---
title: "The FSS package - use case number 1"
author: Sebastian Neuenfeldt
date: "`r format(Sys.time(), '%d %B %Y')`"
output: 
  word_document:  
    reference_docx: "D:/public/neuenfeldt/MIND_STEP/git/MIND STEP - kind of template.docx"
---

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# The FSS package

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The goal of the FSS package is to provide some functions to analyse (German) Farm Structure Survey (FSS) data.

## Installation

You can install the GitLab version of FSS from [GitLab](https://git-dmz.thuenen.de/neuenfeldt/fss) with:

``` {r}
# devtools::install_git("https://git-dmz.thuenen.de/neuenfeldt/fss") -->
```

## Example

### Load FSS package

```{r}
library(FSS)
```

### Test a function

Here we will test one of the provided functions: `generateFakeFSSData_DE()`. We generate some basic variables, which are automatically produced. We set an additional variable to
be generated, namely C0300. The variable will be a continuous variable.

```{r}
FSS_data_DE <- generateFakeFSSData_DE(C0codes = "C0300")
```
### Add a specific categorical variable

No we will add a specific variable, i.e. a categorical variable if a farm is an organic farm. This will be randomly distributed. The probability of a farm to be organic is 20%.

```{r, message=FALSE}
library(tidyverse)
FSS_data_DE <- FSS_data_DE %>% 
  mutate(C0501=sample(c(0,1),
                      size = nrow(FSS_data_DE),
                      replace = TRUE,
                      prob = c(0.8,0.2)))
```

### Some analysis

After that we analyse the data regarding the number of farms that are organic (C0501) for each year (C0008U1) and farm type (C0060UG1).

```{r}
FSS_data_DE %>% group_by(C0008U1,C0060UG1,C0501) %>% 
  count() %>% 
  pivot_wider(names_from = C0060UG1, values_from = n) %>% 
  knitr::kable()
```
## Conclusion

This could be the final output of an overview of conventional (C0501=0) and organic (C0501=1) farms for each year and the provided farm types. As this is a fake data set, these numbers make no sense.
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