Updated Library

Author: Varnasr

learning_library/ABOUT_THIS_LIBRARY.md

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+# ABOUT_THIS_LIBRARY.md
+
+## 📚 What Is This?
+
+This learning library is a curated collection of handbooks, guides, visual references, and templates designed to support:
+- Independent learners seeking to upskill in data science, programming, or AI tools
+- Researchers exploring methodological depth or open paradigms
+- Teams working with AI-enhanced workflows in Excel or R
+
+It brings together open-access resources and review copies in a structured, thematically organized way.
+
+---
+
+## 🎯 Why It Was Built
+
+This library was assembled to bridge practical learning (e.g., Excel + AI, creative programming) with foundational thinking (e.g., research design, epistemology). It reflects a commitment to:
+- Open learning
+- Interdisciplinary fluency
+- Practical application
+
+---
+
+## 📁 How It's Organized
+
+Each folder in this library is organized by theme:
+
+- `01_programming/` → Creative and generative coding
+- `02_data_science/` → Analysis and visualization using R or Excel
+- `03_research_methods/` → Critical paradigms, design, and epistemology
+- `04_ai_tools/` → Prompt engineering and AI productivity (cross-linked)
+- `05_visual_summaries/` → Illustrated concepts for visual reinforcement
+
+---
+
+## ⚖️ Licensing Notes
+
+Many files here are educational review copies or open-licensed works:
+
+- *GO-GN Research Methods Handbook* is CC-BY 4.0  
+- Others are included under educational fair use for private study and scholarship  
+- Attribution is preserved in filenames and summaries
+
+If you believe a file should be removed or replaced, please open an issue or pull request.
+

learning_library/CONTRIBUTING.md

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+# CONTRIBUTING.md
+
+Thank you for considering contributing to this learning library!
+
+This project is open to thoughtful additions, annotations, and organizational improvements.
+
+---
+
+## ✅ What You Can Contribute
+
+- 📘 **New resources**: Books, guides, or papers in PDF/Markdown format
+- 📝 **Summaries or excerpt notes** for existing files
+- 🧰 **Templates**: R markdowns, prompt libraries, or research checklists
+- 🔖 **Tags**: Add useful topical tags to `README.md` entries
+
+---
+
+## 📁 Folder Placement
+
+- Place programming/visual examples in `01_programming/`
+- Data and analytics guides in `02_data_science/`
+- Theoretical and methodology resources in `03_research_methods/`
+- Generative AI or automation workflows in `04_ai_tools/`
+- Sketch-based or visual guides in `05_visual_summaries/`
+
+---
+
+## 🧾 Naming Convention
+
+```
+[AuthorLastName] - [ShortTitle] ([Year]).pdf
+```
+
+Examples:
+- `Gardener - An Introduction to R (2023).pdf`
+- `Lo Duca - Generative AI for Excel (2025).pdf`
+
+---
+
+## 🔄 How to Contribute
+
+1. Fork this repository
+2. Add your files to the appropriate folder
+3. Update the folder's `README.md` with a summary, tags, and excerpt
+4. Open a pull request describing your changes
+
+Thank you for supporting open learning ✨

learning_library/USE_CASES.md

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+# USE_CASES.md
+
+This guide outlines how to navigate the Learning Library depending on your goals and experience level.
+
+---
+
+## 🎓 1. For Researchers Designing a Study
+**Goal:** Ground your research in solid paradigms and practical methods.
+
+**Recommended Path:**
+- 📘 `03_research_methods/Research Methods Handbook.pdf`
+- 📘 `03_research_methods/Network Collective Action.pdf`
+- 📘 `02_data_science/An Introduction to R.pdf` (for analyzing collected data)
+
+---
+
+## 🤖 2. For AI-Enhanced Productivity in Excel
+**Goal:** Use AI tools like Copilot and ChatGPT to speed up spreadsheet work.
+
+**Recommended Path:**
+- 📘 `02_data_science/Learning Generative AI Tools for Excel.pdf`
+- 🧰 `workbooks/excel_prompt_templates.md` (includes reusable prompts)
+- Also keep an eye on `04_ai_tools/` for future additions
+
+---
+
+## 📈 3. For Self-Learners Upskilling in Data Science
+**Goal:** Build foundations in programming, analysis, and visual interpretation.
+
+**Recommended Path:**
+- 🎨 `01_programming/An Artist's Guide to Programming.pdf` (especially for visual thinkers)
+- 📘 `02_data_science/An Introduction to R.pdf`
+- 🖍️ `05_visual_summaries/101 Data Science Drawings.pdf`

learning_library/workbooks/excel_prompt_templates.md

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+# excel_prompt_templates.md
+
+A collection of reusable prompts for working with Excel and AI tools like ChatGPT or Copilot.
+
+---
+
+## 📊 Summarize Data Table
+> "Summarize the key trends and outliers in this Excel table. Highlight anything unusual."
+
+## 📈 Generate Chart Suggestion
+> "Given this dataset, what type of chart best visualizes the difference between columns A and B?"
+
+## 🧹 Clean Data
+> "Identify and suggest fixes for inconsistencies, blank cells, or wrongly formatted dates."
+
+## 📋 Validate Columns
+> "Check if all values in the 'Date' column follow DD-MM-YYYY format. Flag any mismatches."
+
+## 🔍 Insight Extraction
+> "Based on this data, give me three actionable insights that could be shared in a report."
+

learning_library/workbooks/intro_to_r_cheatsheet.md

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+# intro_to_r_cheatsheet.md
+
+A practical starter guide based on *An Introduction to R* by Mark Gardener.
+
+---
+
+## ✅ Load R and Get Help
+```r
+# Start R and use help
+help.start()
+?mean
+```
+
+## 📊 Create a Simple Dataset
+```r
+heights <- c(150, 160, 170, 165, 180)
+weights <- c(55, 60, 72, 68, 80)
+```
+
+## 📉 Plot It
+```r
+plot(heights, weights, main="Height vs Weight", xlab="Height (cm)", ylab="Weight (kg)")
+```
+
+## 📚 Run a Linear Model
+```r
+lm_result <- lm(weights ~ heights)
+summary(lm_result)
+```