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xplorerr

Tools for interactive data analysis

CRAN_Status_Badge cran checks R build status status Lifecycle: stable

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

# 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.

app_descriptive()

Visualize Probability Distributions

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

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.

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.

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.

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.

app_rfm_analysis()

Data Visualization

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

app_visualizer()

About

Shiny apps for interactive data analysis, visualization and modeling.

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MIT
LICENSE.md

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