Tools for interactive data analysis

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

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

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

```
app_descriptive()
```

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

```
app_vistributions()
```

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.

```
app_inference()
```

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.

```
app_linear_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.

```
app_logistic_regression()
```

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.

```
app_rfm_analysis()
```

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

```
app_visualizer()
```

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

**Any scripts or data that you put into this service are public.**

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