PortalForecasts.R

library(lubridate)
library(dplyr)
library(magrittr)
library(rmarkdown)
source('forecast_tools.R')
source('models/model_functions.R')

filename_suffix = 'forecasts'

#The date this forecast model is run. Always today's date.
forecast_date = Sys.Date()

portalr::download_observations()
moons = get_moon_data()
#get dates of 12 new moons following newmoon of interest
future_moons = get_future_moons(moons)

#Beginning and end of the forecast timeperiod
most_recent_newmoon = moons$newmoonnumber[which.max(moons$period)]
first_forecast_newmoon=most_recent_newmoon+1
last_forecast_newmoon=first_forecast_newmoon + 11
forecast_newmoons = first_forecast_newmoon:last_forecast_newmoon
forecast_months = future_moons$month[future_moons$newmoonnumber %in% forecast_newmoons]
forecast_years = future_moons$year[future_moons$newmoonnumber %in% forecast_newmoons]

rodent_data = get_rodent_data(moons, forecast_date, filename_suffix)
weather_data = get_weather_data(moons, rodent_data$all, first_forecast_newmoon, last_forecast_newmoon)

#Get only relevent columns now that this is isn't needed to subset weather.
rodent_data$all = rodent_data$all %>%
  select(-newmoondate,-censusdate,-period,-year,-month)

#tscount::tsglm() will not model a timeseries of all 0's. So for those species, which are
#ones that just haven't been observed in a while, make a forecast of all 0's.
zero_abund_forecast = list(pred=rep(0,12), interval=matrix(rep(0,24), ncol=2))
colnames(zero_abund_forecast$interval) = c('lower','upper')

cat("Making site level forecasts", "\n")
allforecasts=forecastall(rodent_data$all,"All",weather_data,weathermeans, forecast_date, forecast_newmoons, forecast_months, forecast_years)

cat("Making control plot forecasts", "\n")
controlsforecasts=forecastall(rodent_data$controls,"Controls",weather_data,weathermeans, forecast_date, forecast_newmoons, forecast_months, forecast_years)

######Update Website####################################################
rmarkdown::render_site()