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https://github.com/joachim-gassen/ExPanDaR

R Package for Interactive Panel Data Exploration
https://github.com/joachim-gassen/ExPanDaR

accounting eda exploratory-data-analysis finance open-science package r replication shiny shiny-apps

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R Package for Interactive Panel Data Exploration

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README 

        
---

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/joachim-gassen/ExPanDaR.svg?branch=master)](https://travis-ci.org/joachim-gassen/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("joachim-gassen/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://joachim-gassen.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://joachim-gassen.github.io/ExPanDaR) for the CRAN version and 

[here](https://joachim-gassen.github.io/ExPanDaR/dev) for the current development version.



Enjoy!