---
title: "Intro"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Intro}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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


The goal of farrell is to provide an interactive interface to Data Envelopment Analysis modeling in R. The farrell package is built upon [Benchmarking](https://CRAN.R-project.org/package=Benchmarking).

## Installation

You can install the farrell package from CRAN with:

```{r, eval = FALSE}

install.packages("farrell")


```



## Example

You can run: 

```{r, eval=FALSE}
library(farrell)

farrell()

```

or if you're working on RStudio, just click on __Addins__ then __farrell__. 


## Data Loading: 


Hit __Browse...__ to upload your data frame in a csv format. All the inputs and outputs must be contained within the data frame (each column for each input/output). Further, the data frame needs to contain an identification column in order to identify Decision Making Units. It can be a numeric or a character column. 

![](fig1.png)


In the following examples, we use the mtcars data frame which has been exported in a csv format with an additional column: __cars name__. 



![](fig2.png)


## Model Tuning


![](fig3.png)

Within the Model Tuning tab, you will select the input and output variables, then you determine your identification column. Then you choose the Returns to Scale assumption between: crs, vrs, irs, drs, add and fdh. After that, you determine the orientation of the model, whether input or output. Finally, hit __Calculate Efficiency__ to get the results. 

Let's for example consider __mpg__ and __disp__ as the output variables and __wt__ as input. We choose __cars name__ as the identification column and model an input-oriented model with crs assumption. 



![](fig4.png)

## Efficiency Results


The Efficiency Results tab displays the efficiency scores along with the peers for each unit in a descending order. You have the ability to download the result in a csv format. The tab also provides a summary of the distribution of the efficiency scores. 


![](fig5.png)


## Lambdas


In the Lambdas tab, you get the contribution of the peers to the inefficient units' score. 


![](fig6.png)

## Scale Efficiency 


The SE tab provides the Scale Efficiency score of each DMU under consideration. 

![](fig7.png)

## Slacks

The Slacks tab displays a data frame containing the sum of the slacks and the slacks for each input/output variables.

![](fig8.png)

## Citation 

If you use the farrell package in your publications or teaching activities, please cite it as follows:


 Mohamed El Fodil Ihaddaden (2020). farrell: Interactive
  Interface to Data Envelopment Analysis Modeling. R package version 0.2.0.
  https://github.com/feddelegrand7/farrell

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {farrell: Interactive Interface to Data Envelopment Analysis Modeling},
    author = {Mohamed El Fodil Ihaddaden},
    note = {R package version 0.2.0},
    year={2020},
    url = {https://github.com/feddelegrand7/farrell},
  }

## Code of Conduct
  
Please note that the farrell project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.

