README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)
```

# xplorerr 

> Tools for interactive data analysis 

<!-- badges: start -->
[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/xplorerr)](https://cran.r-project.org/package=xplorerr)
[![R-CMD-check](https://github.com/rsquaredacademy/xplorerr/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/rsquaredacademy/xplorerr/actions/workflows/R-CMD-check.yaml)
<!-- badges: end -->

## Overview

xplorerr provides a set of tools for interactive data analysis:

- Descriptive statistics
- Visualize probability distributions
- Inferential statistics
- Linear regression
- Logistic regression
- RFM Analysis
- Data visualization
    - ggplot2
    - plotly
    - rbokeh
    - highcharter

## Installation

```{r gh-installation, eval = FALSE}
# Install release version from CRAN
install.packages("xplorerr")

# Install development version from GitHub
# install.packages("devtools")
devtools::install_github("rsquaredacademy/xplorerr")
```

## Usage

#### Descriptive Statistics

Generate descriptive statistics such as measures of location, dispersion, 
frequency tables, cross tables, group summaries and multiple one/two way tables. 

```{r descriptive, eval=FALSE}
app_descriptive()
```

#### Visualize Probability Distributions

Visualize and compute percentiles/probabilities of normal, t, f, chi square and 
binomial distributions.

```{r vistributions, eval = FALSE}
app_vistributions()
```

#### Inferential Statistics

Select set of parametric and non-parametric statistical tests. 'inferr' builds 
upon the solid set of statistical tests provided in 'stats' package by 
including additional data types as inputs, expanding and restructuring the test 
results. The tests included are t tests, variance tests, proportion tests, chi 
square tests, Levene's test, McNemar Test, Cochran's Q test and Runs test.

```{r inference, eval=FALSE}
app_inference()
```

#### Linear Regression

Tools designed to make it easier for users, particularly beginner/intermediate 
R users to build ordinary least squares regression models. Includes 
comprehensive regression output, heteroskedasticity tests, collinearity 
diagnostics, residual diagnostics, measures of influence, model fit assessment 
and variable selection procedures.


```{r linear, eval=FALSE}
app_linear_regression()
```

#### Logistic Regression

Tools designed to make it easier for beginner and intermediate users to build 
and validate binary logistic regression models. Includes bivariate analysis, 
comprehensive regression output, model fit statistics, variable selection 
procedures, model validation techniques and a 'shiny' app for interactive model 
building.

```{r logistic, eval=FALSE}
app_logistic_regression()
```

#### RFM Analysis

Tools for RFM (recency, frequency and monetary value) analysis. Generate RFM 
score from both transaction and customer level data. Visualize the relationship 
between recency, frequency and monetary value using heatmap, histograms, bar 
charts and scatter plots.

```{r rfm, eval=FALSE}
app_rfm_analysis()
```

#### Data Visualization

Tools for interactive data visualization . Users can visualize data using 
'ggplot2', 'plotly', 'rbokeh' and 'highcharter' libraries. 

```{r visualize, eval=FALSE}
app_visualizer()
```