---
title: "Introduce alookr"
author: "Choonghyun Ryu"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Introduce alookr}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r environment, echo = FALSE, message = FALSE, warning=FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "")
options(tibble.print_min = 6L, tibble.print_max = 6L, width = 80)
```

## Overview

Binary classification modeling with `alookr`.

Features:

  - Clean and split data sets to train and test.
  - Create several representative models.
  - Evaluate the performance of the model to select the best model.
  - Support the entire process of developing a binary classification model.

The name `alookr` comes from `looking at the analytics process` in the data analysis process.

## Install alookr

The released version is available on CRAN.

```{r eval = FALSE}
install.packages("alookr")
```

Or you can get the development version without vignettes from GitHub:

```{r eval = FALSE}
devtools::install_github("choonghyunryu/alookr")
```

Or you can get the development version with vignettes from GitHub:

```{r eval = FALSE}
install.packages(c("ISLR", "spelling", "mlbench"))
devtools::install_github("choonghyunryu/alookr", build_vignettes = TRUE)
```

## Usage

alookr includes several vignette files, which we use throughout the documentation.

The provided vignettes are as follows.

* Cleansing the data set
* Split the data into a train set and a test set
* Modeling and Evaluate, Predict

```{r vignettes, eval=FALSE}
browseVignettes(package = "alookr")
```

## How to use alookr package

For information on using the alookr package, refer to the following website.

- [`Introduce alookr`](https://choonghyunryu.github.io/alookr_vignette/)