README.Rmd

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output: github_document
---

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knitr::opts_chunk$set(
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)
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# x2y

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The goal of {x2y} is to provide column-to-column mutual information gain for large dataframe, so that you can 
remove noise or redundant column based on their information gain for a machine learning modeling.
 
This is a generalization of the fantastic [RViews blog post from Rama Ramakrishnan](https://rviews.rstudio.com/2021/04/15/an-alternative-to-the-correlation-coefficient-that-works-for-numeric-and-categorical-variables/) 

## Installation

You can install the development version of x2y from [GitHub](https://github.com/) with:

``` r
# install.packages("pak")
pak::pak("cregouby/x2y")
```

## Example

This is a basic example which shows you how to solve a common problem:

```{r example}
library(x2y)
library(ggplot2)
## basic example code
set.seed(42)
x <- seq(-1,1,0.01)

circular_df <- data.frame(x = x,
                          y = sqrt(1 - x^2) + rnorm(length(x),mean = 0, sd = 0.05),
                          z = rnorm(length(x))
)

ggplot(circular_df, aes(x = x, y = y)) +
  geom_point() 

```

Here is the mutual information gain provided by {x2y} for this example

```{r circular df}
dx2y(circular_df)
```

## Related work

- [{lares} package](https://laresbernardo.github.io/lares/) is having the `x2y()` function from the same inspiration.