---
title: "Tutorial (Umberto NXT, v7.1.0.13.503)"
author: "Michael Rustler"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Tutorial (Umberto NXT, v7.1.0.13.503)}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

# 1 Install R packages

```{r, eval = FALSE}
# Enable repository from kwb-r
options(repos = c(
  kwbr = 'https://kwb-r.r-universe.dev',
  CRAN = 'https://cloud.r-project.org'))

# Download and install kwb.umberto in R
install.packages('kwb.umberto')
```

# 2 Load the R package kwb.umberto

```{r, fig.show='hold'}
library(kwb.umberto)
```

# 3 Import data   

## 3.1 Directory with example .csv files 

The example .csv files (in German format, i.e. decimals are indicated with `,` 
and `;` is used as field separator) were exported from Umberto NXT (v7.1.0.13.503) 
and attached to the R package `kwb.umberto` as shown below:

```{r}
zipfile <- system.file("extdata/umberto-nxt_v7.1.0.13.503/Beispiel_Auswertung.zip", 
                       package = "kwb.umberto")
temp <- file.path(tempdir(), "Beispiel_Auswertung")
unzip(zipfile, exdir = temp)

dir(temp, pattern = ".csv")
```

## 3.2 Getting the data into R 

Using the function `kwb.umberto::import_rawdata()` and specifying the parameter 
`csv_dir` =  `temp`) imports the model results from one .csv file 
that is located in the folder `r temp`.  

```{r}
rawdata <- kwb.umberto::import_rawdata(csv_dir = temp)
```

To access the structure of the imported data one can run the following command: 

```{r}
head(rawdata)
```

# 3.3 Data aggregation

Once the data is imported into R, it can be aggregated as shown in the subsequent
subchapters.

## 3.3.1 Grouping

```{r}
data_grouped <- kwb.umberto::group_data(rawdata)
head(data_grouped)
```

## 3.3.2 Making pivot data
```{r}
data_pivot <- kwb.umberto::pivot_data(data_grouped)
head(data_pivot)
```

```{r}
data_pivot_list <- kwb.umberto::create_pivot_list(data_pivot)
head(data_pivot)
```

# 4 Data export

Finally the resulting data can be exported to an EXCEL spreatsheet. For each 
`lci_method` available in the imported dataset a sheet named `lci_method_1` to 
`lci_method_9` will be created, as there are 9 distinct `lci_method` available 
for this example data set: 

`r sprintf("\n- %s\n", unique(rawdata$lci_method))`


```{r}
export_path <- file.path(temp, "results.xlsx")
print(sprintf("Exporting aggregated results to %s", export_path))
write_xlsx(data_pivot_list, 
           path = export_path)
```           

# 5 Data visualisation

In addition a simple visualisation of the imported and grouped data can 
be performed by calling the function `kwb.umberto::plot_results()` as shown below:

```{r}
rawdata <- kwb.umberto::import_rawdata(csv_dir = temp)
data_grouped <- kwb.umberto::group_data(rawdata)
kwb.umberto::plot_results(grouped_data = data_grouped)
```