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https://github.com/rsquaredacademy/xplorerr
Shiny apps for interactive data analysis, visualization and modeling.
https://github.com/rsquaredacademy/xplorerr
data exploration r rstats shiny-apps statistics visualization
Last synced: 3 days ago
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Shiny apps for interactive data analysis, visualization and modeling.
- Host: GitHub
- URL: https://github.com/rsquaredacademy/xplorerr
- Owner: rsquaredacademy
- License: other
- Created: 2017-09-08T04:38:06.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2024-10-31T07:43:04.000Z (2 months ago)
- Last Synced: 2025-01-02T15:12:21.773Z (10 days ago)
- Topics: data, exploration, r, rstats, shiny-apps, statistics, visualization
- Language: R
- Homepage: https://xplorerr.rsquaredacademy.com/
- Size: 36.9 MB
- Stars: 38
- Watchers: 4
- Forks: 12
- Open Issues: 14
-
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS.md
- License: LICENSE
Awesome Lists containing this project
- jimsghstars - rsquaredacademy/xplorerr - Shiny apps for interactive data analysis, visualization and modeling. (R)
README
---
output: github_document
---
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
```
# xplorerr
> Tools for interactive data analysis
[](https://cran.r-project.org/package=xplorerr)
[](https://github.com/rsquaredacademy/xplorerr/actions/workflows/R-CMD-check.yaml)
## 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()
```