<!-- README.md is generated from README.Rmd. Please edit that file -->

# FSS

Some useful functions for working with (German) farm structure survey
(FSS) data.

# Installation

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

``` r
devtools::install_git("https://git-dmz.thuenen.de/mindstep/fss")

devtools::install_git("https://gitlab.iiasa.ac.at/mind-step/fss")

```

Then the Related R packages can be installed.

``` r
library(FSS)
```

# Example

A little example for one specific function from the FSS package.

## 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")
#> 
#>  Build fake data
```

### 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
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).