README.Rmd

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output: github_document
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# reasyeda

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This is a package developed to quickly create interactive visualizations for your data and standardize how they look. Other functionality has also been built in order to check correlation, predictive power score (PPS), and how various transformations might influence your data.

Note: This package was built on R 4.1.2.

## Installation

You can install reasyeda like this:

``` r
# install.packages("devtools")
devtools::install_github("amanda-park/reasyeda")
```

## How to Use

```{r eval = FALSE}
require(reasyeda)

#Load penguins dataset
data(penguins, package = "modeldata")

#Opens Shiny App to Interactively Explore Data
#explore_df(penguins)
```

Upon opening the Shiny app there will be 5 tabs:

* Visualization
* Transformations
* Table
* Predictive Power Score
* Pearson correlations

**Visualization** creates plots of your raw data that executive leaders will find pleasing to the eyes. Most plots are plotly-wrapped ggplots for a more Tableau-like feel, but can be downloaded separately to remove the interactivity component. A variety of color choices are available to make sure the plot fits your organization's preferred color schemes.

**Transformations** will let you see how normally distributed your data looks visually and according to Shapiro-Wilk after applying some common transformations to your numeric variables.

**Table** will let you see your raw data in an interactive datatable for easier exploration at a very granular level.

**Correlations** measures the relationship between numeric variables via Pearson's Correlation and then detecting overall relationships between variables using Predictive Power Score. Plots and Table outputs are provided.