R for Data Science Import, Tidy, Transform, Visualize, and Model Data (Hadley Wickham, Mine Çetinkaya-Rundel etc.)（Z-Library）

【One-Line Pitch】 A hands-on guide to doing data science in R with the tidyverse, taking you from your first plot to a complete, reproducible analysis workflow. Best for aspiring data scientists and analysts who want practical skills, even with no prior programming experience. 【Book Arc】 - **Opening (~0%–10%)**: Sets up the whole data-science cycle and your toolkit—installing R/RStudio, loading the tidyverse, and reading the book's code conventions—so you can run code immediately. - **Early (~10%–30%)**: Builds the core loop of visualizing and transforming data: ggplot2 grammar of graphics, dplyr verbs, the pipe, groups, and tidy data pivoting with tidyr. - **Middle (~30%–50%)**: Moves into workflow craft—project organization, importing data from files, getting help, and layering plots with geoms, stats, and coordinate systems. - **Late (~50%–75%)**: Deepens the toolkit with programming tools (functions, iteration) and communication via Quarto, plus expanded import sources like spreadsheets, databases, and websites. - **Ending (~75%–100%)**: Rounds out the cycle with modeling and exploratory data analysis, then ties everything together into a reproducible, communicable result. 【Key Takeaways】 - **The data-science cycle is the book's spine** (Opening): import → tidy → transform → visualize → model → communicate, giving you a mental map before you touch syntax. - **Visualization is a grammar, not a gallery** (Early): mapping variables to aesthetics versus setting fixed values, and choosing geoms, is the core skill for exploration and communication. - **Tidy data is the prerequisite for everything downstream** (Early): pivot_longer() and pivot_wider() reshape messy real-world data into variables-in-columns, observations-in-rows form. - **dplyr plus the pipe makes transformation fluent** (Early): filter, group_by, summarize, and the native |> pipe let you express multi-step data manipulation readably. - **Workflow discipline separates beginners from experts** (Middle): RStudio Projects, working directories, and reproducible examples (reprex) prevent the chaos of scattered files and unrepeatable analyses. - **Import is broader than CSV** (Middle): read_csv() covers most files, but the book extends to spreadsheets, databases, and websites so real data sources are in reach. - **Programming tools reduce repetition** (Late): writing functions and iterating clarify data problems that copy-paste code obscures. - **Communication is part of the analysis** (Late): Quarto integrates prose, code, and results so findings are shareable, not just computed. 【Reading Tips】 - Deep-read the visualization and transformation chapters early; they are the foundation every later chapter assumes. - Skim the workflow chapters (code style, getting help, projects) on a first pass, then return when your own projects get messy. - Type and run the exercises rather than reading passively—the book is explicitly practice-driven, and errors teach the most. - Treat pivoting and the pipe as the two hardest early hurdles; slow down there before moving to modeling. - Use the book as a reference after finishing: jump back to import or Quarto chapters when a real project demands them. 【Coverage Limits】 The excerpts cover the book's structure, early visualization/transformation material, workflow, and pivoting, but do not detail the modeling, programming, or Quarto chapters in depth. Specific chapter titles and later examples are therefore summarized at a high level.