salmon.R

#Set your working directory
setwd('~/GitHub/RWorkflow-NWFSC-2020/')
#setwd('C:Users/Eli.Holmes/Workshops/RWorkflow-NWFSC-2020/')

#-----Read in salmon data----
fil <- "data/salmon.csv"
saldat <- read.csv(fil)
colnames(saldat) <- c("year", "wild", "flow", "temp")
fil <- "data/steelhead.csv"
steeldat <- as.matrix(read.csv(fil))

#Create training and testing data frames
dat1 <- saldat[saldat$year<=2010,]
dat2 <- saldat[saldat$year>2010,]

# gam fit
library(mgcv)
fit1 <- gam(wild ~ s(flow), data=dat1)
fit2 <- lm(steeldat[1,1:30] ~ steeldat[2,1:30])
library(forecast)
dat <- dat1$wild
fit3 <- auto.arima(dat)

#Save data
save(fit1, fit2, fit3, file="Sim_6_5_2020_wild.RData") #save data 

#Make some plots
plot(fit1)
plot(fit2)

pred1 <- predict(fit1, newdata=data.frame(dat2))
plot(dat1$year, dat1$wild, xlim=c(1980,2020), type="l")
lines(dat2$year, dat2$wild, col="blue")
lines(dat2$year, pred1, col="red", lty=2)

pred2 <- predict(fit2, newdata=data.frame(dat2))
plot(dat1$year, dat1$wild, xlim=c(1980,2020), type="l")
lines(dat2$year, dat2$wild, col="blue")
lines(dat2$year, pred2, col="red", lty=2)

pred3 <- forecast(fit3, h=9)
plot(dat1$year, dat1$wild, xlim=c(1980,2020), type="l")
lines(dat2$year, dat2$wild, col="blue")
lines(dat2$year, pred3$mean, col="red", lty=2)