goosegmse_output.Rmd

---
output:
  word_document:
    reference_docx: word_template.docx
---

```{r, include=FALSE}
library(knitr)
knitr::opts_chunk$set(echo = FALSE)
library(flextable)
library(markdown)
source('goose_predict_gui.R')
load('input.Rdata')
load('sims.Rdata')
load('output_summary.Rdata')
load('in_summary.Rdata')
load('cull_summary.Rdata')
```

# Goose-GMSE simulation summary report

### Generated on `r format(Sys.time(), "%d %B %Y %H:%M")`

## Input parameters
_Table 1. Input parameter values._   
```{r inputpars, ft.align = "left"}
#kable(in_summary, booktabs = T) %>% kable_styling(full_width = F)
#kable(in_summary, booktabs = T, full_width=F)
#kable(in_summary, full_width=F)
#kable(in_summary)
#flextable(in_summary, col_keys = c("Parameter", "Value"))

# Table width
# 
# By default, table width is fixed. This setting allows to have the same rendering with Word, HTML and PowerPoint formats.
# You can use another setting with function set_table_properties() when argument layout is set to ‘autofit’, an algorithm 
# implemented by HTML and Word.
# When layout is set to autofit, you can also size the table along an available width with argument width (default to 1 
# that means 100% of the available width).
input_tab = flextable(data = in_summary, col_keys = c("Parameter","Value"))
input_tab = set_header_labels(input_tab, 
                              Parameter = "Parameter", 
                              Value = "Value")
input_tab = align(input_tab, align = "left", part = "all")
#input_tab = theme_vanilla(input_tab)
input_tab = autofit(input_tab)
#input_tab = set_table_properties(input_tab, layout = "autofit")
#input_tab = set_table_properties(input_tab, width = 0.8, layout = "autofit")
input_tab
```

## Output
```{r output1, ft.align = "left"}
include_graphics('./mainPlot.png')
#include_graphics('./zoomPlot.png')
```
  
_Figure 1. Observed and projected goose population trajectories._  
_Solid black dots and lines: the observed mean annual goose population counts on Islay to date._  
_Red lines: projected future mean annual goose populations on Islay; one for each simulation run._  
_Dashed red lines (if calculated and plotted): mean upper and lower 95% quantiles of mean goose population counts on Islay, across all simulations. This takes account of parameter uncertainty in predictor variables._  
<br>
<br>

### Output summary
```{r output2}
output_summary[[3]][[1]]
```

## Numbers culled per projected year

_Table 2. Summary of mean projected population size in each year, and the mean, standard deviation, minimum and maximum number of geese culled per year, across all simulations._  
```{r culled, ft.align = "left"}
names(cull_summary) <- c("Year","MeanPop","MeanCulled","SDCulled","MinCulled","MaxCulled")
#kable(cull_summary, booktabs = T, full_width = F)
#kable(cull_summary, full_width = F)
output_tab = flextable(data = cull_summary)
output_tab = set_header_labels(output_tab, 
                               Year = "Year", 
                               MeanPop = "Mean projected\npop. size",
                               MeanCulled = "Mean\nculled",
                               SDCulled = "SD\nculled",
                               MinCulled = "Min. culled",
                               MaxCulled = "Max. culled")
output_tab = align(output_tab, align = "left", part = "all")
output_tab = width(output_tab, j = 2, width = 1.5)
#output_tab = autofit(output_tab)
output_tab
```