report.Rmd

---
title: "Report - September 2017"
---
Here's the most recent data (blue) compared to the forecast performed right before data collection (black, with error bars)
```{r, echo=FALSE, message=FALSE, warning=FALSE}
source("forecast_tools.R")
library(portalr)

# get most recent rodent data
rodents = abundance("repo", shape='flat')
period_code = max(rodents$period)
observed = dplyr::filter(rodents, period == period_code)

# get new moon number associated with this period
newmoons_table = read.csv(text = getURL("https://raw.githubusercontent.com/weecology/PortalData/master/Rodents/moon_dates.csv"))
moon_code = dplyr::filter(newmoons_table,period==period_code) %>% select(newmoonnumber) %>% as.integer()

# get most recent forecast prior to data collection -- WIP!!!!!!!! there's an issue up
data = read.csv('predictions/2017-06-23Allforecasts.csv',na.strings = '')
data = plyr::rename(data,c('NewMoonNumber' = 'newmoonnumber'))

ensemble = dplyr::filter(data, level == 'All', model == 'Ensemble', newmoonnumber==moon_code)
sp_predictions = get_sp_predicts(ensemble, 'All', lead_time = 1)


joined_data = left_join(sp_predictions, observed, by = "species")
joined_data[is.na(joined_data)] = 0
joined_data[joined_data$species=='total','abundance'] = sum(joined_data$abundance,na.rm=T)
joined_data[joined_data$species=='total','period'] = period_code

sp_predict = plot_species_forecast(sp_predictions)  
sp_predict = sp_predict + geom_point(data = joined_data, mapping = aes(x = abundance, y = species),
             color = "blue")
plot(sp_predict)
```