{"id":22325573,"url":"https://github.com/corneliustanui/statsreporter","last_synced_at":"2025-10-09T02:43:27.198Z","repository":{"id":211322389,"uuid":"728706609","full_name":"corneliustanui/StatsReporter","owner":"corneliustanui","description":"An interactive shiny-based data analysis tool.","archived":false,"fork":false,"pushed_at":"2024-01-20T22:47:14.000Z","size":360,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-31T07:29:59.358Z","etag":null,"topics":["data-science","dataanalysis","dataanalytics","r","shiny","shiny-apps"],"latest_commit_sha":null,"homepage":"https://corneliuskiplimo.shinyapps.io/StatsReporter/","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/corneliustanui.png","metadata":{"files":{"readme":"README.md","changelog":null,"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":"2023-12-07T14:15:53.000Z","updated_at":"2023-12-15T12:55:38.000Z","dependencies_parsed_at":"2023-12-26T04:38:22.271Z","dependency_job_id":"2a60b6cb-1c47-4dcf-8e21-04dbc9f3e2c6","html_url":"https://github.com/corneliustanui/StatsReporter","commit_stats":{"total_commits":30,"total_committers":2,"mean_commits":15.0,"dds":0.06666666666666665,"last_synced_commit":"488f8f2236c2cad1757fd7bf0d0c5605a2aeb81c"},"previous_names":["corneliustanui/statsreporter"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/corneliustanui%2FStatsReporter","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/corneliustanui%2FStatsReporter/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/corneliustanui%2FStatsReporter/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/corneliustanui%2FStatsReporter/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/corneliustanui","download_url":"https://codeload.github.com/corneliustanui/StatsReporter/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245598311,"owners_count":20641884,"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-science","dataanalysis","dataanalytics","r","shiny","shiny-apps"],"created_at":"2024-12-04T02:12:39.032Z","updated_at":"2025-10-09T02:43:22.155Z","avatar_url":"https://github.com/corneliustanui.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# QSR\n\n[![Deploy App](https://github.com/corneliustanui/StatsReporter/actions/workflows/deploy-shinyapp.yml/badge.svg)](https://github.com/corneliustanui/StatsReporter/actions/workflows/deploy-shinyapp.yml)\n\nThe [Quick Stats Reporter(QSR)](https://corneliuskiplimo.shinyapps.io/StatsReporter/) generates tabular reports in `.csv`, `.pdf`, \u0026 `.htlm` and graphical reports in `.png`, `.pdf`, \u0026 `.svg` formats for basic descriptive statistical analyses:\n\n### Univariate analysis\n\nThe univariate analysis entails reporting frequencies and percentages of a primary categorical variable, or means, medians, standard deviation, minimum, maximum, and standard error of the mean of a primary numerical variable. Such a table is shown below.\n\n![](./www/primary_table.png)\n\n### Bivariate analysis\n\nThe bivariate analysis entails reporting frequencies and percentages of a primary categorical variable, or means, medians, standard deviation, minimum, maximum, and standard error of the mean of a primary numerical variable by categories of another grouping variable (secondary variable). The analysis also reports test statistics (Chi-square is used when the primary variable is categorical, while Kruskal-Wallis is used when the primary variable is numeric.) Such a table is shown below.\n\n![](./www/secondary_table.png)\n\n### Trivariate analysis\n\nThe trivariate analysis entails reporting frequencies and percentages of a primary categorical variable, or means, medians, standard deviation, minimum, maximum, and standard error of the mean of a primary numerical variable by categories of two grouping variables (secondary and tertiary variables). The analysis also reports test statistics (Chi-square is used when the primary variable is categorical, while Kruskal-Wallis is used when the primary variable is numeric.). The trivariate analysis report is only available in .html and .pdf formats. Such a table is shown below.\n\n![](./www/tertiary_table.png)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcorneliustanui%2Fstatsreporter","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcorneliustanui%2Fstatsreporter","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcorneliustanui%2Fstatsreporter/lists"}