp.year, sep = ''))
samp.year.folder <- file.path(paste(sprintf('C:/Users/%s/Dropbox (UTAS Research)/DiveFisheries/Abalone/FISdata',
Sys.info()[["user"]])), paste('FIS_TimedSwimSurveys', samp.year, sep = ''))
# identify associated sampling year folder path to save plots
# ts.plots.folder <- file.path('C:', 'CloudStor', 'DiveFisheries',
# 'Abalone', 'Assessment', 'Figures', 'FIS',
# paste('FIS_TimedSwimSurvey', samp.year, '_Plots', sep = ''))
ts.plots.folder <- file.path(paste(sprintf('C:/Users/%s/Dropbox (UTAS Research)/DiveFisheries/Abalone/Assessment/Figures/FIS',
Sys.info()[["user"]])), paste('FIS_TimedSwimSurveys', samp.year, '_Plots', sep = ''))
##---------------------------------------------------------------------------##
## 3. Load raw data ####
# load timed swim length frequency raw data
time.swim.dat <- read.xlsx("R:/TAFI/TAFI_MRL_Sections/Abalone/Section Shared/Abalone_databases/Data/Data for Transfer/2020/FIS_TimedSwim_RawData_2020.xlsx",
detectDates = T)
# load timed swim meta data
time.swim.meta.dat <- read.xlsx("R:/TAFI/TAFI_MRL_Sections/Abalone/Section Shared/Abalone_databases/Data/Data for Transfer/2020/FIS_TimedSwim_MetaData_2020.xlsx",
detectDates = T)
##---------------------------------------------------------------------------##
# remove blank or additional rows from raw data where cells have been pre-filled
# for data entry.This step can be deleted once data entry is complete.
time.swim.dat <- time.swim.dat %>%
filter(!is.na(starttime) &
!is.na(finishtime) &
!is.na(sizeclass_freq))
##---------------------------------------------------------------------------##
# clean site data by removing white space from Excel data entry
time.swim.dat <- time.swim.dat %>%
mutate(site = str_trim(site))
time.swim.meta.dat <- time.swim.meta.dat %>%
mutate(site = str_trim(site))
##---------------------------------------------------------------------------##
## 4. Raw data compile ####
# re-format Excel times for time.swim.dat
time.swim.dat$starttime <- convertToDateTime(time.swim.dat$starttime, origin = "1970-01-01", tz = "Australia/HOBART")
time.swim.dat$firstabtime <- convertToDateTime(time.swim.dat$firstabtime, origin = "1970-01-01", tz = "Australia/HOBART")
time.swim.dat$finishtime <- convertToDateTime(time.swim.dat$finishtime, origin = "1970-01-01", tz = "Australia/HOBART")
time.swim.dat <- time.swim.dat %>%
mutate(starttime = strftime(starttime, format="%H:%M:%S"),
firstabtime = strftime(firstabtime, format="%H:%M:%S"),
finishtime = strftime(finishtime, format="%H:%M:%S"))
time.swim.dat$starttime <- as.POSIXct(paste(time.swim.dat$sampdate, time.swim.dat$starttime), format = "%Y-%m-%d %H:%M:%S")
time.swim.dat$firstabtime <- as.POSIXct(paste(time.swim.dat$sampdate, time.swim.dat$firstabtime), format = "%Y-%m-%d %H:%M:%S")
time.swim.dat$finishtime <- as.POSIXct(paste(time.swim.dat$sampdate, time.swim.dat$finishtime), format = "%Y-%m-%d %H:%M:%S")
# re-format Excel times for time.swim.meta.dat
time.swim.meta.dat$time.in <- convertToDateTime(time.swim.meta.dat$time.in, origin = "1970-01-01", tz = "Australia/HOBART")
time.swim.meta.dat$time.out <- convertToDateTime(time.swim.meta.dat$time.out, origin = "1970-01-01", tz = "Australia/HOBART")
time.swim.meta.dat <- time.swim.meta.dat %>%
mutate(time.in = strftime(time.in, format="%H:%M:%S"),
time.out = strftime(time.out, format="%H:%M:%S"))
time.swim.meta.dat$time.in <- as.POSIXct(paste(time.swim.meta.dat$date, time.swim.meta.dat$time.in), format = "%Y-%m-%d %H:%M:%S")
time.swim.meta.dat$time.out <- as.POSIXct(paste(time.swim.meta.dat$date, time.swim.meta.dat$time.out), format = "%Y-%m-%d %H:%M:%S")
time.swim.meta.dat <- time.swim.meta.dat %>%
dplyr::rename('sampdate' = date,
'starttime' = time.in,
'finishtime' = time.out) %>%
mutate(site = gsub('AB-22-Random1', 'WP-72-73', site),
site = gsub('AB-22-Random2', 'WP-74-75', site))
# calculate elapsed dive time (seconds)
time.swim.dat <- time.swim.dat %>%
mutate(time.elapsed = ifelse(is.na(firstabtime), finishtime - starttime, finishtime - firstabtime))
# add sample year
time.swim.dat <- time.swim.dat %>%
mutate(sampyear = year(sampdate))
time.swim.dat <- time.swim.dat %>%
mutate(nums = str_count(site, '\\d+')) %>%
# mutate(site2 = site) %>%
mutate(site2 = ifelse(nums == 2, site, '')) %>%
separate(col = site2, into = c('ab', 'blockno'), sep = '-') %>%
mutate(site3 = ifelse(nums == 3, site, '')) %>%
separate(col = site3, into = c('ab', 'sampyear2', 'blockno2'), sep = '-') %>%
mutate(blockno = ifelse(blockno %in% c(72, 74, 'LEG', 'THU'), 22,
ifelse(blockno %in% c('BRS'), 13, blockno))) %>%
mutate(blockno = ifelse(is.na(blockno), blockno2, blockno)) %>%
dplyr::select(-c(ab, sampyear2, blockno2, nums))
# determine legal and sub-legal abalone
time.swim.dat <- time.swim.dat %>%
mutate(legal.size = ifelse(sizeclass %in% c("0-60", "60-100", "0-100", "0-20", "20-40", "40-60", "60-80", "80-100", "100-120", "120-140"), '<140 mm', '>140 mm'))
# add mid-point to 2020 size classes; add 2021 size classes and add midpoint
time.swim.dat <- time.swim.dat %>%
separate(sizeclass, into = c('minsize', 'maxsize'), sep = '-', convert = TRUE, remove = F) %>%
mutate(midsize = (minsize + maxsize)/2,
sizeclass.2021 = ifelse(maxsize <= 100, '0-100', sizeclass),
midsize.2021 = ifelse(maxsize <= 100, 50, midsize))
# create data frame of individual abalone lengths
time.swim.dat.df <- time.swim.dat %>%
uncount(sizeclass_freq, .remove = F) %>%
dplyr::rename('shelllength' = midsize,
'shelllength.2021' = midsize.2021)
# manually add vessel name to metadata
time.swim.meta.dat <- time.swim.meta.dat %>%
mutate(vesselname = ifelse(sampdate == as.Date('2021-10-08'), 'Taroona', 'MoranaII'))
# save dataframes
saveRDS(time.swim.dat, paste(samp.year.folder, '/time.swim.dat.RDS', sep = ''))
saveRDS(time.swim.dat.df, paste(samp.year.folder, '/time.swim.dat.df.RDS', sep = ''))
saveRDS(time.swim.meta.dat, paste(samp.year.folder, '/time.swim.meta.dat.RDS', sep = ''))
##---------------------------------------------------------------------------##
## 5. Raw data check ####
#quick check that sites match between raw and meta data
# create summary data frames of sites sampled by date
ts.dat.sites <- time.swim.dat %>%
dplyr::select(sampdate, site) %>%
distinct_all()
ts.meta.dat.sites <- time.swim.meta.dat %>%
dplyr::select(sampdate, site) %>%
filter(!site %in% c('AB-22-4-A', 'Betsey')) %>%
distinct_all()
# compare data frames to see if the site-date combinations match
summary(arsenal::comparedf(ts.dat.sites, ts.meta.dat.sites, by = c('sampdate', 'site')))
##---------------------------------------------------------------------------##
## 6. Vessel GPS data compile ####
## load data from vessel GPS downloads and match to raw data
# define CRS
GDA2020 <- st_crs(7855)
# read GPX files from vessel plotter
gps_downloads_folder <- paste(sprintf("C:/Users/%s/OneDrive - University of Tasmania/IMAS-DiveFisheries-FIS-Data/FIS_VesselGPS_Downloads", Sys.info()[["user"]]))
# vessel data for 2020 surveys
morana.gps.2020 <- st_read(file.path(gps_downloads_folder, 'MORANAII-2020-10-09_download.gpx'), layer = 'waypoints')
# vessel data for 2021 additional surveys (i.e. block 13-14)
morana.gps.ref <- st_read(file.path(gps_downloads_folder, 'MORANAII-2021-04-06_download.gpx'), layer = 'waypoints')
# vessel data for 2021 surveys
morana.gps.2021.a <- st_read(file.path(gps_downloads_folder, 'MORANAII-2021-07-15_download.gpx'), layer = 'waypoints')
morana.gps.2021.b <- st_read(file.path(gps_downloads_folder, 'MORANAII-2021-08-12_download.gpx'), layer = 'waypoints')
morana.gps.2021.c <- st_read(file.path(gps_downloads_folder, 'MORANAII-2021-09-08_download.gpx'), layer = 'waypoints')
morana.gps.2021.d <- st_read(file.path(gps_downloads_folder, 'MORANAII-2021-10-07_download.gpx'), layer = 'waypoints')
taroona.gps.2021.a <- st_read(file.path(gps_downloads_folder, 'TAROONA-2021-10-15_download.gpx'), layer = 'waypoints')
morana.gps.2021 <- bind_rows(morana.gps.2021.a, morana.gps.2021.b, morana.gps.2021.c,
morana.gps.2021.d) %>%
distinct(., name, .keep_all = T)
# vessel data for 2022 surveys
morana.gps.2022.a <- st_read(file.path(gps_downloads_folder, 'MORANAII-2022-08-10_download.gpx'), layer = 'waypoints')
morana.gps.2022.b <- st_read(file.path(gps_downloads_folder, 'MORANAII-2022-09-06_download.gpx'), layer = 'waypoints')
morana.gps.2022.c <- st_read(file.path(gps_downloads_folder, 'MORANAII-2022-09-27_download.gpx'), layer = 'waypoints')
morana.gps.2022 <- bind_rows(morana.gps.2022.a, morana.gps.2022.b, morana.gps.2022.c) %>%
distinct(., name, .keep_all = T)
# vessel data for 2023 surveys
morana_gps_2023_a <- st_read(file.path(gps_downloads_folder, 'MORANAII-2023-03-27_download.gpx'), layer = 'waypoints')
morana_gps_2023_b <- st_read(file.path(gps_downloads_folder, 'MORANAII-2023-06-24_download.gpx'), layer = 'waypoints')
morana_gps_2023_c <- st_read(file.path(gps_downloads_folder, 'MORANAII-2023-07-17_download.gpx'), layer = 'waypoints')
morana_gps_2023_d <- st_read(file.path(gps_downloads_folder, 'MORANAII-2023-07-19_download.gpx'), layer = 'waypoints')
morana_gps_2023_e <- st_read(file.path(gps_downloads_folder, 'MORANAII-2023-08-03_download.gpx'), layer = 'waypoints')
morana_gps_2023_f <- st_read(file.path(gps_downloads_folder, 'MORANAII-2023-08-15_download.gpx'), layer = 'waypoints')
morana_gps_2023_g <- st_read(file.path(gps_downloads_folder, 'MORANAII-2023-08-30_download.gpx'), layer = 'waypoints')
morana_gps_2023_h <- st_read(file.path(gps_downloads_folder, 'MORANAII-2023-09-07_download.gpx'), layer = 'waypoints')
morana_gps_2023_i <- st_read(file.path(gps_downloads_folder, 'MORANAII-2023-09-19_download.gpx'), layer = 'waypoints')
morana_gps_2023_j <- st_read(file.path(gps_downloads_folder, 'MORANAII-2023-09-25_download.gpx'), layer = 'waypoints')
morana_gps_2023_k <- st_read(file.path(gps_downloads_folder, 'MORANAII-2023-10-02_download.gpx'), layer = 'waypoints')
morana_gps_2023_l <- st_read(file.path(gps_downloads_folder, 'MORANAII-2023-10-19_download.gpx'), layer = 'waypoints')
morana_gps_2023_m <- st_read(file.path(gps_downloads_folder, 'MORANAII-2023-10-31_download.gpx'), layer = 'waypoints')
morana_gps_2023_n <- st_read(file.path(gps_downloads_folder, 'MORANAII-2023-11-21_download.gpx'), layer = 'waypoints')
morana_gps_2023_o <- st_read(file.path(gps_downloads_folder, 'MORANAII-2023-11-30_download.gpx'), layer = 'waypoints') %>%
filter(time >= ymd_hms('2023-09-20 00:00:00'))
morana_gps_2023 <- bind_rows(morana_gps_2023_a,
morana_gps_2023_b,
morana_gps_2023_c,
morana_gps_2023_d,
morana_gps_2023_e,
morana_gps_2023_f,
morana_gps_2023_g,
morana_gps_2023_h,
morana_gps_2023_i,
morana_gps_2023_j,
morana_gps_2023_k,
morana_gps_2023_l,
morana_gps_2023_m,
morana_gps_2023_n,
morana_gps_2023_o) %>%
mutate(gps_date = as.Date(time)) %>%
distinct(., name, gps_date, .keep_all = T)
# vessel data for 2024 surveys
morana_gps_2024_a <- st_read(file.path(gps_downloads_folder, 'MORANAII-2024-02-07_download.gpx'), layer = 'waypoints')
morana_gps_2024_b <- st_read(file.path(gps_downloads_folder, 'MORANAII-2024-03-07_download.gpx'), layer = 'waypoints')
morana_gps_2024_c <- st_read(file.path(gps_downloads_folder, 'MORANAII-2024-03-21_download.gpx'), layer = 'waypoints')
morana_gps_2024_d <- st_read(file.path(gps_downloads_folder, 'MORANAII-2024-07-30_download.gpx'), layer = 'waypoints')
morana_gps_2024_e <- st_read(file.path(gps_downloads_folder, 'MORANAII-2024-08-08_download.gpx'), layer = 'waypoints')
morana_gps_2024_f <- st_read(file.path(gps_downloads_folder, 'MORANAII-2024-08-24_download.gpx'), layer = 'waypoints')
morana_gps_2024_g <- st_read(file.path(gps_downloads_folder, 'MORANAII-2024-09-10_download.gpx'), layer = 'waypoints')
morana_gps_2024_h <- st_read(file.path(gps_downloads_folder, 'MORANAII-2024-09-14_download.gpx'), layer = 'waypoints')
morana_gps_2024_i <- st_read(file.path(gps_downloads_folder, 'MORANAII-2024-09-27_download.gpx'), layer = 'waypoints')
morana_gps_2024_j <- st_read(file.path(gps_downloads_folder, 'MORANAII-2024-10-03_download.gpx'), layer = 'waypoints')
morana_gps_2024 <- bind_rows(morana_gps_2024_a,
morana_gps_2024_b,
morana_gps_2024_c,
morana_gps_2024_d,
morana_gps_2024_e,
morana_gps_2024_f,
morana_gps_2024_g,
morana_gps_2024_h,
morana_gps_2024_i,
morana_gps_2024_j) %>%
mutate(gps_date = as.Date(time)) %>%
distinct(., name, gps_date, .keep_all = T)
# add sample year (note: gps time refers to time waypoint was uploaded or taken,
# therefore DO NOT use this column to determine year. Waypoint numbers should
# always correspond to the actual sample time whereas names will generally
# correspond to an upload time unless they have been manually entered)
morana.gps.2020 <- morana.gps.2020 %>%
mutate(sampyear = 2020,
vesselname = 'MoranaII')
morana.gps.ref <- morana.gps.ref %>%
mutate(sampyear = 2021,
vesselname = 'MoranaII')
morana.gps.2021 <- morana.gps.2021 %>%
mutate(sampyear = 2021,
vesselname = 'MoranaII')
taroona.gps.2021 <- taroona.gps.2021.a %>%
distinct(., name, .keep_all = T) %>%
mutate(sampyear = 2021,
vesselname = 'Taroona',
name = as.numeric(name),
name = if_else(name < 10, as.character(paste('00', name, sep = '')),
if_else(between(name, 10, 99), as.character(paste(0, name, sep = '')),
as.character(name))))
morana.gps.2022 <- morana.gps.2022 %>%
mutate(sampyear = 2022,
vesselname = 'MoranaII')
morana_gps_2023 <- morana_gps_2023 %>%
mutate(sampyear = 2023,
vesselname = 'MoranaII')
morana_gps_2024 <- morana_gps_2024 %>%
mutate(sampyear = 2024,
vesselname = 'MoranaII')
# combine dataframes
vessel.gps <- bind_rows(morana.gps.2020, morana.gps.ref, morana.gps.2021, taroona.gps.2021,
morana.gps.2022, morana_gps_2023, morana_gps_2024) %>%
mutate(gpsdate = as.Date(time),
gpstime = time) %>%
dplyr::select(c(name, sampyear, gpstime, gpsdate, geometry, vesselname))
# load latest compiled timed swim meta dataframe
time.swim.meta.dat <- readRDS(paste(samp.year.folder, '/time.swim.meta.dat.RDS', sep = ''))
# separate start positions
ts.site.start <- time.swim.meta.dat %>%
dplyr::select(-c(waypoint.finish, finishtime)) %>%
dplyr::rename('waypoint' = waypoint.start,
'samptime' = starttime) %>%
mutate(sampperiod = 'start',
sampyear = year(sampdate))
# separate finish positions
ts.site.finish <- time.swim.meta.dat %>%
dplyr::select(-c(waypoint.start, starttime)) %>%
dplyr::rename('waypoint' = waypoint.finish,
'samptime' = finishtime) %>%
mutate(sampperiod = 'finish',
sampyear = year(sampdate))
# re-join start and finish positions and remove non-sampled sites (e.g. Betsey)
ts.site.start.finish <- bind_rows(ts.site.start, ts.site.finish) %>%
mutate(waypoint = if_else(waypoint < 10, as.character(paste('00', waypoint, sep = '')),
if_else(between(waypoint, 10, 99), as.character(paste(0, waypoint, sep = '')),
as.character(waypoint)))) %>%
dplyr::select(c(sampdate, samptime, sampperiod, site, waypoint, sampyear, vesselname)) %>%
filter(site != 'Betsey')
# join geometry where start waypoint recorded as being on the original GPS position/waypoint mark
ts.site.start.finish.mark <- ts.site.start.finish %>%
filter(is.na(waypoint) & sampperiod == 'start') %>%
left_join(., vessel.gps, by = c('site' = 'name', 'sampyear', 'vesselname')) %>%
dplyr::select(c(sampyear, samptime, gpstime, sampperiod, site, waypoint, geometry, vesselname))
# join geometry where start and finish waypoints were recorded
ts.site.start.finish.wp <- ts.site.start.finish %>%
filter(!is.na(waypoint)) %>%
left_join(., vessel.gps, by = c('waypoint' = 'name', 'sampdate' = 'gpsdate', 'vesselname')) %>%
dplyr::rename('sampyear' = sampyear.x) %>%
dplyr::select(c(sampyear, samptime, gpstime, sampperiod, site, waypoint, geometry, vesselname))
# re-join all waypoint data and transform to GDA2020
ts.site.start.finish.loc <- bind_rows(ts.site.start.finish.mark, ts.site.start.finish.wp) %>%
st_as_sf() %>%
st_transform(GDA2020)
vessel.gps.dat <- ts.site.start.finish.loc
# re-combine metadata frame
ts_meta_dat <- bind_rows(ts.site.start, ts.site.finish) %>%
mutate(waypoint = if_else(waypoint < 10, as.character(paste('00', waypoint, sep = '')),
if_else(between(waypoint, 10, 99), as.character(paste(0, waypoint, sep = '')),
as.character(waypoint)))) %>%
dplyr::select(c(sampdate, samptime, sampperiod, site, waypoint, sampyear, vesselname, max.depth,
habitat.type, percent.algae.cover, percent.urchins, urchin.deep, comments)) %>%
filter(site != 'Betsey')
# separate metadata where start waypoint not recorded and join vessel gps data
meta_dat_no_wp <- ts_meta_dat %>%
filter(is.na(waypoint) & sampperiod == 'start') %>%
left_join(., vessel.gps, by = c('site' = 'name', 'sampyear', 'vesselname')) %>%
dplyr::select(c(sampyear, samptime, gpstime, sampperiod, site, waypoint, geometry, vesselname, max.depth,
habitat.type, percent.algae.cover, percent.urchins, urchin.deep, comments))
# separate metadata where start waypoint recorded and join vessel data
meta_dat_wp <- ts_meta_dat %>%
filter(!is.na(waypoint)) %>%
left_join(., vessel.gps, by = c('waypoint' = 'name', 'sampdate' = 'gpsdate', 'vesselname')) %>%
dplyr::rename('sampyear' = sampyear.x) %>%
dplyr::select(c(sampyear, samptime, gpstime, sampperiod, site, waypoint, geometry, vesselname, max.depth,
habitat.type, percent.algae.cover, percent.urchins, urchin.deep, comments))
# recombine metadata and transform geometry
meta_dat_gps <- bind_rows(meta_dat_no_wp, meta_dat_wp) %>%
st_as_sf() %>%
st_transform(GDA2020)
# extract blockno from site name
meta_dat_vessel <- meta_dat_gps %>%
mutate(nums = str_count(site, '\\d+')) %>%
mutate(site2 = ifelse(nums == 2, site, '')) %>%
separate(col = site2, into = c('ab', 'blockno'), sep = '-') %>%
mutate(site3 = ifelse(nums == 3, site, '')) %>%
separate(col = site3, into = c('ab', 'sampyear2', 'blockno2'), sep = '-') %>%
mutate(blockno = ifelse(blockno %in% c(72, 74, 'LEG', 'THU'), 22,
ifelse(blockno %in% c('BRS'), 13, blockno))) %>%
mutate(blockno = ifelse(is.na(blockno), blockno2, blockno)) %>%
dplyr::select(-c(ab, sampyear2, blockno2, nums))
# save files
saveRDS(vessel.gps.dat, paste(samp.year.folder, '/vessel.gps.dat.RDS', sep = ''))
saveRDS(meta_dat_vessel, paste(samp.year.folder, '/meta_dat_vessel.RDS', sep = ''))
# save spatial layer for QGIS
st_write(vessel.gps.dat,
dsn = paste(samp.year.folder, '/vessel.gps.dat_', Sys.Date(), '.gpkg', sep = ''),
layer = "vessel.gps.dat", driver = "GPKG", overwrite = T, delete_dsn = T)
rm(list = setdiff(ls(), c('vessel.gps.dat', 'meta_dat_vessel', 'time.swim.dat.df',
'time.swim.meta.dat', 'time.swim.dat', 'samp.year', 'samp.year.folder',
'ts_meta_dat', 'ts.site.start.finish.loc')))
##---------------------------------------------------------------------------##
## 7. Proposed site data ####
## identify sites sampled based on proposed GPS site data and
## create map of sites sampled within each Block adding subblock to site name
## based on proposed position
## use these data for 'proposed geometry' when joining to raw data
# Combine proposed timed swim sites from all years into one data frame
# load final proposed sites for 2020 and 2021 surveys (i.e. adjusted site names)
ts.sites.final.pre2022 <- readRDS(paste(sprintf("C:/Users/%s/Dropbox (UTAS Research)/DiveFisheries/Abalone/FISdata/FIS_TimedSwimSurveys2021/ts.sites.final.sf.RDS", Sys.info()[["user"]])))
# load final proposed sites for 2022
ts.sites.final.2022 <- read.xlsx(paste(sprintf("C:/Users/%s/Dropbox (UTAS Research)/DiveFisheries/Abalone/FISdata/FIS_TimedSwimSurveys2022/TimedSwimSites_Final2022.xlsx", Sys.info()[["user"]])))
# load final proposed sites for 2023
ts_sites_east_final_2023 <- read.xlsx(paste(sprintf("C:/Users/%s/Dropbox (UTAS Research)/DiveFisheries/Abalone/FISdata/FIS_TimedSwimSurveys2023/TimedSwim_EastCoast/TimedSwimSites_EastCoast_Final2023.xlsx", Sys.info()[["user"]]))) %>% .[,-1]
ts_sites_actaeons_final_2023 <- read.xlsx(paste(sprintf("C:/Users/%s/Dropbox (UTAS Research)/DiveFisheries/Abalone/FISdata/FIS_TimedSwimSurveys2023/TimedSwim_Actaeons/TimedSwimSites_SubBlock13DE_Final2023.xlsx", Sys.info()[["user"]]))) %>% .[,-1]
ts_sites_greens_final_2023 <- read.xlsx(paste(sprintf("C:/Users/%s/Dropbox (UTAS Research)/DiveFisheries/Abalone/FISdata/FIS_TimedSwimSurveys2023/TimedSwim_NEGreens/TimedSwim_NE_GL_2023_50m.xlsx", Sys.info()[["user"]]))) %>% .[,-1] %>% mutate(oid = as.character(oid))
ts_sites_block21_final_2023 <- read.xlsx(paste(sprintf("C:/Users/%s/Dropbox (UTAS Research)/DiveFisheries/GIS/SpatialLayers/TimeSwimLayers/TimedSwimSites_Block21_Final2023.xlsx", Sys.info()[["user"]]))) %>% .[,-1]
ts.sites.final.2023 <- bind_rows(ts_sites_east_final_2023,
ts_sites_actaeons_final_2023,
ts_sites_greens_final_2023,
ts_sites_block21_final_2023)
# load final proposed sites for 2024
ts_sites_block29_final_2024 <- read.xlsx(paste(sprintf("C:/Users/%s/Dropbox (UTAS Research)/DiveFisheries/Abalone/FISdata/FIS_TimedSwimSurveys2024/TimedSwimSites_Block29_Final2024_GPX.xlsx", Sys.info()[["user"]]))) %>%
dplyr::rename('site' = 'name')
ts_sites_east_final_2024 <- read.xlsx(paste(sprintf("C:/Users/%s/Dropbox (UTAS Research)/DiveFisheries/Abalone/FISdata/FIS_TimedSwimSurveys2024/TimedSwimSites_EastCoast_Final_2024.xlsx", Sys.info()[["user"]])))
ts_sites_actaeons_final_2024 <- read.xlsx(paste(sprintf("C:/Users/%s/Dropbox (UTAS Research)/DiveFisheries/Abalone/FISdata/FIS_TimedSwimSurveys2023/TimedSwim_Actaeons/TimedSwimSites_SubBlock13DE_Final2023.xlsx", Sys.info()[["user"]])))
ts_sites_block21_final_2024 <- read.xlsx(paste(sprintf("C:/Users/%s/Dropbox (UTAS Research)/DiveFisheries/GIS/SpatialLayers/TimeSwimLayers/TimedSwimSites_Block21_Final2023.xlsx", Sys.info()[["user"]]))) %>% .[,-1]
ts.sites.final.2024 <- bind_rows(ts_sites_block29_final_2024,
ts_sites_east_final_2024,
ts_sites_actaeons_final_2024,
ts_sites_block21_final_2024)
# set CRS
GDA2020 <- st_crs(7855)
GDA94 <- st_crs(28355)
WGS84 <- st_crs(4326)
# convert 2022 site data to sf
ts.site.2022.sf <- ts.sites.final.2022 %>%
st_as_sf(coords = c("longitude", "latitude"), crs = WGS84) %>%
dplyr::select(c(site, geometry, oid)) %>%
mutate(sampyear = 2022)
# convert 2023 site data to sf
ts.site.2023.sf <- ts.sites.final.2023 %>%
st_as_sf(coords = c("longitude", "latitude"), crs = WGS84) %>%
dplyr::select(c(site, geometry, oid)) %>%
mutate(sampyear = 2023)
# convert 2024 site data to sf
ts.site.2024.sf <- ts.sites.final.2024 %>%
st_as_sf(coords = c("longitude", "latitude"), crs = WGS84) %>%
# dplyr::rename('site' = 'name') %>%
dplyr::select(c(site, geometry, oid)) %>%
mutate(sampyear = 2024)
ts.sites.final.pre2022 <- st_transform(ts.sites.final.pre2022, crs = WGS84)
# combine all years
ts.sites.final.sf <- bind_rows(ts.sites.final.pre2022, ts.site.2022.sf, ts.site.2023.sf, ts.site.2024.sf)
# save final proposed sites up to sample year
saveRDS(ts.sites.final.sf, paste(samp.year.folder, '/ts.sites.final.sf.RDS', sep = ''))
rm(list = setdiff(ls(), c('vessel.gps.dat', 'meta_dat_vessel', 'time.swim.dat.df',
'time.swim.meta.dat', 'time.swim.dat', 'samp.year', 'samp.year.folder',
'ts_meta_dat', 'ts.site.start.finish.loc', 'ts.sites.final.sf')))
##---------------------------------------------------------------------------##
## Join proposed site data to timed swim count data to identify sites sampled
# load timed swim raw dataframe
time.swim.dat <- readRDS(paste(samp.year.folder, '/time.swim.dat.RDS', sep = ''))
# identify unique sites sampled from timed swim dataframe
ts.dat.unique <- time.swim.dat %>%
distinct(site, sampyear, .keep_all = T) %>%
dplyr::select(c(site, sampyear, sampdate))
# join sites sampled to renamed site file for 2020
ts.sites.join.2020 <- ts.sites.final.sf %>%
filter(sampyear == 2020) %>%
left_join(., ts.dat.unique, by = c('site.old' = 'site',
'sampyear' = 'sampyear'))
# join sites sampled to renamed site file for 2021
ts.sites.join.2021 <- ts.sites.final.sf %>%
filter(sampyear == 2021) %>%
left_join(., ts.dat.unique, by = c('site' = 'site',
'sampyear' = 'sampyear'))
# join sites sampled to renamed site file for 2022
ts.sites.join.2022 <- ts.sites.final.sf %>%
filter(sampyear == 2022) %>%
left_join(., ts.dat.unique, by = c('site' = 'site',
'sampyear' = 'sampyear'))
# join sites sampled to renamed site file for 2023
ts.sites.join.2023 <- ts.sites.final.sf %>%
filter(sampyear == 2023) %>%
left_join(., ts.dat.unique, by = c('site' = 'site',
'sampyear' = 'sampyear'))
# join sites sampled to renamed site file for 2024
ts.sites.join.2024 <- ts.sites.final.sf %>%
filter(sampyear == 2024) %>%
left_join(., ts.dat.unique, by = c('site' = 'site',
'sampyear' = 'sampyear'))
# compile joint site files
ts.dat.site.join <- bind_rows(ts.sites.join.2020, ts.sites.join.2021,
ts.sites.join.2022, ts.sites.join.2023,
ts.sites.join.2024)
# identity sites sampled
ts.dat.site.sampled <- ts.dat.site.join %>%
mutate(sampled = ifelse(is.na(sampdate), 0, 1)) %>%
dplyr::select(c(site, site.old, sampled, sampyear, geometry, oid))
# transform to GDA2020
GDA2020 <- st_crs(7855)
ts.dat.site.sampled <- st_transform(ts.dat.site.sampled, GDA2020)
# read in Subblock map as an sf::sfc polygon object
sf.subblock.map <- st_read(paste(sprintf("C:/Users/%s/Dropbox (UTAS Research)/DiveFisheries/GIS/SpatialLayers/SubBlockMaps.gpkg", Sys.info()[["user"]])))
# transform map to GDA2020
sf.subblock.map <- st_transform(sf.subblock.map, GDA2020)
# extract block number from site names
site.samp.loc <- ts.dat.site.sampled %>%
mutate(nums = str_count(site, '\\d+')) %>%
mutate(site2 = ifelse(nums == 2, site, '')) %>%
separate(col = site2, into = c('ab', 'blockno'), sep = '-') %>%
mutate(site3 = ifelse(nums == 3, site, '')) %>%
separate(col = site3, into = c('ab', 'sampyear2', 'blockno2'), sep = '-') %>%
mutate(blockno = ifelse(blockno %in% c(72, 74, 'LEG', 'THU'), 22,
ifelse(blockno %in% c('BRS'), 13, blockno))) %>%
mutate(blockno = ifelse(is.na(blockno), blockno2, blockno)) %>%
dplyr::select(-c(ab, sampyear2, blockno2, nums))
# join data to subblock map to identify subblockno and remove reference sites
# (i.e. these subblocks were open to fishing)
site.samp.subblock.loc <- st_join(site.samp.loc, sf.subblock.map, join = st_nearest_feature) %>%
dplyr::select(-c(version, area, blockno.y)) %>%
dplyr::rename(blockno = blockno.x) %>%
rename_all(tolower) %>%
mutate(subblockno = ifelse(subblockno == '29A' &
sampyear == 2020, '28C',
ifelse(site == 'AB-23-50-A', '23A', subblockno))) %>%
filter(
(!subblockno %in% c('28C', '28B'))&
(!blockno %in% c('13', '14', '29', '30')))
# join data to subblock map to identify subblockno (i.e. inc. all blocks)
site.samp.subblock.loc.ref <- st_join(site.samp.loc, sf.subblock.map, join = st_nearest_feature) %>%
dplyr::select(-c(version, area, blockno.y)) %>%
dplyr::rename(blockno = blockno.x) %>%
rename_all(tolower) %>%
mutate(subblockno = ifelse(subblockno == '29A' &
sampyear == 2020, '28C',
ifelse(site == 'AB-23-50-A', '23A', subblockno)))
# save file of actual geometry for raw data join
ts.proposed.geom <- site.samp.subblock.loc.ref
saveRDS(ts.proposed.geom, paste(samp.year.folder, '/ts.proposed.geom.RDS', sep = ''))
# save spatial layer for QGIS
st_write(ts.proposed.geom,
dsn = paste(samp.year.folder, '/ts.proposed.geom_', Sys.Date(), '.gpkg', sep = ''),
layer = "ts.proposed.geom", driver = "GPKG", overwrite = T, delete_dsn = T)
rm(list = setdiff(ls(), c('vessel.gps.dat', 'meta_dat_vessel', 'time.swim.dat.df',
'time.swim.meta.dat', 'time.swim.dat', 'samp.year', 'samp.year.folder',
'ts_meta_dat', 'ts.site.start.finish.loc', 'ts.sites.final.sf',
'ts.proposed.geom')))
##----------------------------------------------------------------------------##
# # create maps for each block (#note: sample year and/or reference sites)
#
# # select sampling year
# samp.year <- 2023
#
# # identify blocks sampled
# blocks.sampled <- site.samp.loc %>%
# filter(sampyear == samp.year) %>%
# distinct(blockno) %>%
# pull()
#
# # create maps
# for (i in blocks.sampled){
#
# site.samp.loc.blockno <- site.samp.subblock.loc.ref %>%
# filter(blockno == i &
# sampyear == samp.year) %>%
# mutate(sampled = factor(sampled, levels = c('0', '1')))
#
# sub_blocks <- site.samp.loc.blockno %>% distinct(subblockno) %>% pull()
#
# sf.subblock.map.crop.1 <- sf.subblock.map %>%
# filter(blockno == i &
# !subblockno %in% c('28B', '28C'))
#
# sf.subblock.map.crop.2 <- sf.subblock.map %>%
# {if(i == '27') filter(., blockno == '26') else filter(., blockno == i)}
#
# sf.subblock.map.crop.3 <- sf.subblock.map %>%
# {if(i == '16') filter(., blockno %in% c('14', '15')) else filter(., blockno == i)}
#
# sf.subblock.map.crop.4 <- sf.subblock.map %>%
# {if(i == '13') filter(., subblockno %in% c('13D', '13E')) else filter(., blockno == i)}
#
#
# sf.subblock.map.crop <- bind_rows(sf.subblock.map.crop.1, sf.subblock.map.crop.2,
# sf.subblock.map.crop.3, sf.subblock.map.crop.4) %>%
# filter(subblockno %in% sub_blocks)
#
# site.samp.map.blockno <- ggplot(data = st_geometry(sf.subblock.map.crop)) +
# geom_sf(fill = NA) +
# geom_sf_text(data = sf.subblock.map.crop, aes(label = subblockno))+
# geom_sf(data = site.samp.loc.blockno, aes(colour = sampled)) +
# scale_colour_manual(values = c('red', 'blue'))+
# theme_bw() +
# annotation_scale(location = "bl", width_hint = 0.5) +
# annotation_north_arrow(location = "br", which_north = "true",
# pad_x = unit(0.05, "cm"), pad_y = unit(0.1, "cm"),
# style = north_arrow_fancy_orienteering)+
# theme(legend.position="none")+
# xlab('Longitude')+
# ylab('Latitude')
#
# # save plots
# setwd(ts.plots.folder)
# ggsave(filename = paste('TimedSwimSurvey_', samp.year, '_SitesSampled_Proposed_BlockNo_', i, '.pdf', sep = ''),
# plot = site.samp.map.blockno, units = 'mm', width = 190, height = 250)
# ggsave(filename = paste('TimedSwimSurvey_', samp.year, '_SitesSampled_Proposed_BlockNo_', i, '.png', sep = ''),
# plot = site.samp.map.blockno, units = 'mm', width = 190, height = 150)
#
# }
##----------------------------------------------------------------------------##
## 8. Actual site data ####
## Create dataframe of actual sites sampled and generate map of sites sampled within
## each Block with recorded GPS positions and
## add subblock to site name based on vessel GPS data and start position
## use these data for 'actual geometry' when joining to raw data
# load compiled vessel GPS data
vessel.gps.dat <- readRDS(paste(samp.year.folder, '/vessel.gps.dat.RDS', sep = ''))
# add sampdate
vessel.gps.dat <- vessel.gps.dat %>%
mutate(sampdate = as.Date(format(samptime, '%Y-%m-%d')))
# read in Subblock map as an sf::sfc polygon object
sf.subblock.map <- st_read(paste(sprintf("C:/Users/%s/Dropbox (UTAS Research)/DiveFisheries/GIS/SpatialLayers/SubBlockMaps.gpkg", Sys.info()[["user"]])))
# transform map to GDA2020
GDA2020 <- st_crs(7855)
sf.subblock.map <- st_transform(sf.subblock.map, GDA2020)
# extract block number from site names
site.samp.start.loc <- vessel.gps.dat %>%
mutate(nums = str_count(site, '\\d+')) %>%
mutate(site2 = ifelse(nums == 2, site, '')) %>%
separate(col = site2, into = c('ab', 'blockno'), sep = '-') %>%
mutate(site3 = ifelse(nums == 3, site, '')) %>%
separate(col = site3, into = c('ab', 'sampyear2', 'blockno2'), sep = '-', remove = F) %>%
mutate(blockno = ifelse(blockno %in% c(72, 74, 'LEG', 'THU'), 22,
ifelse(blockno %in% c('BRS'), 13, blockno))) %>%
mutate(blockno = ifelse(is.na(blockno), blockno2, blockno)) %>%
dplyr::select(-c(ab, sampyear2, blockno2, nums))
# join data to subblock map to identify subblockno and remove any blocks/sites
# (i.e. these subblocks were open to fishing) and select start position
site.samp.start.subblock.loc <- st_join(site.samp.start.loc, sf.subblock.map, join = st_nearest_feature) %>%
dplyr::select(-c(version, area, blockno.y, site3)) %>%
dplyr::rename(blockno = blockno.x) %>%
rename_all(tolower) %>%
filter((!subblockno %in% c('28C', '28B') & sampperiod == 'start'))
# (!blockno %in% c('13', '14', '29', '30') & sampperiod == 'start'))
# join data to subblock map to identify subblockno and remove any blocks/sites
# (i.e. these subblocks were open to fishing) and select finish position
site.samp.finish.subblock.loc <- st_join(site.samp.start.loc, sf.subblock.map, join = st_nearest_feature) %>%
dplyr::select(-c(version, area, blockno.y, site3)) %>%
dplyr::rename(blockno = blockno.x,
finish.geom = geometry) %>%
rename_all(tolower) %>%
filter(
(!subblockno %in% c('28C', '28B') & sampperiod == 'finish'))
# (!blockno %in% c('13', '14', '29', '30') & sampperiod == 'finish'))
# adjust sub-block number where finish position of vessel has crept over block boundary
site.samp.finish.subblock.loc <- site.samp.finish.subblock.loc %>%
mutate(subblockno = case_when(site == 'AB-2024-23-7' & sampdate == as.Date('2024-07-23') ~ '23A', .default = subblockno))
# re-combine start and finish data
site.samp.start.finish <- left_join(as.data.frame(site.samp.start.subblock.loc),
as.data.frame(site.samp.finish.subblock.loc),
by = c("sampyear", "sampdate", "site",
"vesselname", "blockno", "zone",
"subblockno"))
# calculate straighline distance between start and finish position
site.samp.distance <- site.samp.start.finish %>%
mutate(dive_dist = units::drop_units(st_distance(geometry, finish.geom, by_element = T))) %>%
# mutate(distance = as.numeric(units::drop_units(st_distance(geometry, finish.geom, by_element = T)))) %>%
dplyr::rename(samptime = samptime.x,
gpstime = gpstime.x,
sampperiod = sampperiod.x,
waypoint = waypoint.x) %>%
dplyr::select(-c(samptime.y, gpstime.y, sampperiod.y, waypoint.y, finish.geom))
# st_as_sf()
# calculate straightline direction between start and finish position
site.samp.direction <- site.samp.start.finish %>%
filter(!st_is_empty(geometry), !st_is_empty(finish.geom)) %>%
mutate(dive_dir = nngeo::st_azimuth(geometry, finish.geom)) %>%
dplyr::rename(samptime = samptime.x,
gpstime = gpstime.x,
sampperiod = sampperiod.x,
waypoint = waypoint.x) %>%
dplyr::select(-c(samptime.y, gpstime.y, sampperiod.y, waypoint.y, finish.geom))
# st_as_sf()
# rejoin straigline distance and direction tables
site_samp_dist_dir <- left_join(site.samp.distance, site.samp.direction) %>%
st_as_sf()
# join data to subblock map to identify subblockno (i.e. inc. all blocks)
site.samp.start.subblock.loc.ref <- st_join(site_samp_dist_dir, sf.subblock.map, join = st_nearest_feature) %>%
dplyr::select(-c(version, area, blockno.y, subblockno.y, zone.y)) %>%
dplyr::rename(blockno = blockno.x,
subblockno = subblockno.x,
zone = zone.x) %>%
rename_all(tolower) %>%
filter(sampperiod == 'start')
# load proposed site file
ts.sites.final.sf <- readRDS(paste(samp.year.folder, '/ts.sites.final.sf.RDS', sep = ''))
# join sites sampled to renamed site file for 2020
ts.actual.geom <- ts.sites.final.sf %>%
st_drop_geometry() %>%
filter(sampyear == 2020) %>%
left_join(site.samp.start.subblock.loc.ref, ., by = c('site' = 'site.old',
'sampyear' = 'sampyear')) %>%
mutate(site.old = ifelse(sampyear == 2020, site, ''),
site = ifelse(sampyear == 2020, site.y, site),
site = ifelse(sampyear == 2020 & is.na(site), site.old, site)) %>%
dplyr::select(-site.y)
# save file of actual geometry for raw data join
saveRDS(ts.actual.geom, paste(samp.year.folder, '/ts.actual.geom.RDS', sep = ''))
# save spatial layer for QGIS
st_write(ts.actual.geom,
dsn = paste(samp.year.folder, '/ts.actual.geom_', Sys.Date(), '.gpkg', sep = ''),
layer = "ts.actual.geom", driver = "GPKG", overwrite = T, delete_dsn = T)
rm(list = setdiff(ls(), c('vessel.gps.dat', 'meta_dat_vessel', 'time.swim.dat.df',
'time.swim.meta.dat', 'time.swim.dat', 'samp.year', 'samp.year.folder',
'ts_meta_dat', 'ts.site.start.finish.loc', 'ts.sites.final.sf',
'ts.proposed.geom', 'ts.actual.geom')))
##----------------------------------------------------------------------------##
# # create maps for each block (#note: sample year and/or reference sites)
#
# # identify blocks sampled
# blocks.sampled <- site.samp.start.subblock.loc.ref %>%
# filter(sampyear == samp.year) %>%
# distinct(blockno) %>%
# pull()
#
# # create maps
# for (i in blocks.sampled){
#
# site.samp.loc.blockno <- site.samp.start.subblock.loc.ref %>%
# filter(blockno == i &
# sampyear == samp.year)
#
# sub_blocks <- site.samp.loc.blockno %>% distinct(subblockno) %>% pull()
#
# sf.subblock.map.crop.1 <- sf.subblock.map %>%
# filter(blockno == i &
# !subblockno %in% c('28B', '28C'))
#
# sf.subblock.map.crop.2 <- sf.subblock.map %>%
# {if(i == '27') filter(., blockno == '26') else filter(., blockno == i)}
#
# sf.subblock.map.crop.3 <- sf.subblock.map %>%
# {if(i == '16') filter(., blockno %in% c('14', '15')) else filter(., blockno == i)}
#
# sf.subblock.map.crop.4 <- sf.subblock.map %>%
# {if(i == '13') filter(., subblockno %in% c('13D', '13E')) else filter(., blockno == i)}
#
#
# sf.subblock.map.crop <- bind_rows(sf.subblock.map.crop.1, sf.subblock.map.crop.2,
# sf.subblock.map.crop.3, sf.subblock.map.crop.4) %>%
# filter(subblockno %in% sub_blocks)
#
# site.samp.map.blockno <- ggplot(data = st_geometry(sf.subblock.map.crop)) +
# geom_sf(fill = NA) +
# geom_sf_text(data = sf.subblock.map.crop, aes(label = subblockno))+
# geom_sf(data = site.samp.loc.blockno, colour = 'blue') +
# theme_bw() +
# annotation_scale(location = "bl", width_hint = 0.5) +
# annotation_north_arrow(location = "br", which_north = "true",
# pad_x = unit(0.05, "cm"), pad_y = unit(0.1, "cm"),
# style = north_arrow_fancy_orienteering)+
# theme(legend.position="none")+
# xlab('Longitude')+
# ylab('Latitude')
#
#
# setwd(ts.plots.folder)
# ggsave(filename = paste('TimedSwimSurvey_', samp.year, '_SitesSampled_GPS_BlockNo_', i, '.pdf', sep = ''),
# plot = site.samp.map.blockno, units = 'mm', width = 190, height = 250)
# ggsave(filename = paste('TimedSwimSurvey_', samp.year, '_SitesSampled_GPS_BlockNo_', i, '.png', sep = ''),
# plot = site.samp.map.blockno, units = 'mm', width = 190, height = 150)
#
# }
##---------------------------------------------------------------------------##
# # Extract block 13 and 14 reference site coordinates
# df.1 <- site.samp.start.loc %>%
# filter(blockno %in% c(13, 14) &
# sampperiod == 'start')
#
# WGS84 <- st_crs(4326)
#
# df.2 <- st_transform(df.1, WGS84) %>%
# st_coordinates()
#
# st_write(df.1,
# dsn = "C:/CloudStor/Shared/DiveFisheries/Abalone/FISdata/FIS_TimedSwimSurveys2021/block13-14_refsites.gpkg",
# layer = "df.1", driver = "GPKG", overwrite = T, delete_dsn = T)
##---------------------------------------------------------------------------##
## 9. Join site data ####
## Join spatial site data (proposed and actual GPS sites) to count and meta data
# Load raw count, proposed and actual site data frames
time.swim.dat <- readRDS(paste(samp.year.folder, '/time.swim.dat.RDS', sep = ''))
time.swim.dat.df <- readRDS(paste(samp.year.folder, '/time.swim.dat.df.RDS', sep = ''))
ts.actual.geom <- readRDS(paste(samp.year.folder, '/ts.actual.geom.RDS', sep = ''))
ts.proposed.geom <- readRDS(paste(samp.year.folder, '/ts.proposed.geom.RDS', sep = ''))
# add unique join ID
time.swim.dat <- time.swim.dat %>%
mutate(join_id = row_number())
# add sampdate to actual.geom file
ts.actual.geom <- ts.actual.geom %>%
mutate(sampdate = date(samptime))
# join proposed site data to size frequency data
ts.dat.prop.2020 <- time.swim.dat %>%
filter(sampyear == 2020) %>%
left_join(., ts.proposed.geom, by = c('site' = 'site.old',
'sampyear' = 'sampyear')) %>%
mutate(site = ifelse(is.na(site.y), site, site.y))
ts.dat.prop.2021 <- time.swim.dat %>%
filter(sampyear == 2021) %>%
left_join(., ts.proposed.geom, by = c('site' = 'site',
'sampyear' = 'sampyear'))
ts.dat.prop.2022 <- time.swim.dat %>%
filter(sampyear == 2022) %>%
left_join(., ts.proposed.geom, by = c('site' = 'site',
'sampyear' = 'sampyear'))
ts.dat.prop.2023 <- time.swim.dat %>%
filter(sampyear == 2023) %>%
left_join(., ts.proposed.geom, by = c('site' = 'site',
'sampyear' = 'sampyear'))
ts.dat.prop.2024 <- time.swim.dat %>%
filter(sampyear == 2024) %>%
left_join(., ts.proposed.geom, by = c('site' = 'site',
'sampyear' = 'sampyear'))
ts.dat.prop <- bind_rows(ts.dat.prop.2020, ts.dat.prop.2021,
ts.dat.prop.2022, ts.dat.prop.2023,
ts.dat.prop.2024) %>%
dplyr::select(-c(blockno.y, site.y, site.old)) %>%
dplyr::rename(proposed.geom = 'geometry',
blockno = 'blockno.x')
# join actual site data to size frequency data
ts.dat.act.2020 <- time.swim.dat %>%
filter(sampyear == 2020) %>%
left_join(., ts.actual.geom, by = c('site' = 'site.old',
'sampyear' = 'sampyear',
'sampdate')) %>%
mutate(site = ifelse(is.na(site.y), site, site.y)) %>%
dplyr::select(-site.y)
ts.dat.act.2021 <- time.swim.dat %>%
filter(sampyear == 2021) %>%
left_join(., ts.actual.geom, by = c('site' = 'site',
'sampyear' = 'sampyear',
'sampdate'))
ts.dat.act.2022 <- time.swim.dat %>%
filter(sampyear == 2022) %>%
left_join(., ts.actual.geom, by = c('site' = 'site',
'sampyear' = 'sampyear',
'sampdate'))
ts.dat.act.2023 <- time.swim.dat %>%
filter(sampyear == 2023) %>%
left_join(., ts.actual.geom, by = c('site' = 'site',
'sampyear' = 'sampyear',
'sampdate'))
ts.dat.act.2024 <- time.swim.dat %>%
filter(sampyear == 2024) %>%
left_join(., ts.actual.geom, by = c('site' = 'site',
'sampyear' = 'sampyear',
'sampdate'))
ts.dat.act <- bind_rows(ts.dat.act.2020, ts.dat.act.2021,
ts.dat.act.2022, ts.dat.act.2023,
ts.dat.act.2024) %>%
dplyr::select(-c(blockno.y, site.old)) %>%
dplyr::rename(actual.geom = 'geometry',
blockno = 'blockno.x')
time.swim.dat.act.prop <- left_join(ts.dat.prop %>% dplyr::select(-c(subblockno, zone)), ts.dat.act %>% dplyr::select(-c(oid))) %>%
dplyr::select(c(site, sampdate, sampyear, blockno, subblockno, diver, starttime,
firstabtime, finishtime, time.elapsed, sizeclass, sizeclass.2021, sizeclass_freq,
minsize, midsize, midsize.2021, maxsize, legal.size, actual.geom, proposed.geom, dive_dist, dive_dir, oid))
# join proposed site data to individual length data
# add unique join ID
time.swim.dat.df <- time.swim.dat.df %>%
mutate(join_id = row_number())
ts.dat.prop.df.2020 <- time.swim.dat.df %>%
filter(sampyear == 2020) %>%
left_join(., ts.proposed.geom, by = c('site' = 'site.old',
'sampyear' = 'sampyear')) %>%
mutate(site = ifelse(is.na(site.y), site, site.y))
ts.dat.prop.df.2021 <- time.swim.dat.df %>%
filter(sampyear == 2021) %>%
left_join(., ts.proposed.geom, by = c('site' = 'site',
'sampyear' = 'sampyear'))
ts.dat.prop.df.2022 <- time.swim.dat.df %>%
filter(sampyear == 2022) %>%
left_join(., ts.proposed.geom, by = c('site' = 'site',
'sampyear' = 'sampyear'))
ts.dat.prop.df.2023 <- time.swim.dat.df %>%
filter(sampyear == 2023) %>%
left_join(., ts.proposed.geom, by = c('site' = 'site',
'sampyear' = 'sampyear'))
ts.dat.prop.df.2024 <- time.swim.dat.df %>%