{"id":17657411,"url":"https://github.com/dicook/tutorial_effective_data_plots","last_synced_at":"2026-01-23T07:05:14.231Z","repository":{"id":259113569,"uuid":"875058912","full_name":"dicook/tutorial_effective_data_plots","owner":"dicook","description":"Materials for WOMBAT 2024 tutorial","archived":false,"fork":false,"pushed_at":"2024-10-21T23:13:40.000Z","size":46810,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-05-07T11:53:38.877Z","etag":null,"topics":["data","graphics","inference","statistics","tidyverse","visualisation"],"latest_commit_sha":null,"homepage":"https://dicook.github.io/tutorial_effective_data_plots/","language":"JavaScript","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/dicook.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2024-10-19T02:11:35.000Z","updated_at":"2025-03-22T08:13:06.000Z","dependencies_parsed_at":"2024-10-22T21:22:59.808Z","dependency_job_id":null,"html_url":"https://github.com/dicook/tutorial_effective_data_plots","commit_stats":null,"previous_names":["dicook/tutorial_effective_data_plots"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/dicook/tutorial_effective_data_plots","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dicook%2Ftutorial_effective_data_plots","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dicook%2Ftutorial_effective_data_plots/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dicook%2Ftutorial_effective_data_plots/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dicook%2Ftutorial_effective_data_plots/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dicook","download_url":"https://codeload.github.com/dicook/tutorial_effective_data_plots/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dicook%2Ftutorial_effective_data_plots/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28682284,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-23T05:48:07.525Z","status":"ssl_error","status_checked_at":"2026-01-23T05:48:07.129Z","response_time":59,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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","graphics","inference","statistics","tidyverse","visualisation"],"created_at":"2024-10-23T14:40:42.940Z","updated_at":"2026-01-23T07:05:14.216Z","avatar_url":"https://github.com/dicook.png","language":"JavaScript","funding_links":[],"categories":[],"sub_categories":[],"readme":"# [WOMBAT 2024](https://wombat2024.numbat.space) Tutorial: Creating data plots for effective decision-making using statistical inference with R \n\n\u003cimg src=\"wombat-2024.png\" align=\"right\" width=\"150\" /\u003e\n\nWebsite: [https://dicook.github.io/tutorial_effective_data_plots/](https://dicook.github.io/tutorial_effective_data_plots/)\n\nThis is for statisticians and data science practitioners who are interested in improving their data visualisation skills. \n\n**Presenter**: Dianne Cook is Professor of Business Analytics at Monash University in Melbourne, Australia.  She is a world leader in data visualisation, especially the visualisation of high-dimensional data using tours with low-dimensional projections, and projection pursuit.  She is currently focusing on bridging the gap between exploratory graphics and statistical inference.  Di is a Fellow of the American Statistical Association, past editor of the Journal of Computational and Graphical Statistics, current editor of the R Journal, elected Ordinary Member of the R Foundation, and elected member of the International Statistical Institute.\n\n## Structure of tutorial\n\n- Review of making effective plots using ggplot2's grammar of graphics:\n    - Organising your data to enable mapping variables to graphical elements, \n    - Common plot descriptions as scripts,\n    - Do's and don'ts following cognitive perception principles.\n- Making decisions and inferential statements based on data plots\n    - What is your plot testing? Determining the hypothesis based on the type of plot.\n    - Creating null samples to build lineups for comparison and testing.\n    - Conducting a lineup test using your friends to determine whether what you see is real or spurious, and to determine the best design for your plot.\n\nBackground: Participants should have a good working knowledge of R, and tidy verse, and some experience with ggplot2. Familiarity with the material in [R4DS](https://r4ds.hadley.nz) is helpful.\n\n## Course Schedule\n\n| time | topic |\n|------|-------|\n|1:30-1:45|\tWhy, philosophy and benefits|\n|1:45-2:05|\tOrganising data to map variables to plots|\n|2:05-2:35|\tMaking a variety of plots|\n|2:35-3:00|\tDo but don’t, and cognitive principles|\n|2:30-3:00|\tBREAK|\n|3:00-3:20|\tWhat is your plot testing?|\n|3:20-3:35|\tCreating null samples|\n|3:35-4:00|\tConducting a lineup test|\n|4:00-4:30|\tTesting for best plot design|\n\n[Session 1 Slides](https://dicook.github.io/tutorial_effective_data_plots/slides1.html)\n\n[Session 2 Slides](https://dicook.github.io/tutorial_effective_data_plots/slides2.html)\n\n[Zip file of materials](https://dicook.github.io/tutorial_effective_data_plots/tutorial.zip)\n\n## Getting started\n\n1. You should have a reasonably up to date version of R and R Studio, eg RStudio 2024.09.0 +375 and R version 4.4.1 (2024-06-14). Install the following packages, and their dependencies.\n\n```\ninstall.packages(c(\"ggplot2\", \"tidyr\", \"dplyr\", \"readr\", \"stringr\", \"nullabor\", \"colorspace\", \"palmerpenguins\", \"broom\", \"ggbeeswarm\", \"vcd\", \"MASS\", \"conflicted\"), dependencies=c(\"Depends\", \"Imports\"))\n```\n\n2. Download the [Zip file of materials](https://dicook.github.io/tutorial_effective_data_plots/tutorial.zip) to your laptop, and unzip it. \n\n3. Download just the R scripts, [slides1.R](https://dicook.github.io/tutorial_effective_data_plots/slides1.R), [slides2.R](https://dicook.github.io/tutorial_effective_data_plots/slides2.R)\n\n4. Open your RStudio be clicking on `tutorial.Rproj`. \n\nGitHub repo with all materials is \n[https://dicook.github.io/tutorial_effective_data_plots/](https://dicook.github.io/tutorial_effective_data_plots/).\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdicook%2Ftutorial_effective_data_plots","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdicook%2Ftutorial_effective_data_plots","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdicook%2Ftutorial_effective_data_plots/lists"}