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plot_length_weight.R
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# Length - Weight data
library(dplyr)
library(lubridate)
options(scipen = 100) # to fix VTR serial numbers
ml_wt_wks0510_0608 <- read.csv('ILXSMdata_5-10-22_6-8-22.csv')
ml_wt_wks0609_0616 <- read.csv('ILXSMdata_6-9-22_6-16-22.csv')
ml_wt_wks0617_0622 <- read.csv('ILXSMdata_6-17-22_6-22-22.csv')
ml_wt_wks0623_0630 <- read.csv('ILXSMdata_6-23-22_6-30-22.csv')
ml_wt_wks0701_0707 <- read.csv('ILXSMdata_7-1-22_7-7-22.csv')
ml_wt_wks0708_0714 <- read.csv('ILXSMdata_7-8-22_7-14-22.csv')
ml_wt_wks0721_0727 <- read.csv('ILXSMdata_7-21-22_7-27-22.csv')
ml_wt_wks0728_0803 <- read.csv('ILXSMdata_07-28-22_08-03-22.csv')
ml_wt_wks0804_0810 <- read.csv('ILXSMdata_08-04-22_08-10-22.csv')
ml_wt_wks0811_0817 <- read.csv('ILXSMdata_08-11-22_08-17-22.csv')
ml_wt_wks0818_0824 <- read.csv('ILXSMdata_08-18-22_08-24-22.csv')
# get same setup
ml_wt_wks0701_0707 <- ml_wt_wks0701_0707[,-c(3,4)]
ml_wt_wks0708_0714 <- ml_wt_wks0708_0714[,-c(3,4)]
ml_wt_wks0721_0727 <- ml_wt_wks0721_0727[,-c(3,4)]
ml_wt_wks0728_0803 <- ml_wt_wks0728_0803[,-c(3,4)]
ml_wt_wks0804_0810 <- ml_wt_wks0804_0810[,-c(3,4)]
ml_wt_wks0811_0817 <- ml_wt_wks0811_0817[,-c(3,4)]
ml_wt_wks0818_0824 <- ml_wt_wks0818_0824[,-c(3,4)]
# an alternative way to fix
# lwt_wks23_24 <- lwt_wks23_24 %>%
# mutate(VTR_SERIAL_NUM = as.numeric(format(VTR_SERIAL_NUM,
# scientific = FALSE,
# big.mark = '')))
ml_wt_22 <- rbind(ml_wt_wks0510_0608, ml_wt_wks0609_0616,
ml_wt_wks0617_0622,ml_wt_wks0623_0630,
ml_wt_wks0701_0707, ml_wt_wks0708_0714,
ml_wt_wks0721_0727,ml_wt_wks0728_0803,
ml_wt_wks0804_0810, ml_wt_wks0811_0817,
ml_wt_wks0818_0824)
vtr_serial_numbers <-unique(ml_wt_22$VTR_SERIAL_NUM)
vessel_names <-unique(ml_wt_22$VESSEL_NAME)
draft_latlons <- read.csv('draft_locations_illex.txt')
for (i in 1:nrow(draft_latlons)){
draft_latlons$lat[i] <- c(paste0(draft_latlons[i,28],'.',paste0(draft_latlons[i,29])))
draft_latlons$lon[i] <- c(paste0(draft_latlons[i,31],'.',paste0(draft_latlons[i,32])))
}
tbl_serial_numbers <-unique(draft_latlons$SERIAL_NUM)
ids <- data.frame(vessel_id = c('STARBRITE', 'DYRSTEN',
'PERSISTENCE', 'RELENTLESS',
'GABBY G', 'RETRIEVER'),
vtr_srl_num = c(33016722060714,
33072500000000,
33059122052212,
41042522052312,
33057522060218,
41045800000000))
#ENDEAVOUR
draft_latlons$vessel_id <- NA
draft_latlons$VESSEL_NAME <- draft_latlons$vessel_id
# 'TOW_HRS',
dat <- draft_latlons %>%
dplyr::select(c('IMG_DATE', 'SERIAL_NUM', 'DEPTH',
'DATE_RECV', 'TENMSQ',
'lat', 'lon', 'VESSEL_NAME'))
dat$IMG_DATE <- lubridate::dmy(dat$IMG_DATE)
dat$DATE_RECV <- lubridate::dmy(dat$DATE_RECV)
# Add in date info
ml_wt_22$LAND_DATE <- lubridate::dmy(ml_wt_22$LAND_DATE)
ml_wt_22 <- ml_wt_22 %>%
mutate(year = year(LAND_DATE),
month = month(LAND_DATE),
week = week(LAND_DATE),
day = day(LAND_DATE))
dat <- dat %>%
mutate(year = year(DATE_RECV),
month = month(DATE_RECV),
week = week(DATE_RECV),
day = day(DATE_RECV))
dt <- left_join(ml_wt_22, dat, by = c('VESSEL_NAME','year','month','week'))
library(RColorBrewer)
library(scales)
library(ggplot2)
library(plotly)
## bring in bathymetry
atl <- marmap::getNOAA.bathy(-80,-63, 33, 46)
atl = fortify.bathy(atl)
blues = colorRampPalette(brewer.pal(9,'Blues'))(25)
bluess = colorRampPalette(brewer.pal(9,'Blues'))(30)
depths <- c(0,50,100,200,300,500,Inf)
blues2 <- blues[c(5,7,9,11,13,24)]
## get coastline from mapdata
coast = map_data("world2Hires")
coast = subset(coast, region %in% c('Canada', 'USA'))
coast$lon = (360 - coast$long)*-1
ggplot() +
geom_contour_filled(data = atl,
aes(x=x,y=y,z=-1*z),
breaks=c(0,50,100,200,300,500,Inf),
size=c(0.3)) +
scale_fill_manual(values = blues2, # 5:25
name = paste("Depth (m)"),
labels = depths) +
geom_polygon(data = coast, aes(x=lon, y = lat, group = group),
color = "gray20", fill = "wheat3") +
coord_sf(xlim = c(-76,-65), ylim = c(35,45),datum = sf::st_crs(4326)) +
geom_points(data = dt %>% filter(week == 25 & PARAM_TYPE == 'ML'),
aes(x=lon, y=lat, size = PARAM_VAL_NUM),
size=c(0.3),
col = '#4daf89') + # chl.pal[8] #4daf71
# NAFO SHELFBREAK
geom_polygon(data=shbrk, aes(x=long, y = lat, group = group),
colour = 'black', fill = NA, lwd = 1.2) +
xlab('Longitude') +
ylab('Latitude') +
theme_bw()
library(tidyverse)
dt_wt <- dt %>%
filter(PARAM_TYPE == 'WT') %>%
rename(weight = PARAM_VALUE_NUM)
dt_ml <- dt %>%
filter(PARAM_TYPE == 'ML') %>%
rename(length = PARAM_VALUE_NUM)
dt_wt <- dt_wt[,-c(5:7)]
dt_ml <- dt_ml[,-c(5:7)]
dt2 <- full_join(dt_wt, dt_ml, by = "ORGANISM_ID")
#put all data frames into list
df_list <- list(dt_wt, dt_ml)
#merge all data frames together
dt2 <- df_list %>% reduce(full_join, by=c('LAND_DATE','AREA_CODE',
'ORGANISM_ID','VESSEL_NAME',
'VTR_SERIAL_NUM','year',
'month','week', 'day.x', 'day.y',
'lat', 'lon',
'IMG_DATE','SERIAL_NUM','DEPTH',
'DATE_RECV', 'TENMSQ'))
write.csv(dt2, 'weight_lengths_22.csv')
library(ggnewscale)
wk25 = dt2 %>% filter(week == 25)
p = ggplot() +
# geom_contour(data = atl,
# aes(x=x,y=y,z=-1*z),
# breaks=c(0,50,100,200,500,Inf),
# size=c(0.3),
# col = 'darkgrey') +
# geom_contour_filled(data = atl,
# aes(x=x,y=y,z=-1*z),
# breaks=c(0,50,100,200,300,500,Inf),
# size=c(0.3)) +
# scale_fill_manual(values = blues2, # 5:25
# name = paste("Depth (m)"),
# labels = depths) +
#new_scale_fill() +
geom_point(data = wk25,
aes(x=lon, y=lat, size = length, color = weight)) +
scale_color_gradient(low = "blue", high = "red") +
labs(title = 'Week 25', subtitle = '2022') +
theme_bw()
ggplot() +
geom_point(data = wk25,
aes(x=lon, y=lat, size = length, color = weight)) +
#scale_color_gradient(low = "blue", high = "red") +
labs(title = 'Week 25', subtitle = '2022') +
theme_bw()
ggplotly(p)
hist(wk25$length)
hist(wk25$weight)
# Basic histogram
ggplot(wk25, aes(x=weight)) +
geom_histogram(aes(y=..density..), colour="black", fill="white")+
geom_density(alpha=.2, fill="#FF6666") +
labs(title = 'Weights', subtitle = 'Week 25') +
theme_bw()
ggplot(wk25, aes(x=length)) +
geom_histogram(aes(y=..density..), colour="black", fill="white")+
geom_density(alpha=.2, fill="#FF6666") +
labs(title = 'Length', subtitle = 'Week 25') +
theme_bw()
## ---- Plot up histograms ---- ##
library(dbplyr)
library(dplyr)
library(lubridate)
library(ggridges)
library(ggplot2)
library(patchwork)
ml_wt_22$LAND_DATE <- as.Date(ml_wt_22$LAND_DATE)
ml_wt_22$LAND_DATE <-lubridate::dmy(ml_wt_22$LAND_DATE)
ml_wt_22$week <- lubridate::week(ml_wt_22$LAND_DATE)
write.csv(ml_wt_22, 'ml_wt_22.csv')
wt_22 <- ml_wt_22 %>%
filter(PARAM_TYPE == 'WT') %>%
rename(weight = PARAM_VALUE_NUM)
ml_22 <- ml_wt_22 %>%
filter(PARAM_TYPE == 'ML') %>%
rename(length = PARAM_VALUE_NUM)
ggplot(wt_22,
aes(x = weight, y = factor(week), fill = factor(week))) +
geom_density_ridges(alpha = .8, color = 'white',
scale = 2.5, rel_min_height = .01) +
labs(title = '2022 Illex Weights', x = 'Weight (gm)', y = 'Week', fill = 'Week') +
guides(color = guide_legend(title = 'Week')) +
theme_ridges() +
theme_classic()
ggplot(ml_22,
aes(x = length, y = factor(week), fill = factor(week))) +
geom_density_ridges(alpha = .8, color = 'white',
scale = 2.5, rel_min_height = .01) +
labs(title = '2022 Illex Lengths', x = 'Length (mm)', y = 'Week', fill = 'Week') +
guides(color = guide_legend(title = 'Week')) +
theme_ridges() +
theme_classic()
# ggplot(wt_22, aes(x = weight, y = factor(week),
# fill = factor(week))) +
# geom_density_ridges(alpha = .8, color = 'black',
# scale = 2.5, rel_min_height = .01) +
# labs(title = 'Total Illex catch per unit effort',
# subtitle = 'Study Fleet and Observer Dataset (1998-2021)',
# x = 'Catch per unit effort (lbs/hr)', y = 'Year') +
# theme_ridges() +
# scale_fill_grey() +
# # guides(fill=FALSE)+
# theme_classic()
#
ggplot(ml_22, aes(x = length, y = factor(week),
fill = stat(quantile))) +
stat_density_ridges(quantile_lines = FALSE,
calc_ecdf = TRUE, rel_min_height = 0.0001,
geom = "density_ridges_gradient") +
labs(title = 'Total Illex catch per unit effort',
subtitle = 'Study Fleet and Observer Dataset (2011-2020)',
x = 'Catch per unit effort (lbs/hr)', y = 'Year') +
scale_fill_brewer(name = '', palette = 'Greys') +
ecodata::theme_ts()
ggplot(wt_22 %>% filter(week == 28), aes(x=weight)) +
geom_histogram(aes(y=..density..), colour="black", fill="white")+
geom_density(alpha=.2, fill="#FF6666") +
labs(title = 'Weights', subtitle = 'Week 28') +
theme_bw()
ggplot(ml_22 %>% filter(week == 28), aes(x=length)) +
geom_histogram(aes(y=..density..), colour="black", fill="white")+
geom_density(alpha=.2, fill="#FF6666") +
labs(title = 'Length', subtitle = 'Week 28') +
theme_bw()
library(ggplot2)