---
title: "Usage (Log10 Uniform Inflow)"
author: "Michael Rustler"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Usage (Log10 Uniform Inflow)}
  %\VignetteEngine{knitr::rmarkdown}
  \usepackage[utf8]{inputenc}
---

How to work with the `kwb.qmra` package in R(Studio) is described in the following chapters

Once the R package is installed it can be loaded with the following command in 
R(Studio):

```{r}
library(kwb.qmra)
```


# 1 Input data 

## 1.1 Location of 'dummy' configuration 

The folder with the csv configuration files  for a hypothetical use case is 
located here: 

```{r}
#### DEFINE DIRECTORY ################
confDir <- system.file("extdata/configs/dummy_log10_uniform", package = "kwb.qmra")
confDir
```

The following screenshot shows the required configuration files located in 
`confDir` (here: `r confDir`):

![Screenshot of required configuration files](dummy_config.jpg)

## 1.2 Import configuration into R

All csv files with the input data for the hypothetical u `dummy` (as shown above) are 
imported into R with the following function:

```{r, message = FALSE, warnings = FALSE }
#### LOAD ############################
config <- config_read(confDir) 
```

# 2 Check input data 

The QMRA will be performed - in case the user does not modify them in R - based on the 
imported input data, which are defined in the configuration folder. 

In case of the dummy configuration, a Monte carlo simulation (n = 10) for 365 exposure events per year for three pathogens and the following input parameters will be performed:


```{r echo = FALSE, message = FALSE, warnings = FALSE }
simulated <- config$inflow[config$inflow$simulate == 1, 1:3]
knitr::kable(simulated,caption = "Simulated pathogens for QMRA (defined in: 'inflow.csv' with 
             simulated = 1):")

knitr::kable(config$inflow[config$inflow$PathogenName %in% simulated$PathogenName,
 c("PathogenID","PathogenName", "PathogenGroup", "type", "min", "max")], 
 caption = "Inflow concentrations (defined in: 'inflow.csv') for pathogens used for QMRA:")

knitr::kable(config$treatment$schemes,
 caption = "Treatment schemes (defined in: 'treatment_schemes.csv') used for QMRA:")

knitr::kable(config$treatment$processes[config$treatment$processes$TreatmentID %in% config$treatment$schemes$TreatmentID,
c("TreatmentID","TreatmentName", "TreatmentGroup", "PathogenGroup", "type", "min", "max")], 
 caption = "Treatment processes (defined in: 'treatment_proecesses.csv') and assumed log-reductions used for QMRA (from [WHO, 2011](http://apps.who.int/iris/bitstream/10665/44584/1/9789241548151_eng.pdf#page=162)):")


exposure <- list(number_of_repeatings = as.numeric(config$exposure[config$exposure$name == "number_of_repeatings","value"]),
                 number_of_exposures =  as.numeric(config$exposure[config$exposure$name == "number_of_exposures","value"]),
                 volume_perEvent = config$exposure[config$exposure$name == "volume_perEvent",]
                 )


knitr::kable(exposure$volume_perEvent[,c("name", "type", "min", "max", "mode")],
 caption = "Ingested volume per event (defined in: row 3 'volume_perEvent' in 'exposure.csv') (source: own assumption)):")


volume_per_event <- kwb.qmra::create_random_distribution(type = exposure$volume_perEvent$type, 
                                     number_of_repeatings = exposure$number_of_repeatings,
                                     number_of_events = exposure$number_of_exposures, 
                                     value = exposure$volume_perEvent$value, 
                                     min = exposure$volume_perEvent$min, 
                                     max = exposure$volume_perEvent$max, 
                                     mode = exposure$volume_perEvent$mode, 
                                     mean = exposure$volume_perEvent$mean,debug = FALSE)


knitr::kable(config$doseresponse[config$doseresponse$PathogenName %in% simulated$PathogenName,], 
              caption = "Dose-response models (defined in: 'doseresponse.csv') used for QMRA (from [QMRAwiki](http://qmrawiki.canr.msu.edu/index.php/Quantitative_Microbial_Risk_Assessment_(QMRA)_Wiki)):")

knitr::kable(config$health[config$health$PathogenName %in% simulated$PathogenName,], 
              caption = "Health parameters (defined in: 'health.csv') for simulated pathogens (from [WHO, 2011](http://apps.who.int/iris/bitstream/10665/44584/1/9789241548151_eng.pdf#page=132)):")


```

# 4 Run risk calculation

Subsequently the risk calculation can be performed in R(Studio) by executing the 
following code, which uses the `config` that was imported and inspected above:

```{r}
risk <- kwb.qmra::simulate_risk(config)
```

All (input & output) data will be saved in the resulting R object `risk` which an be easily 
inspected by the user, e.g.: 


**Input data**


```{r}
str(risk$input, 1)
```


**Output data**


```{r}
str(risk$output, 1)
```

Thus the user has access to all 

# 5 Visualise results 

Finally the results of the QMRA can be visualised for each system component as shown 
below:


## 5.1 Inflow
```{r, fig.width = 12,  fig.cap="Simulated inflow concentrations", warning= FALSE}
kwb.qmra::plot_inflow(risk)
```

## 5.2 Treatment
```{r, fig.width = 12, fig.cap="Simulated reductions in the treatment plant", warning= FALSE}
kwb.qmra::plot_reduction(risk)
```

## 5.3 Effluent
```{r, fig.width = 12, fig.cap="Simulated effluent concentrations", warning= FALSE}
kwb.qmra::plot_effluent(risk)
```

## 5.4 Exposure 
```{r, fig.width = 12, fig.cap="Simulated dose per event", warning= FALSE}
kwb.qmra::plot_event_dose(risk)
```

```{r, fig.width = 12, fig.cap="Simulated ingested volume per event", warning= FALSE}
kwb.qmra::plot_event_volume(risk)
```

## 5.5 Health results 

### 5.5.1 Per event
```{r, fig.width = 12, fig.cap="Simulated infection probability per event", warning= FALSE}
kwb.qmra::plot_event_infectionProb(risk)
```

```{r, fig.width = 12, fig.cap="Simulated illness probability per event", warning= FALSE}
kwb.qmra::plot_event_illnessProb(risk)
```
```{r, fig.width = 12, fig.cap="Simulated DALYs per event", warning= FALSE}
kwb.qmra::plot_event_dalys(risk)
```

### 5.5.2 Total 

```{r, fig.width = 12, fig.cap="Simulated total infection probability (for all events)", warning= FALSE}
kwb.qmra::plot_total_infectionProb(risk)
```

```{r, fig.width = 12, fig.cap="Simulated total illness probability (for all events", warning= FALSE}
kwb.qmra::plot_total_illnessProb(risk)
```

```{r, fig.width = 12, fig.cap="Simulated total DALYs (for all events)", warning= FALSE}
kwb.qmra::plot_total_dalys(risk)
```

In addtion tables with summary statistics, e.g. for the **total risk** can be generated easily 
as shown below:

```{r, echo = FALSE}
knitr::kable(risk$output$total[risk$output$total$repeatID==1, ], 
             caption = "Total risk (for first repeat of random generation)")
```

## 6 Export results 


E.g. Write reports for all configurations in package subfolder `extdata/configs/` (here: 
`r system.file("extdata/configs/", package = "kwb.qmra")`):

```{r eval = FALSE}
confDirs <- system.file("extdata/configs/", package = "kwb.qmra")
kwb.qmra::report_workflow(confDirs)

```

```{r eval = FALSE}


```