[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-ujjwalkarn--DataScienceR":3,"tool-ujjwalkarn--DataScienceR":64},[4,17,27,35,43,56],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":16},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,3,"2026-04-05T11:01:52",[13,14,15],"开发框架","图像","Agent","ready",{"id":18,"name":19,"github_repo":20,"description_zh":21,"stars":22,"difficulty_score":23,"last_commit_at":24,"category_tags":25,"status":16},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",138956,2,"2026-04-05T11:33:21",[13,15,26],"语言模型",{"id":28,"name":29,"github_repo":30,"description_zh":31,"stars":32,"difficulty_score":23,"last_commit_at":33,"category_tags":34,"status":16},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",107662,"2026-04-03T11:11:01",[13,14,15],{"id":36,"name":37,"github_repo":38,"description_zh":39,"stars":40,"difficulty_score":23,"last_commit_at":41,"category_tags":42,"status":16},3704,"NextChat","ChatGPTNextWeb\u002FNextChat","NextChat 是一款轻量且极速的 AI 助手，旨在为用户提供流畅、跨平台的大模型交互体验。它完美解决了用户在多设备间切换时难以保持对话连续性，以及面对众多 AI 模型不知如何统一管理的痛点。无论是日常办公、学习辅助还是创意激发，NextChat 都能让用户随时随地通过网页、iOS、Android、Windows、MacOS 或 Linux 端无缝接入智能服务。\n\n这款工具非常适合普通用户、学生、职场人士以及需要私有化部署的企业团队使用。对于开发者而言，它也提供了便捷的自托管方案，支持一键部署到 Vercel 或 Zeabur 等平台。\n\nNextChat 的核心亮点在于其广泛的模型兼容性，原生支持 Claude、DeepSeek、GPT-4 及 Gemini Pro 等主流大模型，让用户在一个界面即可自由切换不同 AI 能力。此外，它还率先支持 MCP（Model Context Protocol）协议，增强了上下文处理能力。针对企业用户，NextChat 提供专业版解决方案，具备品牌定制、细粒度权限控制、内部知识库整合及安全审计等功能，满足公司对数据隐私和个性化管理的高标准要求。",87618,"2026-04-05T07:20:52",[13,26],{"id":44,"name":45,"github_repo":46,"description_zh":47,"stars":48,"difficulty_score":23,"last_commit_at":49,"category_tags":50,"status":16},2268,"ML-For-Beginners","microsoft\u002FML-For-Beginners","ML-For-Beginners 是由微软推出的一套系统化机器学习入门课程，旨在帮助零基础用户轻松掌握经典机器学习知识。这套课程将学习路径规划为 12 周，包含 26 节精炼课程和 52 道配套测验，内容涵盖从基础概念到实际应用的完整流程，有效解决了初学者面对庞大知识体系时无从下手、缺乏结构化指导的痛点。\n\n无论是希望转型的开发者、需要补充算法背景的研究人员，还是对人工智能充满好奇的普通爱好者，都能从中受益。课程不仅提供了清晰的理论讲解，还强调动手实践，让用户在循序渐进中建立扎实的技能基础。其独特的亮点在于强大的多语言支持，通过自动化机制提供了包括简体中文在内的 50 多种语言版本，极大地降低了全球不同背景用户的学习门槛。此外，项目采用开源协作模式，社区活跃且内容持续更新，确保学习者能获取前沿且准确的技术资讯。如果你正寻找一条清晰、友好且专业的机器学习入门之路，ML-For-Beginners 将是理想的起点。",84991,"2026-04-05T10:45:23",[14,51,52,53,15,54,26,13,55],"数据工具","视频","插件","其他","音频",{"id":57,"name":58,"github_repo":59,"description_zh":60,"stars":61,"difficulty_score":10,"last_commit_at":62,"category_tags":63,"status":16},3128,"ragflow","infiniflow\u002Fragflow","RAGFlow 是一款领先的开源检索增强生成（RAG）引擎，旨在为大语言模型构建更精准、可靠的上下文层。它巧妙地将前沿的 RAG 技术与智能体（Agent）能力相结合，不仅支持从各类文档中高效提取知识，还能让模型基于这些知识进行逻辑推理和任务执行。\n\n在大模型应用中，幻觉问题和知识滞后是常见痛点。RAGFlow 通过深度解析复杂文档结构（如表格、图表及混合排版），显著提升了信息检索的准确度，从而有效减少模型“胡编乱造”的现象，确保回答既有据可依又具备时效性。其内置的智能体机制更进一步，使系统不仅能回答问题，还能自主规划步骤解决复杂问题。\n\n这款工具特别适合开发者、企业技术团队以及 AI 研究人员使用。无论是希望快速搭建私有知识库问答系统，还是致力于探索大模型在垂直领域落地的创新者，都能从中受益。RAGFlow 提供了可视化的工作流编排界面和灵活的 API 接口，既降低了非算法背景用户的上手门槛，也满足了专业开发者对系统深度定制的需求。作为基于 Apache 2.0 协议开源的项目，它正成为连接通用大模型与行业专有知识之间的重要桥梁。",77062,"2026-04-04T04:44:48",[15,14,13,26,54],{"id":65,"github_repo":66,"name":67,"description_en":68,"description_zh":69,"ai_summary_zh":69,"readme_en":70,"readme_zh":71,"quickstart_zh":72,"use_case_zh":73,"hero_image_url":74,"owner_login":75,"owner_name":76,"owner_avatar_url":77,"owner_bio":78,"owner_company":79,"owner_location":80,"owner_email":78,"owner_twitter":78,"owner_website":81,"owner_url":82,"languages":83,"stars":92,"forks":93,"last_commit_at":94,"license":95,"difficulty_score":96,"env_os":97,"env_gpu":97,"env_ram":97,"env_deps":98,"category_tags":110,"github_topics":111,"view_count":23,"oss_zip_url":78,"oss_zip_packed_at":78,"status":16,"created_at":116,"updated_at":117,"faqs":118,"releases":119},3388,"ujjwalkarn\u002FDataScienceR","DataScienceR","a curated list of R tutorials for Data Science, NLP and Machine Learning ","DataScienceR 是一个精心整理的 R 语言学习资源库，专注于数据科学、自然语言处理（NLP）和机器学习领域。它并非单一软件，而是一份汇聚了优质教程、核心代码包及实战指南的“地图”，旨在帮助使用者系统性地掌握 R 语言在数据分析中的实际应用。\n\n面对 R 语言生态中资源分散、初学者难以筛选高质量内容的痛点，DataScienceR 提供了结构化的学习路径。从基础的在线课程、免费电子书（如 Hadley Wickham 的经典著作），到进阶的统计分析与可视化技巧，甚至涵盖了数据清洗、分类算法实现等具体任务的操作手册。此外，它还收录了速查表、博客聚合及与其他工具（如 Tableau）协作的实用案例，极大地降低了学习门槛。\n\n这份资源库特别适合数据分析师、科研人员、统计学学生以及希望转型数据科学的开发者使用。无论您是刚接触编程的新手，还是寻求特定算法实现的专业人士，都能在这里找到对应的指引。其独特亮点在于“精选”与“全面”并重，不仅罗列链接，更按主题分类整理了从入门到精通的全套资料，让用户能高效地按需索取，避免在海量信息中迷失方向，是探索 R 语言数据科学世界的理想起点。","# R Data Science Tutorials\n- This repo contains a curated list of R tutorials and packages for Data Science, NLP and Machine Learning. This also serves as a reference guide for several common data analysis tasks.\n\n- [Curated list of Python tutorials for Data Science, NLP and Machine Learning](https:\u002F\u002Fgithub.com\u002Fujjwalkarn\u002FDataSciencePython).\n\n- [Comprehensive topic-wise list of Machine Learning and Deep Learning tutorials, codes, articles and other resources](https:\u002F\u002Fgithub.com\u002Fujjwalkarn\u002FMachine-Learning-Tutorials\u002Fblob\u002Fmaster\u002FREADME.md).\n\n## Learning R\n- Online Courses\n    - [tryR on Codeschool](http:\u002F\u002Ftryr.codeschool.com\u002F)\n    - [Introduction to R for Data Science - Microsoft | edX](https:\u002F\u002Fwww.edx.org\u002Fcourse\u002Fintroduction-r-data-science-microsoft-dat204x?gclid=CLiyoPb448wCFRJxvAod-RoLsA)\n    - [Introduction to R on DataCamp](https:\u002F\u002Fwww.datacamp.com\u002Fcourses\u002Ffree-introduction-to-r)\n    - [Data Analysis with R](https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fdata-analysis-with-r--ud651)\n- [**Free resources for learning R**](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F138\u002Ffree-resources-for-learning-r)\n- [R for Data Science - Hadley Wickham](http:\u002F\u002Fr4ds.had.co.nz\u002F)\n- [Advanced R - Hadley Wickham](http:\u002F\u002Fadv-r.had.co.nz\u002F)\n- [swirl: Learn R, in R](http:\u002F\u002Fswirlstats.com\u002F)\n- [Data Analysis and Visualization Using R](http:\u002F\u002Fvarianceexplained.org\u002FRData\u002F)\n- [**MANY R PROGRAMMING TUTORIALS**](http:\u002F\u002Fwww.listendata.com\u002Fp\u002Fr-programming-tutorials.html)\n- [**A Handbook of Statistical Analyses Using R**](https:\u002F\u002Fcran.r-project.org\u002Fweb\u002Fpackages\u002FHSAUR\u002Fvignettes\u002FCh_introduction_to_R.pdf), Find Other Chapters\n- [**Cookbook for R**](http:\u002F\u002Fwww.cookbook-r.com\u002F)\n- [Learning R in 7 simple steps](http:\u002F\u002Fwww.datasciencecentral.com\u002Fprofiles\u002Fblogs\u002Flearning-r-in-seven-simple-steps)\n\n## More Resources\n- [Awesome-R Repository on GitHub](https:\u002F\u002Fgithub.com\u002Fqinwf\u002Fawesome-R)\n- [R Reference Card: Cheatsheet](https:\u002F\u002Fcran.r-project.org\u002Fdoc\u002Fcontrib\u002FShort-refcard.pdf)\n- [R bloggers: blog aggregator](http:\u002F\u002Fwww.r-bloggers.com\u002F)\n- [R Resources on  GitHub](https:\u002F\u002Fgithub.com\u002Fbinga\u002FDataScienceArsenal\u002Fblob\u002Fmaster\u002Fr-resources.md)\n- [Awesome R resources](https:\u002F\u002Fgithub.com\u002Fujjwalkarn\u002Fawesome-R)\n- [Data Mining with R](https:\u002F\u002Fgithub.com\u002Fujjwalkarn\u002FData-Mining-With-R)\n- [Rob J Hyndman's R Blog](http:\u002F\u002Frobjhyndman.com\u002Fhyndsight\u002Fr\u002F)\n- [Simple R Tricks and Tools](http:\u002F\u002Frobjhyndman.com\u002Fhyndsight\u002Fsimpler\u002F) [(Video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Toc__W7L2Qo)\n- [RStudio GitHub Repo](https:\u002F\u002Fgithub.com\u002Frstudio\u002F)\n- [Tidying Messy Data in R](http:\u002F\u002Fwww.dataschool.io\u002Ftidying-messy-data-in-r\u002F) [Video](https:\u002F\u002Fvimeo.com\u002F33727555)\n- [Baseball Research with R](http:\u002F\u002Fwww.hardballtimes.com\u002Fa-short-ish-introduction-to-using-r-for-baseball-research)\n- [600 websites about R](http:\u002F\u002Fwww.datasciencecentral.com\u002Fprofiles\u002Fblogs\u002F600-websites-about-r)\n- [Implementation of 17 classification algorithms in R](http:\u002F\u002Fwww.datasciencecentral.com\u002Fprofiles\u002Fblogs\u002Fimplemetation-of-17-classification-algorithms-in-r)\n- [Cohort Analysis and LifeCycle Grids mixed segmentation with R](http:\u002F\u002Fanalyzecore.com\u002F2015\u002F04\u002F01\u002Fcohort-analysis-and-lifecycle-grids-mixed-segmentation-with-r\u002F)\n- [Using R and Tableau](http:\u002F\u002Fwww.tableau.com\u002Flearn\u002Fwhitepapers\u002Fusing-r-and-tableau)\n- [COMPREHENSIVE VIEW ON CRAN PACKAGES](http:\u002F\u002Fwww.docfoc.com\u002Fcran-pdf)\n- [Using R for Statistical Tables and Plotting Distributions](http:\u002F\u002Fmath.arizona.edu\u002F~jwatkins\u002FR-01.pdf)\n- [Extended Model Formulas in R: Multiple Parts and Multiple Responses](https:\u002F\u002Fcran.r-project.org\u002Fweb\u002Fpackages\u002FFormula\u002Fvignettes\u002FFormula.pdf)\n- [R vs Python: head to head data analysis](https:\u002F\u002Fwww.dataquest.io\u002Fblog\u002Fpython-vs-r\u002F?utm_content=buffer55639&utm_medium=social&utm_source=linkedin.com&utm_campaign=buffer)\n- [**R for Data Science: Hadley Wickham's Book**](http:\u002F\u002Fr4ds.had.co.nz\u002F)\n- [**R Study Group at UPenn**](https:\u002F\u002Fwww.ling.upenn.edu\u002F~joseff\u002Frstudy\u002Findex.html)\n- [Program-Defined Functions in R](http:\u002F\u002Fdni-institute.in\u002Fblogs\u002Fextracting-data-from-facebook-using-r\u002F)\n\n## Important Questions\n- [**In R, why is bracket better than `subset`?**](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F9860090\u002Fin-r-why-is-better-than-subset)\n- [**Subsetting Data in R**](http:\u002F\u002Fwww.statmethods.net\u002Fmanagement\u002Fsubset.html)\n- [**Vectorization in R: Why?**](http:\u002F\u002Fwww.noamross.net\u002Fblog\u002F2014\u002F4\u002F16\u002Fvectorization-in-r--why.html)\n- [**Quickly reading very large tables as dataframes in R**](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F1727772\u002Fquickly-reading-very-large-tables-as-dataframes-in-r)\n- [**Using R to show data**](http:\u002F\u002Fwww.sr.bham.ac.uk\u002F~ajrs\u002FR\u002Fr-show_data.html)\n- [How can I view the source code for a function?](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F19226816\u002Fhow-can-i-view-the-source-code-for-a-function?lq=1)\n- [How to make a great R reproducible example?](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F5963269\u002Fhow-to-make-a-great-r-reproducible-example)\n- [**R Grouping functions: sapply vs. lapply vs. apply. vs. tapply vs. by vs. aggregate**](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F3505701\u002Fr-grouping-functions-sapply-vs-lapply-vs-apply-vs-tapply-vs-by-vs-aggrega)\n- [**Tricks to manage the available memory in an R session**](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F1358003\u002Ftricks-to-manage-the-available-memory-in-an-r-session)\n- [Difference between Assignment operators '=' and '\u003C-' in R](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F1741820\u002Fassignment-operators-in-r-and)\n- [What is the difference between require() and library()?](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F5595512\u002Fwhat-is-the-difference-between-require-and-library)\n- [How can I view the source code for a function?](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F19226816\u002Fhow-can-i-view-the-source-code-for-a-function)\n- [How can I change fonts for graphs in R?](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F27689222\u002Fchanging-fonts-for-graphs-in-r\u002F)\n\n## Common DataFrame Operations\n- [Create an empty data.frame](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F10689055\u002Fcreate-an-empty-data-frame)\n- [Sort a dataframe by column(s)](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F1296646\u002Fhow-to-sort-a-dataframe-by-columns)\n- [Merge\u002FJoin data frames (inner, outer, left, right)](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F1299871\u002Fhow-to-join-merge-data-frames-inner-outer-left-right)\n- [Drop data frame columns by name](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F4605206\u002Fdrop-data-frame-columns-by-name)\n- [Remove rows with NAs in data.frame](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F4862178\u002Fremove-rows-with-nas-in-data-frame)\n- [Quickly reading very large tables as dataframes in R](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F1727772\u002Fquickly-reading-very-large-tables-as-dataframes-in-r)\n- [Drop factor levels in a subsetted data frame](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F1195826\u002Fdrop-factor-levels-in-a-subsetted-data-frame)\n- [Convert R list to data frame](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F4227223\u002Fr-list-to-data-frame)\n- [Convert data.frame columns from factors to characters](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F2851015\u002Fconvert-data-frame-columns-from-factors-to-characters)\n- [Extracting specific columns from a data frame](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F10085806\u002Fextracting-specific-columns-from-a-data-frame)\n\n## Caret Package in R\n- [Ensembling Models with caret](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F27361\u002Fstacking-ensembling-models-with-caret)\n- [Model Training and Tuning](http:\u002F\u002Ftopepo.github.io\u002Fcaret\u002Ftraining.html)\n- [Caret Model List](http:\u002F\u002Ftopepo.github.io\u002Fcaret\u002FmodelList.html)\n- [relationship-between-data-splitting-and-traincontrol](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F14968874\u002Fcaret-relationship-between-data-splitting-and-traincontrol)\n- [Specify model generation parameters](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F10498477\u002Fcarettrain-specify-model-generation-parameters?lq=1)\n- [Tutorial](https:\u002F\u002Fwww.r-project.org\u002Fnosvn\u002Fconferences\u002FuseR-2013\u002FTutorials\u002Fkuhn\u002Fuser_caret_2up.pdf), [Paper](www.jstatsoft.org\u002Farticle\u002Fview\u002Fv028i05\u002Fv28i05.pdf)\n- [Ensembling models with R](http:\u002F\u002Famunategui.github.io\u002Fblending-models\u002F), [Ensembling Regression Models in R](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F26790\u002Fensembling-regression-models)\n\n## R Cheatsheets\n- [R Reference Card](https:\u002F\u002Fcran.r-project.org\u002Fdoc\u002Fcontrib\u002FShort-refcard.pdf)\n- [R Reference Card 2.0](https:\u002F\u002Fcran.r-project.org\u002Fdoc\u002Fcontrib\u002FBaggott-refcard-v2.pdf)\n- [Data Wrangling in R](https:\u002F\u002Fwww.rstudio.com\u002Fwp-content\u002Fuploads\u002F2015\u002F02\u002Fdata-wrangling-cheatsheet.pdf)\n- [ggplot2 Cheatsheet](https:\u002F\u002Fwww.rstudio.com\u002Fwp-content\u002Fuploads\u002F2015\u002F08\u002Fggplot2-cheatsheet.pdf)\n- [Shiny Cheatsheet](http:\u002F\u002Fshiny.rstudio.com\u002Fimages\u002Fshiny-cheatsheet.pdf)\n- [devtools Cheatsheet](https:\u002F\u002Fwww.rstudio.com\u002Fwp-content\u002Fuploads\u002F2015\u002F06\u002Fdevtools-cheatsheet.pdf)\n- [markdown Cheatsheet](https:\u002F\u002Fwww.rstudio.com\u002Fwp-content\u002Fuploads\u002F2015\u002F02\u002Frmarkdown-cheatsheet.pdf), [reference](https:\u002F\u002Fwww.rstudio.com\u002Fwp-content\u002Fuploads\u002F2015\u002F03\u002Frmarkdown-reference.pdf)\n- [Data Exploration Cheatsheet](http:\u002F\u002Fwww.analyticsvidhya.com\u002Fblog\u002F2015\u002F10\u002Fcheatsheet-11-steps-data-exploration-with-codes\u002F)\n\n## Reference Slides\n- [R Reference Card](https:\u002F\u002Fcran.r-project.org\u002Fdoc\u002Fcontrib\u002FBaggott-refcard-v2.pdf)\n- [Association Rule Mining](https:\u002F\u002F78462f86-a-e2d7344e-s-sites.googlegroups.com\u002Fa\u002Frdatamining.com\u002Fwww\u002Fdocs\u002FRDataMining-slides-association-rules.pdf?attachauth=ANoY7crD9hRI7333KWhK0TVPsS1VfgWoW4BuIsmL8B0NANfntEOq6QbcwJk-aCRUy2N6CmUeJsyrlOOd5bo1CqRUYXkEbSl1JbTniVbb-GSR3cyTt9Qq6xB3ZasMEdACaS9j1fZDiLVn_zLFbrF--aJM7gAu54JwRBhvKuQPOPyeMTosWcTmmrJdRNWH4ZqD5kYEJlmHDcXB8Bp-DWbUxZG2T8sAGbcHGUqkPTTJ_u03wvKyw5MGMrGU7q4xIyyUmBas_PqEDi6q&attredirects=0)\n- [Time Series Analysis](https:\u002F\u002F78462f86-a-e2d7344e-s-sites.googlegroups.com\u002Fa\u002Frdatamining.com\u002Fwww\u002Fdocs\u002FRDataMining-slides-time-series-analysis.pdf?attachauth=ANoY7cphtFEj6IMGuupE5ygQn5flMH5-QPE4yNgJ9fYv3WqfY0qU8LWGgiECZKs6P63Rhx5Nml8lQXQnX7QH7OZm1hoi_Kl0m9sLOAC0tc4sQipWC8DprQVoYSDyw0EdeJfZWAQor0AyjMWeFHPY6nqxIGAaj4arrwZcnR1dYC7nQK4dTVQM80ARrN5Yzq9rNbGic30X-xKwNQxOXL4fO54ThpzmNB4wLKv5geo_hDqPkwtKBmNR7u_kGPOymJHGvxP3nr02aJsB&attredirects=0)\n- [Data Exploration and Visualisation](https:\u002F\u002F78462f86-a-e2d7344e-s-sites.googlegroups.com\u002Fa\u002Frdatamining.com\u002Fwww\u002Fdocs\u002FRDataMining-slides-data-exploration-visualization.pdf?attachauth=ANoY7cpqnCTmCv1omsIoKmefAn8q6M_j4Hizv_1enJlu3nRPIxIhzjBlf-9B_sIxMxpUx-XN5cAw74GUr18Dn0EcaiIm9MVeCtqT-2dcPNo0dfhRJvnb5J8EHKBX_w7Y6mYgb7UAoIUbjdmVGR9VCIfJf6PGQqAlupywcb1yGbT4pv61bQzOzrU4-eICfgHmORdi8YgBqscyT2ThaKHPSeGXD0dd3g08pGN3bY70MKM02ZaqarewbII91KTNH1-zmELEcvatl_sMxmGgNnIDm6MaxEWQ1pIrTQ%3D%3D&attredirects=0)\n- [Regression and Classification](https:\u002F\u002F78462f86-a-e2d7344e-s-sites.googlegroups.com\u002Fa\u002Frdatamining.com\u002Fwww\u002Fdocs\u002FRDataMining-slides-regression-classification.pdf?attachauth=ANoY7cq0yqcj_65pafTfUqHazTYvp4E4r-5OB1kLv3swVKJhVydaJ0YU5yEPiOciQC0k_P1QzO6z1vD0r9E05KU8y7Mn6NTesQOOq_mmwlMqAe7D2mnqkHZBqFT6tk2hJ3g3fK40mvfyU5ggoGMxMYn9nVhihKwcIYJy9A8zlbFo4r9a35kpTDr6jJjAw5eQwSEMe-bvT5iyZuyMS7QS-tvlgHjJ40ZGhPro7GcWXfb7qqaPeTe9NyeU7MxAy2Z_lAzxn0vSnqe6&attredirects=0)\n- [Text Mining on Twitter Data](https:\u002F\u002F78462f86-a-e2d7344e-s-sites.googlegroups.com\u002Fa\u002Frdatamining.com\u002Fwww\u002Fdocs\u002FRDataMining-slides-text-mining.pdf?attachauth=ANoY7cquEwmhHFNHxiKNhv6C2wquNdaib8A_BeTRFaGFXZ2deivENdTK-GS7mSZjermC7b_-L6KtCWhfF1ZOzOF9XaLkIaw6InCEnjdO1fWUhJFujaGwwbcbExJKEVuMmwlBX_SDUFZYgjuTbIb2llgKRMQc3Dd241HNZHTvGVuPG26vHKN_jU_WoEj7uIilRJWFTDvNrZWGWrvImWr0aCNou56qAB-zmBG_cvRS4QOQroiEetLpR7k%3D&attredirects=0)\n\n## Using R for Multivariate Analysis\n- [Little Book of R for Multivariate Analysis!](http:\u002F\u002Flittle-book-of-r-for-multivariate-analysis.readthedocs.io\u002Fen\u002Flatest\u002F)\n- [THE FREQPARCOORD PACKAGE FOR MULTIVARIATE VISUALIZATION](https:\u002F\u002Fmatloff.wordpress.com\u002F2014\u002F03\u002F30\u002Fthe-freqparcoord-package-for-multivariate-visualization\u002F)\n- [Use of freqparcoord for Regression Diagnostics](http:\u002F\u002Fwww.r-bloggers.com\u002Fuse-of-freqparcoord-for-regression-diagnostics\u002F)\n\n## Time Series Analysis\n- [**Time Series Forecasting (Online Book)**](https:\u002F\u002Fwww.otexts.org\u002Ffpp)\n- [**A Little Book of Time Series Analysis in R**](http:\u002F\u002Fa-little-book-of-r-for-time-series.readthedocs.org\u002Fen\u002Flatest\u002Fsrc\u002Ftimeseries.html)\n- [Quick R: Time Series and Forecasting](http:\u002F\u002Fwww.statmethods.net\u002Fadvstats\u002Ftimeseries.html)\n- [Components of Time Series Data](https:\u002F\u002Fwww.linkedin.com\u002Fpulse\u002Fcomponent-time-series-data-jeffrey-strickland-ph-d-cmsp)\n- [Unobserved Component Models using R](https:\u002F\u002Fwww.linkedin.com\u002Fpulse\u002Funobserved-component-models-r-jeffrey-strickland-ph-d-cmsp)\n- [The Holt-Winters Forecasting Method](http:\u002F\u002Fwebarchive.nationalarchives.gov.uk\u002F20080726235635\u002Fhttp:\u002F\u002Fstatistics.gov.uk\u002Fiosmethodology\u002Fdownloads\u002FAnnex_B_The_Holt-Winters_forecasting_method.pdf)\n- [**CRAN Task View: Time Series Analysis**](https:\u002F\u002Fcran.r-project.org\u002Fweb\u002Fviews\u002FTimeSeries.html)\n\n## Bayesian Inference\n- [Packages for Bayesian Inference](https:\u002F\u002Fgithub.com\u002Fujjwalkarn\u002Fawesome-R#bayesian)\n- [Bayesian Inference in R: Video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=fiWIK7ONX3U)\n- [R and Bayesian Statistics](http:\u002F\u002Fwww.r-bloggers.com\u002Fr-and-bayesian-statistics\u002F)\n\n## Machine Learning using R\n- [Machine Learning with R](https:\u002F\u002Fgithub.com\u002Fjhashanti\u002FMachine-Learning-with-R)\n- [Using R for Multivariate Analysis (Online Book)](http:\u002F\u002Flittle-book-of-r-for-multivariate-analysis.readthedocs.org\u002Fen\u002Flatest\u002Fsrc\u002Fmultivariateanalysis.html)\n- [CRAN Task View: Machine Learning & Statistical Learning](https:\u002F\u002Fcran.r-project.org\u002Fweb\u002Fviews\u002FMachineLearning.html)\n- [Machine Learning Using R (Online Book)](https:\u002F\u002Fwww.otexts.org\u002Fsfml)\n- [Linear Regression and Regularization Code](http:\u002F\u002Frpubs.com\u002Fjustmarkham\u002Flinear-regression-salary)\n- [Cheatsheet](http:\u002F\u002Fwww.analyticsvidhya.com\u002Fblog\u002F2015\u002F09\u002Ffull-cheatsheet-machine-learning-algorithms\u002F)\n- [**Multinomial and Ordinal Logistic Regression in R**](http:\u002F\u002Fwww.analyticsvidhya.com\u002Fblog\u002F2016\u002F02\u002Fmultinomial-ordinal-logistic-regression\u002F)\n- [**Evaluating Logistic Regression Models in R**](https:\u002F\u002Fwww.r-bloggers.com\u002Fevaluating-logistic-regression-models\u002F)\n \n## Neural Networks in R\n- [Visualizing Neural Nets in R](https:\u002F\u002Fbeckmw.wordpress.com\u002F2013\u002F11\u002F14\u002Fvisualizing-neural-networks-in-r-update\u002F)\n- [nnet package](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F21788817\u002Fr-nnet-with-a-simple-example-of-2-classes-with-2-variables)\n- [Fitting a neural network in R; neuralnet package](http:\u002F\u002Fwww.r-bloggers.com\u002Ffitting-a-neural-network-in-r-neuralnet-package\u002F)\n- [Neural Networks with R – A Simple Example](http:\u002F\u002Fgekkoquant.com\u002F2012\u002F05\u002F26\u002Fneural-networks-with-r-simple-example\u002F)\n- [NeuralNetTools 1.0.0 now on CRAN](https:\u002F\u002Fbeckmw.wordpress.com\u002Ftag\u002Fneural-network\u002F)\n- [Introduction to Neural Networks in R](http:\u002F\u002Fwww.louisaslett.com\u002FCourses\u002FData_Mining\u002FST4003-Lab5-Introduction_to_Neural_Networks.pdf)\n- [Step by Step Neural Networks using R](https:\u002F\u002Fbicorner.com\u002F2015\u002F05\u002F13\u002Fneural-networks-using-r\u002F)\n- [**R for Deep Learning**](http:\u002F\u002Fwww.parallelr.com\u002Fr-deep-neural-network-from-scratch\u002F)\n- [Neural Networks using package neuralnet](http:\u002F\u002Fwww.di.fc.ul.pt\u002F~jpn\u002Fr\u002Fneuralnets\u002Fneuralnets.html), [Paper](https:\u002F\u002Fjournal.r-project.org\u002Farchive\u002F2010-1\u002FRJournal_2010-1_Guenther+Fritsch.pdf)\n\n## Sentiment Analysis\n- [Different Approaches](https:\u002F\u002Fdrive.google.com\u002Fopen?id=0By_wg-rXnp_6U1JLNVA3cnAxZ3M)\n- [**Sentiment analysis with machine learning in R**](http:\u002F\u002Fdatascienceplus.com\u002Fsentiment-analysis-with-machine-learning-in-r\u002F)\n- [**First shot: Sentiment Analysis in R**](http:\u002F\u002Fandybromberg.com\u002Fsentiment-analysis\u002F)\n- [qdap package](https:\u002F\u002Fgithub.com\u002Ftrinker\u002Fqdap), [code](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F22774913\u002Festimating-document-polarity-using-rs-qdap-package-without-sentsplit)\n- [sentimentr package](https:\u002F\u002Fgithub.com\u002Ftrinker\u002Fsentimentr)\n- [tm.plugin.sentiment package](https:\u002F\u002Fgithub.com\u002Fmannau\u002Ftm.plugin.sentiment)\n- [Packages other than sentiment](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F15194436\u002Fis-there-any-other-package-other-than-sentiment-to-do-sentiment-analysis-in-r)\n- [Sentiment Analysis and Opinion Mining](https:\u002F\u002Fwww.cs.uic.edu\u002F~liub\u002FFBS\u002Fsentiment-analysis.html)\n- [tm_term_score](http:\u002F\u002Fwww.inside-r.org\u002Fpackages\u002Fcran\u002Ftm\u002Fdocs\u002Ftm_term_score)\n- [**vaderSentiment Paper**](http:\u002F\u002Fcomp.social.gatech.edu\u002Fpapers\u002Ficwsm14.vader.hutto.pdf), [**vaderSentiment code**](https:\u002F\u002Fgithub.com\u002Fcjhutto\u002FvaderSentiment)\n\n## Imputation in R\n- [**Imputation in R**](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F13114812\u002Fimputation-in-r)\n- [Imputation with Random Forests](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F49270\u002Fimputation-with-random-forests)\n- [How to Identify and Impute Multiple Missing Values using R](http:\u002F\u002Fwww.unt.edu\u002Frss\u002Fclass\u002FJon\u002FBenchmarks\u002FMissingValueImputation_JDS_Nov2010.pdf)\n- MICE\n    - [error in implementation of random forest in mice r package](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F23974026\u002Ferror-in-implementation-of-random-forest-in-mice-r-package)\n    - [mice.impute.rf {mice}](http:\u002F\u002Fwww.inside-r.org\u002Fpackages\u002Fcran\u002Fmice\u002Fdocs\u002Fmice.impute.rf)\n\n## NLP and Text Mining in R\n- [**What algorithm I need to find n-grams?**](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F8161167\u002Fwhat-algorithm-i-need-to-find-n-grams)\n- [NLP R Tutorial](http:\u002F\u002Fwww.r-bloggers.com\u002Fnatural-language-processing-tutorial\u002F)\n- [Introduction to the tm Package Text Mining in R](https:\u002F\u002Fcran.r-project.org\u002Fweb\u002Fpackages\u002Ftm\u002Fvignettes\u002Ftm.pdf)\n- [Adding stopwords in R tm](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F18446408\u002Fadding-stopwords-in-r-tm)\n- [Text Mining](http:\u002F\u002Fwww.r-bloggers.com\u002Ftext-mining\u002F)\n- [Word Stemming in R](http:\u002F\u002Fwww.omegahat.net\u002FRstem\u002Fstemming.pdf)\n- [**Classification of Documents using Text Mining Package “tm”**](http:\u002F\u002Fweb.letras.up.pt\u002Fbhsmaia\u002FEDV\u002Fapresentacoes\u002FBradzil_Classif_withTM.pdf)\n- [Text mining tools techniques and applications](http:\u002F\u002Fslidegur.com\u002Fdoc\u002F1830649\u002Ftext-mining)\n- [Text Mining: Overview,Applications and Issues ](http:\u002F\u002Fwww3.cs.stonybrook.edu\u002F~cse634\u002FG8present.pdf)\n- [**Text Mining pdf**](http:\u002F\u002Fwww3.cs.stonybrook.edu\u002F~cse634\u002Fpresentations\u002FTextMining.pdf)\n- [Text Mining Another pdf](http:\u002F\u002Fwww.stat.columbia.edu\u002F~madigan\u002FW2025\u002Fnotes\u002FIntroTextMining.pdf)\n- [Good PPT](http:\u002F\u002Fstudylib.net\u002Fdoc\u002F5800473\u002Ftopic7-textmining)\n- [**Scraping Twitter and Web Data Using R**](http:\u002F\u002Fwww.nyu.edu\u002Fprojects\u002Fpoliticsdatalab\u002Flocaldata\u002Fworkshops\u002Ftwitter.pdf)\n\n## Visualisation in R\n- [ggplot2 tutorial](http:\u002F\u002Fwww.ling.upenn.edu\u002F~joseff\u002Favml2012\u002F)\n- [SHINY EXAMPLES](https:\u002F\u002Fgithub.com\u002Frstudio\u002Fshiny-examples)\n- [**Top 50 ggplot2 Visualizations**](http:\u002F\u002Fr-statistics.co\u002FTop50-Ggplot2-Visualizations-MasterList-R-Code.html)\n- [Comprehensive Guide to Data Visualization in  R](http:\u002F\u002Fwww.analyticsvidhya.com\u002Fblog\u002F2015\u002F07\u002Fguide-data-visualization-r\u002F)\n- [Interactive visualizations with R – a minireview](http:\u002F\u002Fwww.r-bloggers.com\u002Finteractive-visualizations-with-r-a-minireview\u002F)\n- [Beginner's guide to R: Painless data visualization](http:\u002F\u002Fwww.computerworld.com\u002Farticle\u002F2497304\u002Fbusiness-intelligence-beginner-s-guide-to-r-painless-data-visualization.html)\n- [Data Visualization in R with ggvis](https:\u002F\u002Fwww.datacamp.com\u002Fcourses\u002Fggvis-data-visualization-r-tutorial)\n- [Multiple Visualization Articles in R](http:\u002F\u002Fwww.r-statistics.com\u002Ftag\u002Fvisualization\u002F)\n\n## Statistics with R\n- [Using R for Biomedical Statistics (Online Book)](http:\u002F\u002Fa-little-book-of-r-for-biomedical-statistics.readthedocs.org\u002Fen\u002Flatest\u002Fsrc\u002Fbiomedicalstats.html)\n- [Elementary Statistics with R](http:\u002F\u002Fwww.r-tutor.com\u002Felementary-statistics)\n- [A Hands-on Introduction to Statistics with R](https:\u002F\u002Fwww.datacamp.com\u002Fintroduction-to-statistics)\n- [Quick R: Basic Statistics](http:\u002F\u002Fwww.statmethods.net\u002FstATS\u002Findex.html)\n- [Quick R: Descriptive Statistics](http:\u002F\u002Fwww.statmethods.net\u002Fstats\u002Fdescriptives.html)\n- [Explore Statistics with R | edX](https:\u002F\u002Fwww.edx.org\u002Fcourse\u002Fexplore-statistics-r-kix-kiexplorx-0)\n\n## Useful R Packages\n- [**TIDY DATA HADLEY PAPER**](https:\u002F\u002Fwww.jstatsoft.org\u002Farticle\u002Fview\u002Fv059i10)\n    - Package ‘tidyr’: tidyr is an evolution of reshape2. It's design specifically for data tidying (not general reshaping or aggregating) and works well with dplyr data pipelines.   \n- [BROOM](https:\u002F\u002Fgithub.com\u002Fdgrtwo\u002Fbroom)\n- [**plyr, stringr, reshape2 tutorial**](http:\u002F\u002Fwww.dataschool.io\u002Ftidying-messy-data-in-r\u002F) [Video](https:\u002F\u002Fvimeo.com\u002F33727555), [CODE](https:\u002F\u002Fgithub.com\u002Fjustmarkham\u002Ftidy-data)\n- dplyr\n    - [Code Files in this Repo](https:\u002F\u002Fgithub.com\u002Fujjwalkarn\u002FDataScienceR\u002Ftree\u002Fmaster\u002FIntro%20to%20dplyr)\n    - [dplyr tutorial 1](http:\u002F\u002Fwww.dataschool.io\u002Fdplyr-tutorial-for-faster-data-manipulation-in-r\u002F), [dplyr tutorial 2](http:\u002F\u002Fwww.dataschool.io\u002Fdplyr-tutorial-part-2\u002F)\n    - [Do your \"data janitor work\" like a boss with dplyr](http:\u002F\u002Fwww.gettinggeneticsdone.com\u002F2014\u002F08\u002Fdo-your-data-janitor-work-like-boss.html)\n- ggplot2\n    - [ggplot2 tutorial](http:\u002F\u002Fwww.ling.upenn.edu\u002F~joseff\u002Favml2012\u002F)\n    - [Good Tutorial!](https:\u002F\u002Fgithub.com\u002Fjennybc\u002Fggplot2-tutorial)\n    - [Introduction to ggplot2](https:\u002F\u002Fspeakerdeck.com\u002Fkarthik\u002Fintroduction-to-ggplot2), [GitHub](https:\u002F\u002Fgithub.com\u002Fkarthik\u002Fggplot-lecture)\n    - [A quick introduction to ggplot()](http:\u002F\u002Fwww.noamross.net\u002Fblog\u002F2012\u002F10\u002F5\u002Fggplot-introduction.html)\n    - [R Graphics cookbook](http:\u002F\u002Fwww.cookbook-r.com\u002FGraphs\u002Findex.html)\n    - [ggplot2 Version of Figures in “Lattice: Multivariate Data Visualization with R” ](https:\u002F\u002Flearnr.wordpress.com\u002F2009\u002F06\u002F28\u002Fggplot2-version-of-figures-in-lattice-multivariate-data-visualization-with-r-part-1\u002F)\n- [A speed test comparison of plyr, data.table, and dplyr](http:\u002F\u002Fwww.r-statistics.com\u002F2013\u002F09\u002Fa-speed-test-comparison-of-plyr-data-table-and-dplyr\u002F)\n- data.table\n    - [Introduction to the data.table package in R](https:\u002F\u002Fcran.r-project.org\u002Fweb\u002Fpackages\u002Fdata.table\u002Fvignettes\u002Fdatatable-intro.pdf)   \n    - [Fast summary statistics in R with data.table](http:\u002F\u002Fblog.yhat.com\u002Fposts\u002Ffast-summary-statistics-with-data-dot-table.html)\n- Other Packages\n    - Package 'e1071'\n    - Package ‘AppliedPredictiveModeling’\n    - Package ‘stringr’: stringr is a set of simple wrappers that make R's string functions more consistent, simpler and easier to use.\n    - Package ‘stringdist’: Implements an approximate string matching version of R's native 'match' function. Can calculate various string distances based on edits (damerau-levenshtein, hamming, levenshtein, optimal sting alignment), qgrams or heuristic metrics\n    - Package ‘FSelector’: This package provides functions for selecting attributes from a given dataset \n    - [Ryacas – an R interface to the yacas computer algebra system](https:\u002F\u002Fcran.r-project.org\u002Fweb\u002Fpackages\u002FRyacas\u002Fvignettes\u002FRyacas.pdf)\n    - [Scatterplot3d – an R package for Visualizing Multivariate Data](https:\u002F\u002Fcran.r-project.org\u002Fweb\u002Fpackages\u002Fscatterplot3d\u002Fvignettes\u002Fs3d.pdf)\n    - [tm.plugin.webmining intro](https:\u002F\u002Fcran.r-project.org\u002Fweb\u002Fpackages\u002Ftm.plugin.webmining\u002Fvignettes\u002FShortIntro.pdf)\n    - [Solving Differential Equations in R - ODE examples](https:\u002F\u002Fcran.r-project.org\u002Fweb\u002Fpackages\u002FdiffEq\u002Fvignettes\u002FODEinR.pdf)\n    - [Structural Equation Modeling With the sem Package in R](http:\u002F\u002Fsocserv.socsci.mcmaster.ca\u002Fjfox\u002FMisc\u002Fsem\u002FSEM-paper.pdf)\n    - [prettyScree - prettyGraphs](http:\u002F\u002Fwww.inside-r.org\u002Fpackages\u002Fcran\u002FprettyGraphs\u002Fdocs\u002FprettyScree)\n\n## Market Basket Analysis in R\n- [Market Basket Analysis with R](http:\u002F\u002Fwww.salemmarafi.com\u002Fcode\u002Fmarket-basket-analysis-with-r\u002F)\n- [Step by Step explanation of Market Basket](http:\u002F\u002Fdni-institute.in\u002Fblogs\u002Fmarket-basket-analysis-step-by-step-approach-using-r\u002F)\n","# R 数据科学教程\n- 本仓库包含一份精心整理的 R 教程和包列表，涵盖数据科学、自然语言处理和机器学习领域。它同时也可作为若干常见数据分析任务的参考指南。\n\n- [数据科学、NLP 和机器学习的 Python 教程精选列表](https:\u002F\u002Fgithub.com\u002Fujjwalkarn\u002FDataSciencePython)。\n- [全面的主题式机器学习和深度学习教程、代码、文章及其他资源列表](https:\u002F\u002Fgithub.com\u002Fujjwalkarn\u002FMachine-Learning-Tutorials\u002Fblob\u002Fmaster\u002FREADME.md)。\n\n## 学习 R\n- 在线课程\n    - [Codeschool 上的 tryR](http:\u002F\u002Ftryr.codeschool.com\u002F)\n    - [微软 | edX 的数据科学 R 入门课程](https:\u002F\u002Fwww.edx.org\u002Fcourse\u002Fintroduction-r-data-science-microsoft-dat204x?gclid=CLiyoPb448wCFRJxvAod-RoLsA)\n    - [DataCamp 的 R 入门课程](https:\u002F\u002Fwww.datacamp.com\u002Fcourses\u002Ffree-introduction-to-r)\n    - [使用 R 进行数据分析](https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fdata-analysis-with-r--ud651)\n- [**免费的 R 学习资源**](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F138\u002Ffree-resources-for-learning-r)\n- [Hadley Wickham 的《R 数据科学》](http:\u002F\u002Fr4ds.had.co.nz\u002F)\n- [Hadley Wickham 的《高级 R 编程》](http:\u002F\u002Fadv-r.had.co.nz\u002F)\n- [swirl: 在 R 中学习 R](http:\u002F\u002Fswirlstats.com\u002F)\n- [使用 R 进行数据分析与可视化](http:\u002F\u002Fvarianceexplained.org\u002FRData\u002F)\n- [**大量 R 编程教程**](http:\u002F\u002Fwww.listendata.com\u002Fp\u002Fr-programming-tutorials.html)\n- [**使用 R 的统计分析手册**](https:\u002F\u002Fcran.r-project.org\u002Fweb\u002Fpackages\u002FHSAUR\u002Fvignettes\u002FCh_introduction_to_R.pdf)，查找其他章节\n- [**R 烹饪书**](http:\u002F\u002Fwww.cookbook-r.com\u002F)\n- [7 步轻松学习 R](http:\u002F\u002Fwww.datasciencecentral.com\u002Fprofiles\u002Fblogs\u002Flearning-r-in-seven-simple-steps)\n\n## 更多资源\n- [GitHub 上的 Awesome-R 仓库](https:\u002F\u002Fgithub.com\u002Fqinwf\u002Fawesome-R)\n- [R 参考卡片：速查表](https:\u002F\u002Fcran.r-project.org\u002Fdoc\u002Fcontrib\u002FShort-refcard.pdf)\n- [R 博主：博客聚合器](http:\u002F\u002Fwww.r-bloggers.com\u002F)\n- [GitHub 上的 R 资源](https:\u002F\u002Fgithub.com\u002Fbinga\u002FDataScienceArsenal\u002Fblob\u002Fmaster\u002Fr-resources.md)\n- [Awesome R 资源](https:\u002F\u002Fgithub.com\u002Fujjwalkarn\u002Fawesome-R)\n- [使用 R 进行数据挖掘](https:\u002F\u002Fgithub.com\u002Fujjwalkarn\u002FData-Mining-With-R)\n- [Rob J Hyndman 的 R 博客](http:\u002F\u002Frobjhyndman.com\u002Fhyndsight\u002Fr\u002F)\n- [简单的 R 技巧与工具](http:\u002F\u002Frobjhyndman.com\u002Fhyndsight\u002Fsimpler\u002F) [(视频)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Toc__W7L2Qo)\n- [RStudio GitHub 仓库](https:\u002F\u002Fgithub.com\u002Frstudio\u002F)\n- [在 R 中整理混乱的数据](http:\u002F\u002Fwww.dataschool.io\u002Ftidying-messy-data-in-r\u002F) [视频](https:\u002F\u002Fvimeo.com\u002F33727555)\n- [使用 R 进行棒球研究](http:\u002F\u002Fwww.hardballtimes.com\u002Fa-short-ish-introduction-to-using-r-for-baseball-research)\n- [600 个关于 R 的网站](http:\u002F\u002Fwww.datasciencecentral.com\u002Fprofiles\u002Fblogs\u002F600-websites-about-r)\n- [在 R 中实现 17 种分类算法](http:\u002F\u002Fwww.datasciencecentral.com\u002Fprofiles\u002Fblogs\u002Fimplemetation-of-17-classification-algorithms-in-r)\n- [使用 R 进行队列分析和生命周期网格混合细分](http:\u002F\u002Fanalyzecore.com\u002F2015\u002F04\u002F01\u002Fcohort-analysis-and-lifecycle-grids-mixed-segmentation-with-r\u002F)\n- [使用 R 和 Tableau](http:\u002F\u002Fwww.tableau.com\u002Flearn\u002Fwhitepapers\u002Fusing-r-and-tableau)\n- [CRAN 包的全面视图](http:\u002F\u002Fwww.docfoc.com\u002Fcran-pdf)\n- [使用 R 进行统计表格和分布图绘制](http:\u002F\u002Fmath.arizona.edu\u002F~jwatkins\u002FR-01.pdf)\n- [R 中扩展的模型公式：多部分与多响应](https:\u002F\u002Fcran.r-project.org\u002Fweb\u002Fpackages\u002FFormula\u002Fvignettes\u002FFormula.pdf)\n- [R 与 Python：面对面的数据分析](https:\u002F\u002Fwww.dataquest.io\u002Fblog\u002Fpython-vs-r\u002F?utm_content=buffer55639&utm_medium=social&utm_source=linkedin.com&utm_campaign=buffer)\n- [**Hadley Wickham 的《R 数据科学》**](http:\u002F\u002Fr4ds.had.co.nz\u002F)\n- [**宾夕法尼亚大学的 R 学习小组**](https:\u002F\u002Fwww.ling.upenn.edu\u002F~joseff\u002Frstudy\u002Findex.html)\n- [R 中的程序自定义函数](http:\u002F\u002Fdni-institute.in\u002Fblogs\u002Fextracting-data-from-facebook-using-r\u002F)\n\n## 重要问题\n- [**在 R 中，为什么方括号比 `subset` 更好？**](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F9860090\u002Fin-r-why-is-better-than-subset)\n- [**在 R 中子集化数据**](http:\u002F\u002Fwww.statmethods.net\u002Fmanagement\u002Fsubset.html)\n- [**R 中的向量化：为什么？**](http:\u002F\u002Fwww.noamross.net\u002Fblog\u002F2014\u002F4\u002F16\u002Fvectorization-in-r--why.html)\n- [**在 R 中快速读取超大表格为数据框**](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F1727772\u002Fquickly-reading-very-large-tables-as-dataframes-in-r)\n- [**使用 R 展示数据**](http:\u002F\u002Fwww.sr.bham.ac.uk\u002F~ajrs\u002FR\u002Fr-show_data.html)\n- [如何查看函数的源代码？](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F19226816\u002Fhow-can-i-view-the-source-code-for-a-function?lq=1)\n- [如何制作一个优秀的 R 可复现示例？](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F5963269\u002Fhow-to-make-a-great-r-reproducible-example)\n- [**R 中的分组函数：sapply vs. lapply vs. apply. vs. tapply vs. by vs. aggregate**](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F3505701\u002Fr-grouping-functions-sapply-vs-lapply-vs-apply-vs-tapply-vs-by-vs-aggrega)\n- [**管理 R 会话中可用内存的技巧**](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F1358003\u002Ftricks-to-manage-the-available-memory-in-an-r-session)\n- [R 中赋值运算符 `=` 和 `\u003C-` 的区别](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F1741820\u002Fassignment-operators-in-r-and)\n- [require() 和 library() 有什么区别？](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F5595512\u002Fwhat-is-the-difference-between-require-and-library)\n- [如何查看函数的源代码？](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F19226816\u002Fhow-can-i-view-the-source-code-for-a-function)\n- [如何更改 R 中图表的字体？](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F27689222\u002Fchanging-fonts-for-graphs-in-r\u002F)\n\n## 常用的 DataFrame 操作\n- [创建一个空的 data.frame](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F10689055\u002Fcreate-an-empty-data-frame)\n- [按列对 data.frame 进行排序](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F1296646\u002Fhow-to-sort-a-dataframe-by-columns)\n- [合并\u002F连接 data.frame（内连接、外连接、左连接、右连接）](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F1299871\u002Fhow-to-join-merge-data-frames-inner-outer-left-right)\n- [按名称删除 data.frame 的列](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F4605206\u002Fdrop-data-frame-columns-by-name)\n- [移除 data.frame 中含有 NA 的行](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F4862178\u002Fremove-rows-with-nas-in-data-frame)\n- [在 R 中快速读取超大表格并转换为 data.frame](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F1727772\u002Fquickly-reading-very-large-tables-as-dataframes-in-r)\n- [在子集化的 data.frame 中删除因子水平](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F1195826\u002Fdrop-factor-levels-in-a-subsetted-data-frame)\n- [将 R 列表转换为 data.frame](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F4227223\u002Fr-list-to-data-frame)\n- [将 data.frame 的列从因子类型转换为字符类型](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F2851015\u002Fconvert-data-frame-columns-from-factors-to-characters)\n- [从 data.frame 中提取特定列](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F10085806\u002Fextracting-specific-columns-from-a-data-frame)\n\n## R 语言中的 caret 包\n- [使用 caret 进行模型集成](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F27361\u002Fstacking-ensembling-models-with-caret)\n- [模型训练与调参](http:\u002F\u002Ftopepo.github.io\u002Fcaret\u002Ftraining.html)\n- [caret 支持的模型列表](http:\u002F\u002Ftopepo.github.io\u002Fcaret\u002FmodelList.html)\n- [数据拆分与 trainControl 之间的关系](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F14968874\u002Fcaret-relationship-between-data-splitting-and-traincontrol)\n- [指定模型生成参数](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F10498477\u002Fcarettrain-specify-model-generation-parameters?lq=1)\n- [教程](https:\u002F\u002Fwww.r-project.org\u002Fnosvn\u002Fconferences\u002FuseR-2013\u002FTutorials\u002Fkuhn\u002Fuser_caret_2up.pdf)、[论文](www.jstatsoft.org\u002Farticle\u002Fview\u002Fv028i05\u002Fv28i05.pdf)\n- [使用 R 进行模型集成](http:\u002F\u002Famunategui.github.io\u002Fblending-models\u002F)、[在 R 中集成回归模型](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F26790\u002Fensembling-regression-models)\n\n## R 语言速查表\n- [R 参考卡片](https:\u002F\u002Fcran.r-project.org\u002Fdoc\u002Fcontrib\u002FShort-refcard.pdf)\n- [R 参考卡片 2.0](https:\u002F\u002Fcran.r-project.org\u002Fdoc\u002Fcontrib\u002FBaggott-refcard-v2.pdf)\n- [R 中的数据清洗](https:\u002F\u002Fwww.rstudio.com\u002Fwp-content\u002Fuploads\u002F2015\u002F02\u002Fdata-wrangling-cheatsheet.pdf)\n- [ggplot2 速查表](https:\u002F\u002Fwww.rstudio.com\u002Fwp-content\u002Fuploads\u002F2015\u002F08\u002Fggplot2-cheatsheet.pdf)\n- [Shiny 速查表](http:\u002F\u002Fshiny.rstudio.com\u002Fimages\u002Fshiny-cheatsheet.pdf)\n- [devtools 速查表](https:\u002F\u002Fwww.rstudio.com\u002Fwp-content\u002Fuploads\u002F2015\u002F06\u002Fdevtools-cheatsheet.pdf)\n- [markdown 速查表](https:\u002F\u002Fwww.rstudio.com\u002Fwp-content\u002Fuploads\u002F2015\u002F02\u002Frmarkdown-cheatsheet.pdf)、[参考文档](https:\u002F\u002Fwww.rstudio.com\u002Fwp-content\u002Fuploads\u002F2015\u002F03\u002Frmarkdown-reference.pdf)\n- [数据探索速查表](http:\u002F\u002Fwww.analyticsvidhya.com\u002Fblog\u002F2015\u002F10\u002Fcheatsheet-11-steps-data-exploration-with-codes\u002F)\n\n## 参考幻灯片\n- [R 参考卡片](https:\u002F\u002Fcran.r-project.org\u002Fdoc\u002Fcontrib\u002FBaggott-refcard-v2.pdf)\n- [关联规则挖掘](https:\u002F\u002F78462f86-a-e2d7344e-s-sites.googlegroups.com\u002Fa\u002Frdatamining.com\u002Fwww\u002Fdocs\u002FRDataMining-slides-association-rules.pdf?attachauth=ANoY7crD9hRI7333KWhK0TVPsS1VfgWoW4BuIsmL8B0NANfntEOq6QbcwJk-aCRUy2N6CmUeJsyrlOOd5bo1CqRUYXkEbSl1JbTniVbb-GSR3cyTt9Qq6xB3ZasMEdACaS9j1fZDiLVn_zLFbrF--aJM7gAu54JwRBhvKuQPOPyeMTosWcTmmrJdRNWH4ZqD5kYEJlmHDcXB8Bp-DWbUxZG2T8sAGbcHGUqkPTTJ_u03wvKyw5MGMrGU7q4xIyyUmBas_PqEDi6q&attredirects=0)\n- [时间序列分析](https:\u002F\u002F78462f86-a-e2d7344e-s-sites.googlegroups.com\u002Fa\u002Frdatamining.com\u002Fwww\u002Fdocs\u002FRDataMining-slides-time-series-analysis.pdf?attachauth=ANoY7cphtFEj6IMGuupE5ygQn5flMH5-QPE4yNgJ9fYv3WqfY0qU8LWGgiECZKs6P63Rhx5Nml8lQXQnX7QH7OZm1hoi_Kl0m9sLOAC0tc4sQipWC8DprQVoYSDyw0EdeJfZWAQor0AyjMWeFHPY6nqxIGAaj4arrwZcnR1dYC7nQK4dTVQM80ARrN5Yzq9rNbGic30X-xKwNQxOXL4fO54ThpzmNB4wLKv5geo_hDqPkwtKBmNR7u_kGPOymJHGvxP3nr02aJsB&attredirects=0)\n- [数据探索与可视化](https:\u002F\u002F78462f86-a-e2d7344e-s-sites.googlegroups.com\u002Fa\u002Frdatamining.com\u002Fwww\u002Fdocs\u002FRDataMining-slides-data-exploration-visualization.pdf?attachauth=ANoY7cpqnCTmCv1omsIoKmefAn8q6M_j4Hizv_1enJlu3nRPIxIhzjBlf-9B_sIxMxpUx-XN5cAw74GUr18Dn0EcaiIm9MVeCtqT-2dcPNo0dfhRJvnb5J8EHKBX_w7Y6mYgb7UAoIUbjdmVGR9VCIfJf6PGQqAlupywcb1yGbT4pv61bQzOzrU4-eICfgHmORdi8YgBqscyT2ThaKHPSeGXD0dd3g08pGN3bY70MKM02ZaqarewbII91KTNH1-zmELEcvatl_sMxmGgNnIDm6MaxEWQ1pIrTQ%3D%3D&attredirects=0)\n- [回归与分类](https:\u002F\u002F78462f86-a-e2d7344e-s-sites.googlegroups.com\u002Fa\u002Frdatamining.com\u002Fwww\u002Fdocs\u002FRDataMining-slides-regression-classification.pdf?attachauth=ANoY7cq0yqcj_65pafTfUqHazTYvp4E4r-5OB1kLv3swVKJhVydaJ0YU5yEPiOciQC0k_P1QzO6z1vD0r9E05KU8y7Mn6NTesQOOq_mmwlMqAe7D2mnqkHZBqFT6tk2hJ3g3fK40mvfyU5ggoGMxMYn9nVhihKwcIYJy9A8zlbFo4r9a35kpTDr6jJjAw5eQwSEMe-bvT5iyZuyMS7QS-tvlgHjJ40ZGhPro7GcWXfb7qqaPeTe9NyeU7MxAy2Z_lAzxn0vSnqe6&attredirects=0)\n- [Twitter 数据上的文本挖掘](https:\u002F\u002F78462f86-a-e2d7344e-s-sites.googlegroups.com\u002Fa\u002Frdatamining.com\u002Fwww\u002Fdocs\u002FRDataMining-slides-text-mining.pdf?attachauth=ANoY7cquEwmhHFNHxiKNhv6C2wquNdaib8A_BeTRFaGFXZ2deivENdTK-GS7mSZjermC7b_-L6KtCWhfF1ZOzOF9XaLkIaw6InCEnjdO1fWUhJFujaGwwbcbExJKEVuMmwlBX_SDUFZYgjuTbIb2llgKRMQc3Dd241HNZHTvGVuPG26vHKN_jU_WoEj7uIilRJWFTDvNrZWGWrvImWr0aCNou56qAB-zmBG_cvRS4QOQroiEetLpR7k%3D&attredirects=0)\n\n## 使用 R 进行多元分析\n- [多元分析 R 小册子！](http:\u002F\u002Flittle-book-of-r-for-multivariate-analysis.readthedocs.io\u002Fen\u002Flatest\u002F)\n- [用于多元可视化的小工具包 freqparcoord](https:\u002F\u002Fmatloff.wordpress.com\u002F2014\u002F03\u002F30\u002Fthe-freqparcoord-package-for-multivariate-visualization\u002F)\n- [freqparcoord 在回归诊断中的应用](http:\u002F\u002Fwww.r-bloggers.com\u002Fuse-of-freqparcoord-for-regression-diagnostics\u002F)\n\n## 时间序列分析\n- [**时间序列预测（在线书籍）**](https:\u002F\u002Fwww.otexts.org\u002Ffpp)\n- [**R 中的时间序列分析小册子**](http:\u002F\u002Fa-little-book-of-r-for-time-series.readthedocs.org\u002Fen\u002Flatest\u002Fsrc\u002Ftimeseries.html)\n- [Quick R：时间序列与预测](http:\u002F\u002Fwww.statmethods.net\u002Fadvstats\u002Ftimeseries.html)\n- [时间序列数据的组成部分](https:\u002F\u002Fwww.linkedin.com\u002Fpulse\u002Fcomponent-time-series-data-jeffrey-strickland-ph-d-cmsp)\n- [使用 R 构建未观测成分模型](https:\u002F\u002Fwww.linkedin.com\u002Fpulse\u002Funobserved-component-models-r-jeffrey-strickland-ph-d-cmsp)\n- [霍尔特-温特斯预测法](http:\u002F\u002Fwebarchive.nationalarchives.gov.uk\u002F20080726235635\u002Fhttp:\u002F\u002Fstatistics.gov.uk\u002Fiosmethodology\u002Fdownloads\u002FAnnex_B_The_Holt-Winters_forecasting_method.pdf)\n- [**CRAN 任务视图：时间序列分析**](https:\u002F\u002Fcran.r-project.org\u002Fweb\u002Fviews\u002FTimeSeries.html)\n\n## 贝叶斯推断\n- [用于贝叶斯推断的R包](https:\u002F\u002Fgithub.com\u002Fujjwalkarn\u002Fawesome-R#bayesian)\n- [R中的贝叶斯推断：视频](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=fiWIK7ONX3U)\n- [R与贝叶斯统计](http:\u002F\u002Fwww.r-bloggers.com\u002Fr-and-bayesian-statistics\u002F)\n\n## 使用R进行机器学习\n- [使用R进行机器学习](https:\u002F\u002Fgithub.com\u002Fjhashanti\u002FMachine-Learning-with-R)\n- [使用R进行多元分析（在线书籍）](http:\u002F\u002Flittle-book-of-r-for-multivariate-analysis.readthedocs.org\u002Fen\u002Flatest\u002Fsrc\u002Fmultivariateanalysis.html)\n- [CRAN任务视图：机器学习与统计学习](https:\u002F\u002Fcran.r-project.org\u002Fweb\u002Fviews\u002FMachineLearning.html)\n- [使用R进行机器学习（在线书籍）](https:\u002F\u002Fwww.otexts.org\u002Fsfml)\n- [线性回归与正则化代码](http:\u002F\u002Frpubs.com\u002Fjustmarkham\u002Flinear-regression-salary)\n- [速查表](http:\u002F\u002Fwww.analyticsvidhya.com\u002Fblog\u002F2015\u002F09\u002Ffull-cheatsheet-machine-learning-algorithms\u002F)\n- [**R中的多项式与有序逻辑回归**](http:\u002F\u002Fwww.analyticsvidhya.com\u002Fblog\u002F2016\u002F02\u002Fmultinomial-ordinal-logistic-regression\u002F)\n- [**在R中评估逻辑回归模型**](https:\u002F\u002Fwww.r-bloggers.com\u002Fevaluating-logistic-regression-models\u002F)\n\n## R中的神经网络\n- [在R中可视化神经网络](https:\u002F\u002Fbeckmw.wordpress.com\u002F2013\u002F11\u002F14\u002Fvisualizing-neural-networks-in-r-update\u002F)\n- [nnet包](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F21788817\u002Fr-nnet-with-a-simple-example-of-2-classes-with-2-variables)\n- [在R中拟合神经网络；neuralnet包](http:\u002F\u002Fwww.r-bloggers.com\u002Ffitting-a-neural-network-in-r-neuralnet-package\u002F)\n- [使用R的神经网络——一个简单示例](http:\u002F\u002Fgekkoquant.com\u002F2012\u002F05\u002F26\u002Fneural-networks-with-r-simple-example\u002F)\n- [NeuralNetTools 1.0.0现已上线CRAN](https:\u002F\u002Fbeckmw.wordpress.com\u002Ftag\u002Fneural-network\u002F)\n- [R中的神经网络简介](http:\u002F\u002Fwww.louisaslett.com\u002FCourses\u002FData_Mining\u002FST4003-Lab5-Introduction_to_Neural_Networks.pdf)\n- [逐步使用R构建神经网络](https:\u002F\u002Fbicorner.com\u002F2015\u002F05\u002F13\u002Fneural-networks-using-r\u002F)\n- [**R用于深度学习**](http:\u002F\u002Fwww.parallelr.com\u002Fr-deep-neural-network-from-scratch\u002F)\n- [使用neuralnet包的神经网络](http:\u002F\u002Fwww.di.fc.ul.pt\u002F~jpn\u002Fr\u002Fneuralnets\u002Fneuralnets.html)，[论文](https:\u002F\u002Fjournal.r-project.org\u002Farchive\u002F2010-1\u002FRJournal_2010-1_Guenther+Fritsch.pdf)\n\n## 情感分析\n- [不同的方法](https:\u002F\u002Fdrive.google.com\u002Fopen?id=0By_wg-rXnp_6U1JLNVA3cnAxZ3M)\n- [**使用R中的机器学习进行情感分析**](http:\u002F\u002Fdatascienceplus.com\u002Fsentiment-analysis-with-machine-learning-in-r\u002F)\n- [**首次尝试：R中的情感分析**](http:\u002F\u002Fandybromberg.com\u002Fsentiment-analysis\u002F)\n- [qdap包](https:\u002F\u002Fgithub.com\u002Ftrinker\u002Fqdap)，[代码](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F22774913\u002Festimating-document-polarity-using-rs-qdap-package-without-sentsplit)\n- [sentimentr包](https:\u002F\u002Fgithub.com\u002Ftrinker\u002Fsentimentr)\n- [tm.plugin.sentiment包](https:\u002F\u002Fgithub.com\u002Fmannau\u002Ftm.plugin.sentiment)\n- [除sentiment之外的其他包](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F15194436\u002Fis-there-any-other-package-other-than-sentiment-to-do-sentiment-analysis-in-r)\n- [情感分析与观点挖掘](https:\u002F\u002Fwww.cs.uic.edu\u002F~liub\u002FFBS\u002Fsentiment-analysis.html)\n- [tm_term_score](http:\u002F\u002Fwww.inside-r.org\u002Fpackages\u002Fcran\u002Ftm\u002Fdocs\u002Ftm_term_score)\n- [**vaderSentiment论文**](http:\u002F\u002Fcomp.social.gatech.edu\u002Fpapers\u002Ficwsm14.vader.hutto.pdf)，[**vaderSentiment代码**](https:\u002F\u002Fgithub.com\u002Fcjhutto\u002FvaderSentiment)\n\n## R中的插补\n- [**R中的插补**](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F13114812\u002Fimputation-in-r)\n- [使用随机森林进行插补](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F49270\u002Fimputation-with-random-forests)\n- [如何使用R识别并插补多个缺失值](http:\u002F\u002Fwww.unt.edu\u002Frss\u002Fclass\u002FJon\u002FBenchmarks\u002FMissingValueImputation_JDS_Nov2010.pdf)\n- MICE\n    - [mice r包中随机森林实现的错误](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F23974026\u002Ferror-in-implementation-of-random-forest-in-mice-r-package)\n    - [mice.impute.rf {mice}](http:\u002F\u002Fwww.inside-r.org\u002Fpackages\u002Fcran\u002Fmice\u002Fdocs\u002Fmice.impute.rf)\n\n## R中的自然语言处理与文本挖掘\n- [**我需要什么算法来查找n-gram？**](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F8161167\u002Fwhat-algorithm-i-need-to-find-n-grams)\n- [NLP R教程](http:\u002F\u002Fwww.r-bloggers.com\u002Fnatural-language-processing-tutorial\u002F)\n- [R中tm包文本挖掘简介](https:\u002F\u002Fcran.r-project.org\u002Fweb\u002Fpackages\u002Ftm\u002Fvignettes\u002Ftm.pdf)\n- [在R tm中添加停用词](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F18446408\u002Fadding-stopwords-in-r-tm)\n- [文本挖掘](http:\u002F\u002Fwww.r-bloggers.com\u002Ftext-mining\u002F)\n- [R中的词干提取](http:\u002F\u002Fwww.omegahat.net\u002FRstem\u002Fstemming.pdf)\n- [**使用文本挖掘包“tm”对文档进行分类**](http:\u002F\u002Fweb.letras.up.pt\u002Fbhsmaia\u002FEDV\u002Fapresentacoes\u002FBradzil_Classif_withTM.pdf)\n- [文本挖掘工具、技术和应用](http:\u002F\u002Fslidegur.com\u002Fdoc\u002F1830649\u002Ftext-mining)\n- [文本挖掘：概述、应用及问题](http:\u002F\u002Fwww3.cs.stonybrook.edu\u002F~cse634\u002FG8present.pdf)\n- [**文本挖掘pdf**](http:\u002F\u002Fwww3.cs.stonybrook.edu\u002F~cse634\u002Fpresentations\u002FTextMining.pdf)\n- [另一份文本挖掘pdf](http:\u002F\u002Fwww.stat.columbia.edu\u002F~madigan\u002FW2025\u002Fnotes\u002FIntroTextMining.pdf)\n- [优秀的PPT](http:\u002F\u002Fstudylib.net\u002Fdoc\u002F5800473\u002Ftopic7-textmining)\n- [**使用R抓取Twitter和Web数据**](http:\u002F\u002Fwww.nyu.edu\u002Fprojects\u002Fpoliticsdatalab\u002Flocaldata\u002Fworkshops\u002Ftwitter.pdf)\n\n## R中的可视化\n- [ggplot2教程](http:\u002F\u002Fwww.ling.upenn.edu\u002F~joseff\u002Favml2012\u002F)\n- [SHINY EXAMPLES](https:\u002F\u002Fgithub.com\u002Frstudio\u002Fshiny-examples)\n- [**前50名ggplot2可视化**](http:\u002F\u002Fr-statistics.co\u002FTop50-Ggplot2-Visualizations-MasterList-R-Code.html)\n- [R中数据可视化的综合指南](http:\u002F\u002Fwww.analyticsvidhya.com\u002Fblog\u002F2015\u002F07\u002Fguide-data-visualization-r\u002F)\n- [使用R进行交互式可视化——简短评论](http:\u002F\u002Fwww.r-bloggers.com\u002Finteractive-visualizations-with-r-a-minireview\u002F)\n- [R入门指南：轻松实现数据可视化](http:\u002F\u002Fwww.computerworld.com\u002Farticle\u002F2497304\u002Fbusiness-intelligence-beginner-s-guide-to-r-painless-data-visualization.html)\n- [使用ggvis在R中进行数据可视化](https:\u002F\u002Fwww.datacamp.com\u002Fcourses\u002Fggvis-data-visualization-r-tutorial)\n- [关于R中可视化的多篇文章](http:\u002F\u002Fwww.r-statistics.com\u002Ftag\u002Fvisualization\u002F)\n\n## 使用R进行统计\n- [使用R进行生物医学统计（在线书籍）](http:\u002F\u002Fa-little-book-of-r-for-biomedical-statistics.readthedocs.org\u002Fen\u002Flatest\u002Fsrc\u002Fbiomedicalstats.html)\n- [R中的基础统计](http:\u002F\u002Fwww.r-tutor.com\u002Felementary-statistics)\n- [动手学习R中的统计学](https:\u002F\u002Fwww.datacamp.com\u002Fintroduction-to-statistics)\n- [Quick R：基础统计](http:\u002F\u002Fwww.statmethods.net\u002FstATS\u002Findex.html)\n- [Quick R：描述性统计](http:\u002F\u002Fwww.statmethods.net\u002Fstats\u002Fdescriptives.html)\n- [通过R探索统计学 | edX](https:\u002F\u002Fwww.edx.org\u002Fcourse\u002Fexplore-statistics-r-kix-kiexplorx-0)\n\n## 有用的 R 包\n- [**TIDY DATA 哈德利论文**](https:\u002F\u002Fwww.jstatsoft.org\u002Farticle\u002Fview\u002Fv059i10)\n    - 包 ‘tidyr’：tidyr 是 reshape2 的演進版本。它的設計專門用於數據整理（而非一般的重塑或聚合），並且能很好地與 dplyr 的數據管道配合使用。\n- [BROOM](https:\u002F\u002Fgithub.com\u002Fdgrtwo\u002Fbroom)\n- [**plyr、stringr、reshape2 教程**](http:\u002F\u002Fwww.dataschool.io\u002Ftidying-messy-data-in-r\u002F) [視頻](https:\u002F\u002Fvimeo.com\u002F33727555)、[代碼](https:\u002F\u002Fgithub.com\u002Fjustmarkham\u002Ftidy-data)\n- dplyr\n    - [此倉庫中的代碼文件](https:\u002F\u002Fgithub.com\u002Fujjwalkarn\u002FDataScienceR\u002Ftree\u002Fmaster\u002FIntro%20to%20dplyr)\n    - [dplyr 教程 1](http:\u002F\u002Fwww.dataschool.io\u002Fdplyr-tutorial-for-faster-data-manipulation-in-r\u002F)、[dplyr 教程 2](http:\u002F\u002Fwww.dataschool.io\u002Fdplyr-tutorial-part-2\u002F)\n    - [用 dplyr 像老闆一樣完成數據清理工作](http:\u002F\u002Fwww.gettinggeneticsdone.com\u002F2014\u002F08\u002Fdo-your-data-janitor-work-like-boss.html)\n- ggplot2\n    - [ggplot2 教程](http:\u002F\u002Fwww.ling.upenn.edu\u002F~joseff\u002Favml2012\u002F)\n    - [優秀教程！](https:\u002F\u002Fgithub.com\u002Fjennybc\u002Fggplot2-tutorial)\n    - [ggplot2 簡介](https:\u002F\u002Fspeakerdeck.com\u002Fkarthik\u002Fintroduction-to-ggplot2)、[GitHub](https:\u002F\u002Fgithub.com\u002Fkarthik\u002Fggplot-lecture)\n    - [ggplot() 快速入門](http:\u002F\u002Fwww.noamross.net\u002Fblog\u002F2012\u002F10\u002F5\u002Fggplot-introduction.html)\n    - [R 繪圖 Cookbook](http:\u002F\u002Fwww.cookbook-r.com\u002FGraphs\u002Findex.html)\n    - [《Lattice: 多變量數據可視化與 R》中圖表的 ggplot2 版本](https:\u002F\u002Flearnr.wordpress.com\u002F2009\u002F06\u002F28\u002Fggplot2-version-of-figures-in-lattice-multivariate-data-visualization-with-r-part-1\u002F)\n- [plyr、data.table 和 dplyr 的速度測試比較](http:\u002F\u002Fwww.r-statistics.com\u002F2013\u002F09\u002Fa-speed-test-comparison-of-plyr-data-table-and-dplyr\u002F)\n- data.table\n    - [R 中 data.table 包的介紹](https:\u002F\u002Fcran.r-project.org\u002Fweb\u002Fpackages\u002Fdata.table\u002Fvignettes\u002Fdatatable-intro.pdf)\n    - [使用 data.table 在 R 中快速計算摘要統計量](http:\u002F\u002Fblog.yhat.com\u002Fposts\u002Ffast-summary-statistics-with-data-dot-table.html)\n- 其他包\n    - 包 ‘e1071’\n    - 包 ‘AppliedPredictiveModeling’\n    - 包 ‘stringr’：stringr 是一組簡單的封裝函數，使 R 的字符串函數更加一致、簡潔且易於使用。\n    - 包 ‘stringdist’：實現了 R 原生 'match' 函數的近似字符串匹配版本。可以根據編輯距離（達梅勞-萊文斯坦、漢明、萊文斯坦、最佳字符串對齊）、qgrams 或啟發式指標來計算各種字符串距離。\n    - 包 ‘FSelector’：該包提供從給定數據集中選擇屬性的函數。\n    - [Ryacas – R 與 yacas 符號計算系統的接口](https:\u002F\u002Fcran.r-project.org\u002Fweb\u002Fpackages\u002FRyacas\u002Fvignettes\u002FRyacas.pdf)\n    - [Scatterplot3d – 用於多變量數據可視化的 R 包](https:\u002F\u002Fcran.r-project.org\u002Fweb\u002Fpackages\u002Fscatterplot3d\u002Fvignettes\u002Fs3d.pdf)\n    - [tm.plugin.webmining 簡介](https:\u002F\u002Fcran.r-project.org\u002Fweb\u002Fpackages\u002Ftm.plugin.webmining\u002Fvignettes\u002FShortIntro.pdf)\n    - [在 R 中解微分方程 - ODE 示例](https:\u002F\u002Fcran.r-project.org\u002Fweb\u002Fpackages\u002FdiffEq\u002Fvignettes\u002FODEinR.pdf)\n    - [使用 R 中 sem 包進行結構方程模型分析](http:\u002F\u002Fsocserv.socsci.mcmaster.ca\u002Fjfox\u002FMisc\u002Fsem\u002FSEM-paper.pdf)\n    - [prettyScree - prettyGraphs](http:\u002F\u002Fwww.inside-r.org\u002Fpackages\u002Fcran\u002FprettyGraphs\u002Fdocs\u002FprettyScree)\n\n## R 中的市場籃子分析\n- [使用 R 進行市場籃子分析](http:\u002F\u002Fwww.salemmarafi.com\u002Fcode\u002Fmarket-basket-analysis-with-r\u002F)\n- [市場籃子分析的逐步解釋](http:\u002F\u002Fdni-institute.in\u002Fblogs\u002Fmarket-basket-analysis-step-by-step-approach-using-r\u002F)","# DataScienceR 快速上手指南\n\nDataScienceR 并非一个单一的 R 包，而是一个精选的 **R 语言数据科学教程、代码片段和资源列表**。它涵盖了数据清洗、自然语言处理（NLP）、机器学习及可视化等核心领域。本指南将帮助你搭建环境并获取这些核心学习资源。\n\n## 环境准备\n\n在开始之前，请确保你的系统满足以下要求：\n\n*   **操作系统**：Windows, macOS 或 Linux。\n*   **核心依赖**：\n    *   **R**: 版本建议 4.0 及以上。\n    *   **RStudio** (推荐): 集成开发环境，提供更友好的编码体验。\n*   **网络环境**：部分资源链接托管于 GitHub 或国外教育平台，国内访问可能较慢，建议配置好网络环境或使用镜像站下载 R 包。\n\n## 安装步骤\n\n由于 DataScienceR 是资源索引库，你不需要“安装”该仓库本身，而是需要安装 R 环境以及仓库中推荐的核心数据分析包（如 `tidyverse`, `caret` 等）。\n\n### 1. 安装 R 和 RStudio\n*   **R 下载**：访问 CRAN 官网或国内镜像（如清华大学镜像）下载安装包。\n    *   清华镜像地址：`https:\u002F\u002Fmirrors.tuna.tsinghua.edu.cn\u002FCRAN\u002F`\n*   **RStudio 下载**：访问 RStudio 官网下载安装免费版。\n\n### 2. 配置国内镜像源 (加速包安装)\n在 R 或 RStudio 控制台执行以下命令，将默认源更改为清华大学镜像，以大幅提升安装包速度：\n\n```R\noptions(repos = c(CRAN = \"https:\u002F\u002Fmirrors.tuna.tsinghua.edu.cn\u002FCRAN\u002F\"))\n```\n\n### 3. 安装核心数据科学包\n根据资源列表中的推荐，安装最常用的数据处理和机器学习套件：\n\n```R\n# 安装 tidyverse (数据清洗与可视化核心)\ninstall.packages(\"tidyverse\")\n\n# 安装 caret (机器学习模型训练统一接口)\ninstall.packages(\"caret\")\n\n# 安装 devtools (用于从 GitHub 安装特定开发版工具)\ninstall.packages(\"devtools\")\n```\n\n### 4. 获取教程资源\n你可以直接克隆该仓库到本地，以便离线查阅目录中的教程链接和代码示例：\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Fujjwalkarn\u002FDataScienceR.git\n```\n\n## 基本使用\n\n本项目的核心价值在于其整理的**学习路径**和**常用操作速查**。以下是如何利用这些资源进行第一个数据分析任务的示例。\n\n### 1. 加载核心库\n启动 RStudio，加载 `tidyverse` 套件，它包含了数据操作 (`dplyr`) 和绘图 (`ggplot2`) 等功能：\n\n```R\nlibrary(tidyverse)\n```\n\n### 2. 数据导入与探索 (参考 \"Common DataFrame Operations\")\n利用内置数据集 `mtcars` 演示基本的 DataFrame 操作（对应资源列表中的常见操作章节）：\n\n```R\n# 查看数据前几行\nhead(mtcars)\n\n# 数据筛选：找出马力 (hp) 大于 150 的车辆\nfiltered_data \u003C- mtcars %>% filter(hp > 150)\n\n# 数据排序：按油耗 (mpg) 降序排列\nsorted_data \u003C- mtcars %>% arrange(desc(mpg))\n\n# 简单可视化：绘制油耗与马力的散点图\nggplot(mtcars, aes(x = hp, y = mpg)) + \n  geom_point() + \n  labs(title = \"Horsepower vs MPG\", x = \"Horsepower\", y = \"Miles Per Gallon\")\n```\n\n### 3. 机器学习建模 (参考 \"Caret Package in R\")\n使用 `caret` 包快速构建一个线性回归模型（对应资源列表中的 Caret 章节）：\n\n```R\nlibrary(caret)\n\n# 划分训练集和测试集 (80% 训练)\nset.seed(123)\ntrain_index \u003C- createDataPartition(mtcars$mpg, p = 0.8, list = FALSE)\ntrain_data \u003C- mtcars[train_index, ]\ntest_data \u003C- mtcars[-train_index, ]\n\n# 训练模型：使用马力预测油耗\nmodel \u003C- train(mpg ~ hp, data = train_data, method = \"lm\")\n\n# 查看模型结果\nprint(model)\npredict(model, newdata = test_data)\n```\n\n### 4. 进阶学习指引\n根据仓库目录结构，你可以针对性地深入学习：\n*   **基础语法**：参考 `Learning R` 章节中的 \"R for Data Science\" 在线书。\n*   **速查手册**：下载 `R Cheatsheets` 章节提供的 PDF (如 Data Wrangling, ggplot2)，贴在桌边随时查阅。\n*   **专题突破**：针对时间序列或文本挖掘，直接跳转至 `Time Series Analysis` 或相关 NLP 教程链接。","某金融分析师急需利用 R 语言构建用户流失预测模型，但面对庞杂的生态资源感到无从下手。\n\n### 没有 DataScienceR 时\n- **资源检索低效**：在搜索引擎中盲目查找教程，常被过时博客或付费广告误导，难以甄别高质量内容。\n- **学习路径断裂**：从基础语法到机器学习算法缺乏系统性指引，频繁在不同网站间跳转，知识体系支离破碎。\n- **实战代码匮乏**：找到理论文章后，往往找不到对应的可运行代码或具体包推荐，导致“看懂了却写不出”。\n- **工具选型困难**：面对 CRAN 上成千上万个包，无法快速确定哪些是处理 NLP 或数据清洗的行业标准工具。\n\n### 使用 DataScienceR 后\n- **精选资源直达**：直接获取经社区验证的教程列表（如 Hadley Wickham 的经典著作），瞬间锁定权威学习材料。\n- **体系化进阶**：依托清晰的分类结构，按“在线课程 -> 专项书籍 -> 实战技巧”的路径稳步提升，逻辑连贯。\n- **代码即拿即用**：通过链接快速访问包含完整代码实现的案例（如 17 种分类算法实现），大幅缩短从理论到实践的距离。\n- **精准工具匹配**：参考 curated list 中针对特定任务（如脏数据清洗、生命周期网格分析）推荐的包，迅速搭建工作流。\n\nDataScienceR 将散乱的 R 语言资源转化为结构化导航图，让数据科学家从“找资料”转向“做分析”，显著提升研发效率。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fujjwalkarn_DataScienceR_458042b8.png","ujjwalkarn","Ujjwal Karn","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fujjwalkarn_a8820a4b.png",null,"@facebook","San Francisco","ujjwalkarn.me","https:\u002F\u002Fgithub.com\u002Fujjwalkarn",[84,88],{"name":85,"color":86,"percentage":87},"R","#198CE7",99.9,{"name":89,"color":90,"percentage":91},"Rebol","#358a5b",0.1,2086,877,"2026-04-02T19:20:06","MIT",1,"未说明",{"notes":99,"python":100,"dependencies":101},"该项目并非可执行的软件工具，而是一个 R 语言数据科学、NLP 和机器学习教程及资源的 curated 列表（集合）。因此，它没有特定的操作系统、GPU 或内存硬件需求。用户只需安装标准的 R 环境和可选的 RStudio，并根据列表中提到的具体教程自行安装相应的 R 包即可。","不适用 (基于 R 语言)",[102,103,104,105,106,107,108,109],"R (基础环境)","caret","ggplot2","shiny","devtools","rmarkdown","Formula","HSAUR",[52,15,26,53,14,55,54,51,13],[112,113,114,115],"datascience","data-science","r","text-mining","2026-03-27T02:49:30.150509","2026-04-06T05:16:49.693677",[],[]]