putting plot of most recent data against forecast on website (#111)

* changed name of plot_data function to plot_species_forecast for clarity

* added code to plot the most recent data from the repo against the most recent forecast prior to that data collection.  there are still some issues, right now this will only work when June 2017 is the most recent data

* removed old code from report.Rmd

forecast_tools.R

@@ -82,7 +82,13 @@ get_sp_predicts = function(data, lvl, lead_time) {
   data2 = filter(data1, newmoonnumber == target_moon)
 }
 
-plot_data = function(data) {
+#' this is the 'second plot on the 'Species-level Forecast' on the 'Current Forecast' page on the website
+#' 
+#' 
+#' @param data
+#' @return sp_predict is a plot object -- plot(sp_predict) displays it
+#' 
+plot_species_forecast = function(data) {
   newmoons_table = read.csv(
     text = getURL(
       "https://raw.githubusercontent.com/weecology/PortalData/master/Rodents/moon_dates.csv"))
@@ -111,21 +117,12 @@ plot_data = function(data) {
     ylab("Species") +
     xlab("Abundance") +
     scale_y_discrete(breaks = reorder(data$species,data$estimate),labels = reorder(species_names$scientificname,species_names$estimate))
-  if(!is.na(period_code)){
-    rodents = abundance("repo", shape='flat')
-    observed = dplyr::filter(rodents, period == period_code)
-    joined_data = left_join(data, 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 = sp_predict +
-      geom_point(data = joined_data, mapping = aes(x = abundance, y = species),
-                 color = "blue")
-  }
-  plot(sp_predict)
+
+  return(sp_predict)
 }
 
 
+
 #' Compares forecasts to observations over different lead times.
 #' Error can be any function. The level, species, and currency columns from
 #' observations and forecasts must have matching values.

index.Rmd

@@ -52,7 +52,8 @@ source("forecast_tools.R")
 data = compile_forecasts()
 ensemble = dplyr::filter(data, level == 'All', model == 'Ensemble', date == max(date))
 sp_predictions = get_sp_predicts(ensemble, 'All', lead_time = 1)
-plot_data(sp_predictions)
+sp_predict = plot_species_forecast(sp_predictions)
+plot(sp_predict)
 ```
 ```{r highabund_ts_forecasts, echo=FALSE, message=FALSE, warning=FALSE}
 most_abund_sp = sp_predictions %>% 

report.Rmd

@@ -1,27 +1,36 @@
 ---
-title: "Report - June 2017"
+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)
 
-Note the forecast for D. spectabalis this month. 
+# get most recent rodent data
+rodents = abundance("repo", shape='flat')
+period_code = max(rodents$period)
+observed = dplyr::filter(rodents, period == period_code)
 
-```{r speciesforecasts, echo=FALSE, message=FALSE, warning=FALSE}
-source("forecast_tools.R")
+# 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'))
 
-data = compile_forecasts()
-ensemble = dplyr::filter(data, level == 'All', model == 'Ensemble', date == max(date))
+ensemble = dplyr::filter(data, level == 'All', model == 'Ensemble', newmoonnumber==moon_code)
 sp_predictions = get_sp_predicts(ensemble, 'All', lead_time = 1)
-ds_forecast = forecast_viz(obs_data = obs_data_newmoon,
-               obs_date_col_name = "newmoonnumber",
-               obs_val_col_name = 'DS',
-               for_data = for_data,
-               for_date_col_name = "newmoonnumber",
-               for_val_col_name = "estimate",
-               for_model_name = "ensemble",
-               for_lowerpi_col_name = "LowerPI",
-               for_upperpi_col_name = "UpperPI",
-               start_newmoon = 300)
-  plot(ds_forecast)
 
+
+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)
 ```
 
-There is an increasing change that we will see a spectab in the coming months. Does this mean Stephanie will return?