{"id":24536104,"url":"https://github.com/petzi53/ldsr","last_synced_at":"2025-10-06T19:50:27.462Z","repository":{"id":83019011,"uuid":"437354121","full_name":"petzi53/ldsr","owner":"petzi53","description":"Learning Data Science with R (ldsr)","archived":false,"fork":false,"pushed_at":"2022-01-02T09:17:52.000Z","size":1562,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-01-22T13:52:43.459Z","etag":null,"topics":["data-science","learning-by-doing","notes","personal-project","r"],"latest_commit_sha":null,"homepage":"","language":"HTML","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/petzi53.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":"2021-12-11T18:04:37.000Z","updated_at":"2022-01-02T09:17:55.000Z","dependencies_parsed_at":null,"dependency_job_id":"3c19182f-2607-47cb-b2bd-6b01fe4e27bb","html_url":"https://github.com/petzi53/ldsr","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/petzi53%2Fldsr","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/petzi53%2Fldsr/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/petzi53%2Fldsr/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/petzi53%2Fldsr/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/petzi53","download_url":"https://codeload.github.com/petzi53/ldsr/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243809888,"owners_count":20351407,"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","learning-by-doing","notes","personal-project","r"],"created_at":"2025-01-22T13:52:15.552Z","updated_at":"2025-10-06T19:50:22.403Z","avatar_url":"https://github.com/petzi53.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Learning Data Science with R (ldsr)\n\nThis bookdown project will function as my personal notebook for learning data science with R. \n\nJudging my knowledge state personally, I believe I am currently (December 2021) on an intermediate level in R. This in-between status of my working knowledge (not a beginner and not an expert) is precisely the reason why I started this notebook. It is difficult to advance on this intermediate level as a self-determined learner. \n\nReading newly published books on data science is not as effective as I would wish. Much of the material I already know, so I have to look for the hidden gems of knowledge that are new for me. But more importantly, some of the books do not incorporate the modern trend I am interested in: Using the `tidyverse` and `tidymodels` approach for data wrangling, data analysis, and data modeling.\n\nFurthermore is my statistical knowledge poor. The historical reason is my distrust of the NHST (Null Hypothesis Significance Test) at a time I didn't even know of this notion and other related issues like p-hacking. Therefore I am also interested to learn more about the differences between frequentist and Bayesian approaches. \n\nUp to now, I have written personal notes and ran code examples by accompanying specific books. But in the last weeks, it turned out that this approach is not very practical. I am exposed to new material from many different sources (books, blogs, vignettes of packages, etc.), where I want to jot down my reflections and experiment with the code snippets. Therefore I thought that a general (meta) notebook would help me advance more effectively.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpetzi53%2Fldsr","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpetzi53%2Fldsr","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpetzi53%2Fldsr/lists"}