R语言实战（第2版） (Robert I. Kabacoff)（Z-Library）

# R in Action (2nd Edition) — Reading Guide ## 【One-Line Pitch】 A practical, example-driven tour of R for data analysis—covering everything from data import and manipulation to statistical modeling, visualization, and advanced programming—ideal for analysts, researchers, and programmers who want to go from R novice to confident practitioner. ## 【Book Arc】 - **Opening (~0%–10%)**: Introduces R's philosophy, interface, and core workflow—entering data, running basic functions (mean, sd, cor, plot), saving outputs as objects for reuse, and handling large datasets with memory considerations. - **Early (~10%–30%)**: Builds the data-management foundation—creating datasets, recoding and renaming variables, subsetting data, understanding control structures (for/while loops, conditionals), and writing your first functions. - **Early (~17%–37%)**: Covers graphics fundamentals—customizing plots with titles, axes, colors, and legends; combining multiple graphs; then moves into basic statistical graphics (bar charts, histograms, boxplots, dot plots) and foundational statistics (frequency tables, cross-tabulations). - **Middle (~37%–50%)**: Dives into intermediate methods—ordinary least squares regression with full diagnostic workflows (normality checks, influence plots, cross-validation), ANOVA and its variants, and power analysis for determining required sample sizes. - **Late (~50%–100%)**: Extends to advanced topics—generalized linear models, resampling and bootstrap methods, time series, cluster analysis, classification, advanced ggplot2 graphics, and programming topics like creating packages and dynamic reports (per the 2nd edition's new chapters). ## 【Key Takeaways】 - **R is a language built around objects and functions** (Early): Every analysis step—from simple mean() to complex lm()—produces objects you can save, inspect, and feed into subsequent analyses, making workflows modular and reproducible. - **Data frames are the central data structure** (Early): Mastering data-frame operations—creating, attaching, subsetting, recoding, and renaming variables—is the prerequisite for everything else in R. - **with() and attach() solve different problems** (Early): attach() can cause name conflicts with existing objects; with() is safer but assignments don't persist outside its scope unless you use the special <<- operator. - **Base graphics are built incrementally** (Early): R's plotting system is interactive—you start with plot(), then layer on abline(), title(), and other functions; par() sets global parameters while plot() arguments affect only that graph. - **Regression is a multi-step, iterative process** (Middle): Fitting a model with lm() is just the beginning—proper analysis requires checking assumptions (normality via qqPlot, influence via Cook's distance), and cross-validation to assess predictive power. - **Power analysis answers "how many subjects do I need?"** (Middle): The pwr package lets you calculate required sample sizes for given effect sizes and power levels, and you can generate curves to visualize these trade-offs. - **Robust methods handle real-world data violations** (Middle): When data don't meet normality assumptions, resampling approaches like bootstrap (boot package) provide alternatives that are computationally intensive but easy to implement in R. ## 【Reading Tips】 - **Skim Chapter 1–2 if you're experienced**: The basics of R sessions, data entry, and data frames are essential for beginners but can be reviewed quickly if you already know another data-analysis tool. - **Deep-read Chapters 3–4 for the foundation**: Graphics customization and data management are where most practical R work happens—these chapters repay careful study with code you'll reuse constantly. - **Work through Chapter 8's regression diagnostics carefully**: The workflow—fit, check assumptions, identify influential points, cross-validate—is the template for all serious modeling in R. - **Use the code examples as templates**: The book's strength is its practical, copy-paste-ready examples (like the power-analysis curve generation in Chapter 10); type them out and modify them for your own data. - **Pay attention to package recommendations**: The book highlights key packages (car, vcd, pwr, boot, ggplot2) that extend base R—these are the tools you'll actually use in real projects. ## 【Coverage Limits】 This guide covers the book's core progression through data management, basic graphics, statistics, and regression (roughly the first half). The excerpts do not cover the later chapters on time series, clustering, classification, advanced ggplot2, package creation, or dynamic reports in detail—these are noted as new content in the 2nd edition but their specifics fall outside the sampled material. ##