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DJFMPTrawls.Rmd
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DJFMPTrawls.Rmd
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---
title: "chipps island"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
library(readxl)
library(lubridate)
library(chron)
library(magrittr)
library(tsibble)
library(ggplot2)
library(dataRetrieval) # may require R v4.1.1
library(DescTools)
library(emmeans)
```
## Chipps island data
The trawl data is structured very similar to the beach seine data, we will just subset it to just the "chipps" location. Since it's just a single location, we can use day of year instead of week to calculate cummulative catch.
```{r}
Trawl1 = read_csv("data/1976-2001_DJFMP_trawl_fish_and_water_quality_data.csv")
Trawl2 = read_csv("data/2002-2021_DJFMP_trawl_fish_and_water_quality_data.csv")
Trawls = bind_rows(Trawl1, Trawl2)%>%
select(Location, StationCode, SampleDate, MethodCode, TowNumber, TowDuration, Volume, IEPFishCode, Count)
TrawlsCH = pivot_wider(Trawls, names_from = IEPFishCode, values_from = Count, values_fn = sum, values_fill = 0) %>%
select(Location, StationCode, SampleDate, MethodCode, TowNumber, TowDuration, Volume, CHISAL) %>%
mutate(year_wk = yearweek(SampleDate),
setID = paste(SampleDate, StationCode),
CPUE = CHISAL/Volume)
#calculate cummulative CPUE for Chipps
Chipps = filter(TrawlsCH, Location == "Chipps Island") %>%
arrange(SampleDate) %>%
mutate(Year = year(SampleDate), julian = yday(SampleDate), wyjulian = case_when(
julian > 274 ~ julian -274,
TRUE ~ julian +91
))%>%
rename(Date = SampleDate) %>%
addWaterYear() %>%
group_by(waterYear) %>%
mutate(cumcatch = cumsum(CHISAL), cumcpue = cumsum(CPUE),
percentcatch = cumcatch/max(cumcatch, na.rm = T))
```
Ok, now let's plot it
```{r}
ggplot(Chipps, aes(x=wyjulian, y = cumcatch, group = waterYear)) + geom_line()+
coord_cartesian(xlim = c(0,360))
ggplot(Chipps, aes(x=wyjulian, y = percentcatch, group = waterYear, color = as.factor(waterYear))) + geom_line()+
coord_cartesian(xlim = c(0,360))+
ylab("Percent of Annual Chinook Salmon Catch")+
xlab("day of water year")
```
Calculating the point where we get to 50% of the annual catch, then join it to the water year
```{r}
Chipps50 = Chipps %>%
filter(percentcatch >= 0.5) %>%
group_by(waterYear) %>%
summarise(a50_day = min(wyjulian))
WYs <- read_csv("data/yearassignments.csv")
chipdate2 = left_join(Chipps50, WYs, by = c("waterYear" = "Year"))
```
Now do a box plot of the 50% day versus our drought designations
```{r}
ggplot(chipdate2, aes(x=Drought, y = a50_day)) + geom_boxplot()
```
Let's see what it looks like if we do the five water year types instaed, or the sacramento water year index
```{r}
ggplot(chipdate2, aes(x=Yr_type, y = a50_day)) + geom_boxplot()
ggplot(chipdate2, aes(x= Index, y = a50_day)) + geom_point() + geom_smooth(method = "lm")
```
How about just the current and past drought?
```{r cars}
ggplot(filter(Chipps, waterYear>2013), aes(x=wyjulian, y = percentcatch, group = waterYear, color = as.factor(waterYear))) + geom_line()+
coord_cartesian(xlim = c(0,350))+
ylab("Percent of Annual Chinook Salmon Catch")+
xlab("day of water year")
```
## Now the Sacramento tral (point of Delta entry)
Theis is the "sherwood harbor" location
```{r}
Shr = filter(TrawlsCH, Location == "Sherwood Harbor") %>%
arrange(SampleDate) %>%
mutate(Year = year(SampleDate), julian = yday(SampleDate), wyjulian = case_when(
julian > 274 ~ julian -274,
TRUE ~ julian +91
))%>%
rename(Date = SampleDate) %>%
addWaterYear() %>%
group_by(waterYear) %>%
mutate(cumcatch = cumsum(CHISAL), cumcpue = cumsum(CPUE),
percentcatch = cumcatch/max(cumcatch, na.rm = T))
```
Ok, now let's plot it
```{r}
ggplot(Shr, aes(x=wyjulian, y = cumcatch, group = waterYear)) + geom_line()+
coord_cartesian(xlim = c(0,360))
ggplot(Shr, aes(x=wyjulian, y = percentcatch, group = waterYear, color = as.factor(waterYear))) + geom_line()+
coord_cartesian(xlim = c(0,360))+
ylab("Percent of Annual Chinook Salmon Catch at Sherwood")+
xlab("day of water year")
```
Calculating the point where we get to 50% of the annual catch, then join it to the water year
```{r}
Sher50 = Shr %>%
filter(percentcatch >= 0.5) %>%
group_by(waterYear) %>%
summarise(a50_day = min(wyjulian))
yeartypes <- read_csv("data/yearassignments.csv")
Sherdate2 = left_join(Sher50, WYs, by = c("waterYear" = "Year"))
```
Now do a box plot of the 50% day versus our drought designations
```{r}
ggplot(Sherdate2, aes(x=Drought, y = a50_day)) + geom_boxplot()
```
Let's see what it looks like if we do the five water year types instaed, or the sacramento water year index
```{r}
ggplot(Sherdate2, aes(x=Yr_type, y = a50_day)) + geom_boxplot()
ggplot(Sherdate2, aes(x= Index, y = a50_day)) + geom_point() + geom_smooth(method = "lm")
```
How about just the current and past drought?
```{r}
ggplot(filter(Shr, waterYear>2013), aes(x=wyjulian, y = percentcatch, group = waterYear, color = as.factor(waterYear))) + geom_line()+
coord_cartesian(xlim = c(0,350))+
ylab("Percent of Annual Chinook Salmon Catch")+
xlab("day of water year")