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README.Rmd
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---
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 <img src="logo.png" align="right" />
[](http://www.repostatus.org/#active)
[](https://travis-ci.org/joachim-gassen/ExPanDaR)
[](https://cran.r-project.org/package=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!