{"id":20103573,"url":"https://github.com/marios-mamalis/mca-visualisation","last_synced_at":"2026-06-29T07:31:52.711Z","repository":{"id":250221494,"uuid":"192526134","full_name":"Marios-Mamalis/mca-visualisation","owner":"Marios-Mamalis","description":"A script for automatic visualisation of Multiple Correspondence Analysis (MCA) results from FactoMineR in 3 dimensions using Plotly (exported as html)","archived":false,"fork":false,"pushed_at":"2020-07-05T10:12:58.000Z","size":26,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-01-13T04:42:05.641Z","etag":null,"topics":["3d-scatterplots","correspondence-analysis","data-analysis","factominer","html","mca","multiple-correspondence-analysis","plotly","visualisation"],"latest_commit_sha":null,"homepage":"","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/Marios-Mamalis.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":"2019-06-18T11:24:42.000Z","updated_at":"2024-08-25T14:11:26.000Z","dependencies_parsed_at":"2024-07-26T00:14:15.033Z","dependency_job_id":"820bc364-6de1-42dd-ba90-05b6ffeeb432","html_url":"https://github.com/Marios-Mamalis/mca-visualisation","commit_stats":null,"previous_names":["marios-mamalis/visualisation-of-mca-results-from-factominer-using-plotly","marios-mamalis/mca-visualisation"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Marios-Mamalis%2Fmca-visualisation","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Marios-Mamalis%2Fmca-visualisation/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Marios-Mamalis%2Fmca-visualisation/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Marios-Mamalis%2Fmca-visualisation/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Marios-Mamalis","download_url":"https://codeload.github.com/Marios-Mamalis/mca-visualisation/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241543137,"owners_count":19979475,"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":["3d-scatterplots","correspondence-analysis","data-analysis","factominer","html","mca","multiple-correspondence-analysis","plotly","visualisation"],"created_at":"2024-11-13T17:37:06.315Z","updated_at":"2026-06-29T07:31:52.704Z","avatar_url":"https://github.com/Marios-Mamalis.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003ch1 align=\"center\"\u003e Visualisation of Multiple Correspondence Analysis (MCA) results from FactoMineR using Plotly\u003c/h1\u003e\n\n\u003ch3\u003eSummary\u003c/h3\u003e\n\nThis is a script for automatic visualisation of MCA results from FactoMineR in 3 dimensions using Plotly (exported as html). It contains a function called `plotfun()` that transforms the results of FactoMineR's MCA function `FactoMineR::MCA()` to a structure that can be used by Plotly, and then creates and exports in html format six 3d scatterplots, namely: \n\n- Contributions of Categories\n- Contributions of Individuals\n- Coordinates of Categories\n- Coordinates of Individuals\n- Cosine Squared of Categories\n- Cosine Squared of Individuals\n\n\u003ch3\u003eUsage\u003c/h3\u003e\n\nThe function, in order to work, must be supplied with two arguments:\n1) A list that contains the results of an MCA function of FactoMineR\n2) One of three valid plotting methods of Plotly's 3d scatterplot (\"lines\", \"markers\" or \"linesmarkers\").\n\nex. `plotfun(results.MCA, \"lines\")`\n\n\u003ch3\u003eResults\u003c/h3\u003e\n\nThe 3d scatterplots' axes are:\n\n- x-dimension: The dimension\n- y-dimension: Name of Category or Individual\n- z-dimension: The value of each dimension for either Contributions, Coordinates or Cosine Squared\n\nExample output:\n\n![Example Result](https://user-images.githubusercontent.com/46795338/64366027-0c91f880-d01e-11e9-822b-473e771e0824.jpg)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmarios-mamalis%2Fmca-visualisation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmarios-mamalis%2Fmca-visualisation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmarios-mamalis%2Fmca-visualisation/lists"}