{"id":14067815,"url":"https://github.com/rsquaredacademy/xplorerr","last_synced_at":"2025-07-02T09:32:18.676Z","repository":{"id":51149006,"uuid":"102818176","full_name":"rsquaredacademy/xplorerr","owner":"rsquaredacademy","description":"Shiny apps for interactive data analysis, visualization and modeling.","archived":false,"fork":false,"pushed_at":"2024-10-31T07:43:04.000Z","size":38708,"stargazers_count":37,"open_issues_count":14,"forks_count":12,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-06-25T16:51:00.998Z","etag":null,"topics":["data","exploration","r","rstats","shiny-apps","statistics","visualization"],"latest_commit_sha":null,"homepage":"https://xplorerr.rsquaredacademy.com/","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/rsquaredacademy.png","metadata":{"files":{"readme":"README.Rmd","changelog":"NEWS.md","contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2017-09-08T04:38:06.000Z","updated_at":"2025-06-22T16:19:32.000Z","dependencies_parsed_at":"2024-10-28T13:01:17.414Z","dependency_job_id":"9d9077d2-fce8-4eb5-aef0-3a1e44ff9393","html_url":"https://github.com/rsquaredacademy/xplorerr","commit_stats":{"total_commits":154,"total_committers":1,"mean_commits":154.0,"dds":0.0,"last_synced_commit":"f78b47a3310bb5600813407d2a4e711a3041846f"},"previous_names":[],"tags_count":4,"template":false,"template_full_name":null,"purl":"pkg:github/rsquaredacademy/xplorerr","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rsquaredacademy%2Fxplorerr","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rsquaredacademy%2Fxplorerr/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rsquaredacademy%2Fxplorerr/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rsquaredacademy%2Fxplorerr/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/rsquaredacademy","download_url":"https://codeload.github.com/rsquaredacademy/xplorerr/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rsquaredacademy%2Fxplorerr/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":261945319,"owners_count":23234228,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data","exploration","r","rstats","shiny-apps","statistics","visualization"],"created_at":"2024-08-13T07:05:47.691Z","updated_at":"2025-07-02T09:32:18.651Z","avatar_url":"https://github.com/rsquaredacademy.png","language":"R","funding_links":[],"categories":["R"],"sub_categories":[],"readme":"---\noutput: github_document\n---\n\n\u003c!-- README.md is generated from README.Rmd. Please edit that file --\u003e\n\n```{r, echo = FALSE}\nknitr::opts_chunk$set(\n  collapse = TRUE,\n  comment = \"#\u003e\",\n  fig.path = \"README-\"\n)\n```\n\n# xplorerr \n\n\u003e Tools for interactive data analysis \n\n\u003c!-- badges: start --\u003e\n[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/xplorerr)](https://cran.r-project.org/package=xplorerr)\n[![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)\n\u003c!-- badges: end --\u003e\n\n## Overview\n\nxplorerr provides a set of tools for interactive data analysis:\n\n- Descriptive statistics\n- Visualize probability distributions\n- Inferential statistics\n- Linear regression\n- Logistic regression\n- RFM Analysis\n- Data visualization\n    - ggplot2\n    - plotly\n    - rbokeh\n    - highcharter\n\n## Installation\n\n```{r gh-installation, eval = FALSE}\n# Install release version from CRAN\ninstall.packages(\"xplorerr\")\n\n# Install development version from GitHub\n# install.packages(\"devtools\")\ndevtools::install_github(\"rsquaredacademy/xplorerr\")\n```\n\n## Usage\n\n#### Descriptive Statistics\n\nGenerate descriptive statistics such as measures of location, dispersion, \nfrequency tables, cross tables, group summaries and multiple one/two way tables. \n\n```{r descriptive, eval=FALSE}\napp_descriptive()\n```\n\n#### Visualize Probability Distributions\n\nVisualize and compute percentiles/probabilities of normal, t, f, chi square and \nbinomial distributions.\n\n```{r vistributions, eval = FALSE}\napp_vistributions()\n```\n\n#### Inferential Statistics\n\nSelect set of parametric and non-parametric statistical tests. 'inferr' builds \nupon the solid set of statistical tests provided in 'stats' package by \nincluding additional data types as inputs, expanding and restructuring the test \nresults. The tests included are t tests, variance tests, proportion tests, chi \nsquare tests, Levene's test, McNemar Test, Cochran's Q test and Runs test.\n\n```{r inference, eval=FALSE}\napp_inference()\n```\n\n#### Linear Regression\n\nTools designed to make it easier for users, particularly beginner/intermediate \nR users to build ordinary least squares regression models. Includes \ncomprehensive regression output, heteroskedasticity tests, collinearity \ndiagnostics, residual diagnostics, measures of influence, model fit assessment \nand variable selection procedures.\n\n\n```{r linear, eval=FALSE}\napp_linear_regression()\n```\n\n#### Logistic Regression\n\nTools designed to make it easier for beginner and intermediate users to build \nand validate binary logistic regression models. Includes bivariate analysis, \ncomprehensive regression output, model fit statistics, variable selection \nprocedures, model validation techniques and a 'shiny' app for interactive model \nbuilding.\n\n```{r logistic, eval=FALSE}\napp_logistic_regression()\n```\n\n#### RFM Analysis\n\nTools for RFM (recency, frequency and monetary value) analysis. Generate RFM \nscore from both transaction and customer level data. Visualize the relationship \nbetween recency, frequency and monetary value using heatmap, histograms, bar \ncharts and scatter plots.\n\n```{r rfm, eval=FALSE}\napp_rfm_analysis()\n```\n\n#### Data Visualization\n\nTools for interactive data visualization . Users can visualize data using \n'ggplot2', 'plotly', 'rbokeh' and 'highcharter' libraries. \n\n```{r visualize, eval=FALSE}\napp_visualizer()\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frsquaredacademy%2Fxplorerr","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frsquaredacademy%2Fxplorerr","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frsquaredacademy%2Fxplorerr/lists"}