expandar

R Package for Interactive Panel Data Exploration

https://github.com/trr266/ExPanDaR

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R Package for Interactive Panel Data Exploration

README.Rmd

---
output: github_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, fig.align = "center", table.align = "center", warnings = FALSE)
library(ExPanDaR)
library(knitr)
```

# ExPanDaR: Explore Your Data Interactively 

[![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](http://www.repostatus.org/badges/latest/active.svg)](http://www.repostatus.org/#active)
[![Travis-CI Build Status](https://travis-ci.org/trr266/ExPanDaR.svg?branch=master)](https://travis-ci.org/trr266/ExPanDaR)
[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/ExPanDaR)](https://cran.r-project.org/package=ExPanDaR)
[![CRAN\_Download\_Badge](http://cranlogs.r-pkg.org/badges/grand-total/ExPanDaR)](https://cran.r-project.org/package=ExPanDaR)

## Overview

You are visiting the github repository of the ExPanDaR (Explore Panel Data with R) package. ExPanDaR provides the code base for the ExPanD web app. ExPanD is a shiny based app supporting interactive exploratory data analysis.

ExPanD has two purposes:

- Provide a toolbox for researchers to explore data on the fly, now also
allowing them to download R notebook code that reflects their analysis. 
- Enable users to assess the robustness of empirical evidence without providing them with access to the underlying data.

While I hope that ExPanD will be particularly helpful in the academic review, publication and replication process I also think that it is convenient for typical exploratory data analysis workflows. In addition, it has already proven to be helpful in the classroom.

This is what ExPanD looks like:

```{r, out.width = "90%", fig.align="center", fig.border = "none", echo=FALSE}
knitr::include_graphics("vignettes/figures/ExPanD_simple_03.jpg")
```

If you are interested to see what ExPanD has to offer without diving into R, click [here](https://jgassen.shinyapps.io/expand_wb/) to explore an instance of ExPanD that hosts World Bank data or click
[here](https://jgassen.shinyapps.io/expand_r3/) for a financial accounting and stock returns dataset of U.S. firms.

To see how ExPanD can be customized, take a look at [this blog post](https://joachim-gassen.github.io/2019/04/customize-your-interactive-eda-explore-the-fuel-economy-of-the-u.s.-car-market/) that generates [this display of the development of fuel economy in the U.S. car market](https://jgassen.shinyapps.io/expand_fuel_economy).

If you want to analyze your own data instead, you can also access a variant of ExPanD app [here](https://jgassen.shinyapps.io/expand/) that allows user-side data uploads. No worries: Your data won't be stored on the server and will get erased from memory as soon as you close the web connection. 


## Installation

If you are in for the full treat and want to test ExPanD from within R, run the following 
in your R session to install the ExPanDaR package from CRAN.

```{r, eval=FALSE}
install.packages("ExPanDaR")
library(ExPanDaR)
````

Or, if you want to install the current development version from Github:

```{r, eval=FALSE}
if (!require("devtools")) {
  install.packages("devtools")
}
devtools::install_github("trr266/ExPanDaR")
library(ExPanDaR)
````


## Basic Usage

You can either start ExPanD without arguments so that it starts with a file upload dialog...

```{r, eval=FALSE}
ExPanD()
```

...or use it to explore a cross-sectional data frame with at least two numeric
variables...

```{r, eval=FALSE}
ExPanD(mtcars)
````

...or start with one of the two example datasets that come with the package to
understand hot to use it on long-format panel data.

Please note: The last parameter (`export_nb_option`) allows the user to download
a notebook and the data to continue the analysis in R. Maybe not the best idea
if you are hosting your app publicly and want to keep its data private.

```{r, eval=FALSE}
ExPanD(df = worldbank,  
       df_def = worldbank_data_def, 
       var_def = worldbank_var_def,
       df_name = "World Bank Data",
       config_list = ExPanD_config_worldbank,
       export_nb_option = TRUE)

ExPanD(df = russell_3000,  
       df_def = russell_3000_data_def, 
       df_name = "Russell 3000",
       config_list = ExPanD_config_russell_3000,
       export_nb_option = TRUE)
````

Some additional information on how to use ExPanD can be found in the code file `ExPanDaR_examples.R` in the root directory.

## Use ExPanDaR functions in your own EDA workflow

Besides providing the ExPanD app, ExPanDaR comes with a set of functions that might be helpful in your own exploratory data analysis workflow, e.g., functions to quickly produce standard tables and plots. See [this vignette](https://trr266.github.io/ExPanDaR/articles/ExPanDaR-functions.html) for a quick walk-trough.


## Further Information

For further information, please refer to the articles and function call references of the package documentation, available 
[here](https://trr266.github.io/ExPanDaR) for the CRAN version and 
[here](https://trr266.github.io/ExPanDaR/dev) for the current development version.

Enjoy!

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