---
title: "simmr: quick start guide"
author: "Andrew Parnell"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{quick_start}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  chunk_output_type: console
---

## Step 1: install `simmr`

Use:
```{r, eval = FALSE}
install.packages('simmr')
```

then
```{r, message=FALSE}
library(simmr)
```

## Step 2: load in the data

Some geese isotope data is included with this package. Find where it is with:
```{r, eval = FALSE}
system.file("extdata", "geese_data.xls", package = "simmr")
```

Load into R with:
```{r, echo = FALSE}
if (!requireNamespace("readxl", quietly = TRUE)) {
    stop("readxl needed for this vignette to work. Please install it.",
      call. = FALSE)
}
```
```{r}
library(readxl)
path = system.file("extdata", "geese_data.xls", package = "simmr")
geese_data = lapply(excel_sheets(path), read_excel, path = path)
```

If you want to see what the original Excel sheet looks like you can run `system(paste('open',path))`.

We can now separate out the data into parts 
```{r}
targets = geese_data[[1]]
sources = geese_data[[2]]
TEFs = geese_data[[3]]
concdep = geese_data[[4]]
```

## Step 3: load the data into `simmr`

```{r}
geese_simmr = simmr_load(mixtures = as.matrix(targets[, 1:2]),
                         source_names = sources$Sources,
                         source_means = sources[,2:3],
                         source_sds = sources[,4:5],
                         correction_means = TEFs[,2:3],
                         correction_sds = TEFs[,4:5],
                         concentration_means = concdep[,2:3],
                         group = as.factor(paste('Day', targets$Time)))
```

## Step 4: plot the data 

```{r, fig.width = 8, fig.height = 8}
plot(geese_simmr, group = 1:8)
```

## Step 5: run through `simmr` and check convergence

```{r, results = 'hide', message = FALSE}
geese_simmr_out = simmr_mcmc(geese_simmr)
summary(geese_simmr_out, type = 'diagnostics',
        group = 1)
```

Check that the model fitted well:
```{r}
posterior_predictive(geese_simmr_out, group = 5)
```

## Step 6: look at the output

Look at the influence of the prior:
```{r}
prior_viz(geese_simmr_out)
```

Look at the histogram of the dietary proportions:
```{r}
plot(geese_simmr_out, type = 'histogram')
```

```{r, fig.width = 8, fig.height = 8}
compare_groups(geese_simmr_out, groups = 1:4, 
               source_name = 'Enteromorpha')
```

For the many more options available to run and analyse output, see the main vignette via `vignette('simmr')`
