{"id":18600628,"url":"https://github.com/sandravizz/visual-data-science-r","last_synced_at":"2025-05-16T17:12:04.894Z","repository":{"id":69104844,"uuid":"547289712","full_name":"sandravizz/Visual-data-science-R","owner":"sandravizz","description":"Visual explorative analysis in R from scratch mainly using ggplot","archived":false,"fork":false,"pushed_at":"2024-10-16T16:05:18.000Z","size":1635,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-18T01:25:53.006Z","etag":null,"topics":["data-science","data-visualization","ggplot","r"],"latest_commit_sha":null,"homepage":"https://slides.com/sandraviz/rggplot2","language":"R","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/sandravizz.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}},"created_at":"2022-10-07T12:46:34.000Z","updated_at":"2024-10-16T16:05:23.000Z","dependencies_parsed_at":"2023-04-04T02:48:16.237Z","dependency_job_id":"1c2dac18-5037-4844-ba28-cd4e2f67dd8c","html_url":"https://github.com/sandravizz/Visual-data-science-R","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sandravizz%2FVisual-data-science-R","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sandravizz%2FVisual-data-science-R/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sandravizz%2FVisual-data-science-R/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sandravizz%2FVisual-data-science-R/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sandravizz","download_url":"https://codeload.github.com/sandravizz/Visual-data-science-R/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254573589,"owners_count":22093731,"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","data-visualization","ggplot","r"],"created_at":"2024-11-07T02:04:45.612Z","updated_at":"2025-05-16T17:12:04.877Z","avatar_url":"https://github.com/sandravizz.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Visual data science\n\nPlease check the corresponding [presentation](https://slides.com/sandraviz/rggplot2/embed?style=light). \n\n![Alt text](Images/R1.png)\n\nExplorative data visualisation is a very important part when analysing the structure in our data. It should always be one of the first steps when starting a new data science project. \n\n### Setup \n\nIn case this is your first time using R you need to install your own copy as well as RStudio, a software application that makes R easier to use. Both R and RStudio are free and easy to download. R is maintained by an international team of developers. The top of the web page provides three links for downloading R. Under this [link](https://posit.co/products/open-source/rstudio/) you can download Rstudio \n\n### Introduction \n\nIn this chapter I introduce the concept of the R visualisation package ggplot. Regarding the installation please check the [code](https://github.com/sandravizz/visual-data-science-R/blob/main/Scripts/Installations.R). Afterwards it is the next step to define a theme, please check the [code](https://github.com/sandravizz/visual-data-science-R/blob/main/Scripts/Themes.R).\n\n### Distributions \n\nIn this chapter we look at different visualisation types showing the distribution of one variables. Please check the [code](https://github.com/sandravizz/visual-data-science-R/blob/main/Scripts/Distributions.R).\n\nChart types covered\n\n- Frequency plot \n- Histogram \n- Box-plot\n- Density plot\n\n### Relationships \n\nIn this chapter we look at different visualisation types presenting the relationships between two variables. Please check the [code](https://github.com/sandravizz/visual-data-science-R/blob/main/Scripts/Explorative%20analysis.R).\n\nChart types covered\n\n- Scatter plot \n- Beeswarm plot \n- Hexagonal binning\n- Heatmap\n\n### Time series \n\nTime series data have a very specific structure. The goal is to understand over time patterns (trends) which are often presented as line charts. The focus of this chapter is on the visual analytical flow when investigating patterns over time. Please check the [code](https://github.com/sandravizz/visual-data-science-R/blob/main/Scripts/Time%20series.R).\n\n### Advanced visualisations \n\nIn this chapter I present three more advanced visualisation types as their strucutre is less commonly used and/or cover higer visual complexity. Pleased check the [code](https://github.com/sandravizz/visual-data-science-R/blob/main/Scripts/Advanced%20visualisations.R).\n\nChart types covered\n\n- Parallel coordinates \n- Dumbbell chart\n- Waffle chart\n\n### Markdown \n\nThe markdown option is a good way to save the whole project including comments and summaries. Please check the [code](https://github.com/sandravizz/visual-data-science-R/blob/main/Scripts/Markdown.R).\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsandravizz%2Fvisual-data-science-r","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsandravizz%2Fvisual-data-science-r","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsandravizz%2Fvisual-data-science-r/lists"}