readme.md

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# fadnUtils

Develop by Dimitrios Kremmydas (JRC) and Xinxin Yang (THÜNEN)

The fadnUtils package facilitates the efficient handling of FADN data within the R language framework. Furthermore, the package is targeted for use within the JRC D.4 context. This means that there is a specific temporal pattern of how a user interacts with the package (see Figure plot). ![plot](inst/examples/pic/workflow.png)

More specifically, after a request for FADN data from DG-AGRI, this data is delivered to JRC D.4 in csv format.

# Installation

You can install the development version from Thuenen or IIASA Gitlab with:

``` r
# Thuenen gitlab
devtools::install_git("https://git-dmz.thuenen.de/mindstep/fadnutilspackages", force = TRUE)
# IIASA gitlab
devtools::install_git("https://gitlab.iiasa.ac.at/mind-step/fadnutilspackage")
```

Then the Related R packages can be installed.

``` r
requiredPackages = c('fadnUtils','data.table', 'devtools','jsonlite', 'ggplot2')
for(p in requiredPackages){
  if(!require(p,character.only = TRUE)) install.packages(p)
  library(p,character.only = TRUE)
}
```

    ## Loading required package: fadnUtils

    ## fadnUtils is loaded.

    ## Loading required package: data.table

    ## Loading required package: devtools

    ## Loading required package: usethis

    ## Loading required package: jsonlite

    ## Loading required package: ggplot2

# Usage in Brief

After loaded the packages, you will have a functional R package on your computer. Then, we will talk about using your package online.

1.  Create a working directory
    -   a user-defined data directory
2.  Import CSV FADN data
    -   convert the csv data into raw r-data
    -   convert raw r-data into str r-data
3.  Load r-data and structured r-data
4.  Perform analysis
5.  Translate between various NUTS Version (FADN Region, NUTS1, NUTS2, NUTS2)

## 1. Create a working directory

Working directory is a path that sets the location of any files your save from R. User sets a working directory. Make sure the relative path stays within `CurrentProjectDirectory`.

``` r
# using a local directory
CurrentProjectDirectory = "D:/public/yang/MIND_STEP/New_test_fadnUtils"
create.data.dir(folder.path = CurrentProjectDirectory)
```

    ## This is already a data.dir structure. Doing nothing.

``` r
set.data.dir(CurrentProjectDirectory)
get.data.dir()
```

    ## [1] "D:/public/yang/MIND_STEP/New_test_fadnUtils"

### Required files

We request FADN data from DG-AGRI, which is delivered to us in csv format. In order to work efficiently with R, we should convert the csv-data to an r friendly format, this step is done with help of a human-readable file, called `raw_str_map.file`. Both files are necessary. `inst/examples` is the folder for use cases that contain fadnUtils package examples and json files.

1.  FADN data in csv format: the data for loading
2.  A json file for extracting the variables

### Folder Structure

A working directory is specified arbitrarily by the user. This structure helps data management and maintenance. The directory looks like this:

``` base
CurrentProjectDirectory/
+-- csv
+-- fadnUtils.metadata.json
+-- rds
\-- spool
    \-- readme.txt
```

-   csv: CSV files are stored here
-   fadnUtils.metadata.json: containing the mapping from the fadn.raw.rds to the fadn.str.rds data
-   rds: placing r-data in the "rds" directory
-   spool: keeping related files

## 2. Import CSV FADN data

First, we will import the data into an R-friendly format using the fadnUtils package.

### Convert the csv data into raw r-data

The raw data will be added to a `rds` directory. We use a convenient function from this package to convert the csv file into raw r-data.

``` r
fadn.data.dir <- "D:/public/data/fadn/lieferung_20210414/csv/"
# load data for country BEL and year 2009
convert.to.fadn.raw.rds(
      file.path =  paste0(fadn.data.dir, "BEL2009.csv"),
      sepS = ",",
      fadn.country = "BEL",
      fadn.year = 2009
      #keep.csv = T # copy csv file in csv.dir
      )
```

    ## File D:/public/data/fadn/lieferung_20210414/csv/BEL2009.csv does not exist. Exiting ...

    ## [1] FALSE

At any time, we can check for the current data dir, what csv files (countries, year) are loaded.

``` r
show.data.dir.contents()
```

### Convert raw r-data into structured r-data

Then, We convert raw data into structured data. Broadly, there are 3 steps to including data in an R package:

1.  setting a structured data in the `structured` directory,
2.  checking the `raw_str_map.file` that all variables can be converted.
3.  converting the structured data successfully into `structured` directory.

#### Set a `structured` directory for saving the structured data

We set a `test` folder to placing the structured data.

``` r
rds.dir = paste0(get.data.dir(),"/rds/")
# set a structured name for for saving the structured r-data in rds.dir
new.str.name = "test"
# set a extraction_dir
dir.create(paste0(rds.dir, new.str.name), showWarnings = FALSE)
new.extraction.dir = paste0(rds.dir, new.str.name)
```

#### Check the variables in the `raw_str_map.file`

Before conversion it is recommended to use `check.column()` method, ensuring that all variables in the`raw_str_map.file` can be converted.

``` r
list_vars = check.column(
              # a rds file or a csv file
              importfilepath = paste0(rds.dir, "fadn.raw.2009.BEL.rds"),
              # a json file
              jsonfile = "D:/public/yang/MIND_STEP/2014_after_copy.json",
              # write a new json file without unmatched variables
              rewrite_json = T,
              # save the new json in extraction_dir
              extraction_dir = new.extraction.dir)
```

#### Convert the raw data into structured r-data using the checked json file

Finally, We can convert a raw r-data to str r-data using a external json file. For more details on converting in fadnUtils packages, `see USE_CASE.R`.

``` r
convert.to.fadn.str.rds(fadn.country = "BEL",
                        fadn.year = 2009,
                        str.name = new.str.name # extraction_dir
)

convert.to.fadn.str.rds(fadn.country = "BEL",
                        fadn.year = 2009,
                        raw_str_map.file = "D:/public/yang/MIND_STEP/new_sample/test01/raw_str_map.json", # a external json file
                        str.name = new.str.name, # extraction_dir
                        force_external_raw_str_map = T,
                        DEBUG = F
                        )
```

#### Files Structure in `rds` folder

After conversion, we can see the `rds` folder:

-   `fadn.raw.2009.BEL.rds`: raw r-data for country "BEL" and year "2009"
-   `test`: extraction\_dir for saving the structured r-data and extracting json file
-   `fadn.str.2009.BEL.rds`: structured s-data for for country of "BEL" and year of "2009"
-   `raw_str_map.json`: default json file
-   `rewrite_2014_after_copy.json`: modified json file after checking the variables

``` base
rds
+-- fadn.raw.2009.BEL.compressed.rds
+-- fadn.raw.2009.BEL.rds
+-- fadn.raw.2010.BEL.compressed.rds
+-- fadn.raw.2010.BEL.rds
+-- fadn.raw.2011.BEL.compressed.rds
+-- fadn.raw.2011.BEL.rds
+-- fadn.raw.2012.BEL.compressed.rds
+-- fadn.raw.2012.BEL.rds
\-- test
     +-- fadn.str.2009.BEL.rds
     +-- raw_str_map.json
     \-- rewrite_2014_after_copy.json
```

## 3. Load raw r-data and structured r-data

In order to initiate any analysis with `fadnUtils`, we first need to load r-data. We can only load data for countries and years that that has already been imported into a data.dir folder.

### Load raw r-data for the country `BEL` and year `2009`

``` r
my.data.2009.raw = load.fadn.raw.rds(
  countries = "BEL",
  years = 2009
)
```

### Load structured data for the country `BEL` and year `2009`

We can load structured from country `BEL` and year `2009`.

``` r
my.data.2009.str = load.fadn.str.rds(
  countries = "BEL",
  years = 2009,
  extraction_dir = "test" # Location of the str r-data
)
```

### Load structured data from all available countries and years.

The following is an example of loading structured data all available countries and years.

``` r
my.str.data = load.fadn.str.rds( extraction_dir = "test")
```

## 4. Perform analysis

Here are some examples to perform data.

### Collection the common id

We can collect the common id from the loaded r-data using `collect.common.id()` function on `fadnUtils`.

``` r
# Collection the common id from loaded structured r-data
collected.common.id_str = collect.common.id(my.str.data)
```

    ## Tranforming  list  to data table....
    ##  [1] 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
    ## 15  year(s) is/are selected.

### Plotting

To build a basic plot, we will use the `ggplot` function using the plotting package `ggplot2`.

``` r
crops.data = my.str.data$crops #catering for easier access at next steps

#this contains the number of crops for each farm-country-year/
#   Be carefule, we hav to filter to count only the LEVL variable
crops.data.Ncrops = crops.data[VARIABLE=="LEVL",.N,by=list(COUNTRY,YEAR,ID)]

# This displays the quantiles of the number of crops
crops.data.Ncrops[,as.list(quantile(N)),by=list(YEAR,COUNTRY)][order(COUNTRY)]

# plot only 2007, 2008, 2009 
ggplot(crops.data.Ncrops %>% filter( YEAR %in% c(2007,2008,2009)),aes(y=N,x=1)) +
  geom_boxplot() +
  facet_grid(YEAR~COUNTRY) +
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),
        strip.text = element_text(size = 8, angle = 90)
  )+
  ylab("Number of Crops")
```

![](README_files/figure-markdown_github/unnamed-chunk-13-1.png)

### Some other examples

``` r
# sample and representend number of farms
my.str.data$info[,list(Nobs_sample=.N,Nobs_represented=sum(WEIGHT)),
                 by=.(COUNTRY,YEAR)]
```

    ##      COUNTRY YEAR Nobs_sample Nobs_represented
    ##   1:     NED 2004        1397            60644
    ##   2:     NED 2005        1446            60598
    ##   3:     NED 2006        1491            60644
    ##   4:     BEL 2007        1168            33315
    ##   5:     BGR 2007        1871           146769
    ##  ---                                          
    ## 329:     SUO 2018         722            34114
    ## 330:     SVE 2018        1010            28884
    ## 331:     SVK 2018         559             4144
    ## 332:     SVN 2018         890            44392
    ## 333:     UKI 2018        2848           100916

``` r
# only for full sample (common id over years in selected data)
my.str.data$info[ID %in% collected.common.id_str[[1]],
                 list(Nobs_sample=.N,
                      Nobs_represented=sum(WEIGHT)),
                 by=.(COUNTRY,YEAR)]
```

    ##     COUNTRY YEAR Nobs_sample Nobs_represented
    ##  1:     NED 2004         446         20358.73
    ##  2:     NED 2005         446         20209.66
    ##  3:     NED 2006         446         19606.76
    ##  4:     NED 2007         446         17748.39
    ##  5:     NED 2008         446         17196.91
    ##  6:     NED 2009         446         16564.05
    ##  7:     NED 2010         446         17407.43
    ##  8:     NED 2011         446         17928.86
    ##  9:     NED 2012         446         16539.63
    ## 10:     NED 2013         446         17078.27
    ## 11:     NED 2014         446         17901.31
    ## 12:     NED 2015         446         16973.80
    ## 13:     NED 2016         446         16961.13
    ## 14:     NED 2017         446         19275.99
    ## 15:     NED 2018         446         17685.72

## 5. Translate the Nomenclature of Territorial Units for Statistics (NUTS) version

The NUTS classification changes every 3-4 years. Changes between various NUTS versions include recoding, merging, splitting of regions or boundary shift. This package provides function for plotting NUTS classification and converting between different NUTS versions.

1.  Plots various regional levels (FADN Region, NUTS1, NUTS2, NUTS3).

``` r
# nuts2 for Germany
nuts.heatmap.group(my.str.data$info, "NUTS2", countries = "DEU", onepage = FALSE)
```

![](README_files/figure-markdown_github/DEU.png) 2. Converts data between the different NUTS versions. This package provides various NUTS tables for converting the lastest NUTS version.

``` r
# get a list of the datasets in an R package
dt_nuts <- data(package = "fadnUtils")
```

**Notices:** Please read `inst/examples/FADN_USE_CASE.R` and `use_case.docx` for more details on using fadnUtils.

# References

1.  History of NUTS: <https://ec.europa.eu/eurostat/en/web/nuts/history>

2.  NUTS Converter web tool: <https://urban.jrc.ec.europa.eu/nutsconverter/#/>