Applied Statistics with R A Practical Guide for the Life Sciences (Justin C. Touchon) (Z-Library)

【One-Line Pitch】 A practical, hands-on guide for life-science students and researchers who want to learn statistical analysis in R using a single real-world dataset, progressing from basic data manipulation to advanced mixed-effects models. 【Book Arc】 - **Opening (~0%–9%)**: Introduces R and RStudio, covering installation, package management, and the crucial habit of working from script windows rather than the command line. Establishes the book's philosophy: use one realistic dataset (the Resource-by-Predation experiment on frog metamorphs) throughout to build familiarity. - **Early (~9%–25%)**: Covers R fundamentals—creating objects, understanding vectors and data frames, handling missing data (NA vs. 0), and writing well-documented code. Includes practical advice on experimental design and why you should plan analyses before collecting data, with a cautionary tale from the author's own fieldwork mistakes. - **Early (~25%–34%)**: Transitions into exploratory data analysis (EDA) and data summarization. Teaches how to check data imports, identify typos and outliers, and use log-transformations to linearize relationships. Emphasizes the importance of viewing data through multiple lenses before modeling. - **Middle (~34%–47%)**: Delves into data wrangling with dplyr (group_by, summarize, the pipe operator %>%) and advanced plotting with ggplot2 and base R graphics. Covers boxplots, scatterplots, faceting, barplots with error bars, and customizing axes and legends—all using the frog dataset. - **Middle (~47% onward)**: Continues with more sophisticated visualization techniques and sets the stage for statistical modeling. The excerpts show a clear progression toward linear models, generalized linear models, and mixed-effects versions, though detailed model-fitting chapters are not fully covered in this sample. 【Key Takeaways】 - **Script windows are non-negotiable for reproducible work** (Opening): Typing directly into the command prompt loses your code; using script windows lets you save, edit, and rerun analyses. This habit is foundational for all subsequent chapters. - **Meaningful object names save time and prevent errors** (Early): Following the advice "You wouldn't call your dog 'dog,' so don't call your data 'data,'" the book stresses short but descriptive names for datasets, variables, and models—critical for complex projects. - **NA is the only acceptable way to code missing data** (Early): Using zeros for missing values corrupts analyses because R treats 0 as a real observation. Functions like mean() require na.rm=TRUE to handle NAs properly. - **Plan your analyses before collecting data** (Early): The author's personal story of being stuck with unanalyzable data in Panama illustrates why experimental design and statistical planning must happen upfront, not after fieldwork. - **Exploratory data analysis is detective work** (Early–Middle): Checking column types, row counts, and plotting variables against each other reveals typos and outliers. Log-transformations can linearize biological relationships, as shown with frog snout-vent length vs. mass. - **The pipe operator %>% transforms workflow efficiency** (Middle): Chaining functions with pipes makes code readable and powerful, especially when combined with group_by() and summarize() for calculating group means and other statistics. - **ggplot2 and base R both have strengths for publication-quality figures** (Middle): The book demonstrates faceting, color-coding by treatment, and adding error bars via barplot2() or geom_errorbar(), giving readers flexibility in plotting style. 【Reading Tips】 - **Skim Chapter 1 if you're already comfortable with R basics**, but don't skip the sections on data frames and NA handling—they're essential for later chapters. - **Deep-read Chapter 2 on experimental design** even if you're eager to code; the principles about planning analyses before data collection will save you from the author's own painful mistakes. - **Practice the dplyr and ggplot2 examples actively**—type the code yourself rather than copying, and experiment with modifying arguments (e.g., changing colors, facet layouts) to internalize the syntax. - **Pay special attention to the outlier discussion in Chapter 3**: the advice to run models with and without suspicious points is a practical, transferable skill for any dataset. - **Expect a learning curve with the pipe operator and data wrangling**; the book acknowledges it's challenging but emphasizes it pays off in Chapter 9 and beyond. 【Coverage Limits】 This guide covers the opening through the middle of the book (roughly 0–47%), focusing on R fundamentals, experimental design, EDA, data wrangling, and plotting. Detailed coverage of linear models, generalized linear models, and mixed-effects models (likely in later chapters) is not included in the sampled excerpts.