A Practical Guide to Data Analysis Using R An Example-Based Approach, 1st Edition

An example-based practical guide to data analysis, ‘A Practical Guide to Data Analysis Using R An Example-Based Approach,’ is a complete exploration of data analysis models within specific research contexts. This text builds on the foundation laid in ‘Data Analysis and Graphics Using R,’ 3rd edition (Cambridge, 2010), and covers advanced topics such as cluster analysis, exponential time series, matching, seasonality, and resampling approaches. Readers are given access to different real-world examples that provide insight into these analyses' underlying assumptions while enabling them to verify their validity.

The book “A Practical Guide to Data Analysis Using R An Example-Based Approach” extensively discusses p-values, including issues like replicability and multiple p-values occurring in settings such as gene expression analysis. Providing practical intuition enables scientists to effectively analyze their own data so that students have a more profound understanding of statistical theory through practical experience in data analysis. With accompanying comments on worked examples or the absence thereof, this helps readers identify when a procedure is appropriate or not, which encourages critical thinking during data analysis.

Every chapter is filled with exercises that reinforce learning, and some solutions, notes, slides, and R code can be found online. The vast references point to further sources of detailed information on using R effectively. “A Practical Guide to Data Analysis Using R An Example-Based Approach” is very useful for professionals willing to improve their analytical skills and learners who want to relate statistical theory and practical data analysis.

A Practical Guide to Data Analysis Using R An Example-Based Approach Table of Contents:

1. Learning from Data, and Tools for the Task
   1. Questions, and Data That May Point to Answers
   2. Graphical Tools for Data Exploration
   3. Data Summary
   4. Distributions: Quantifying Uncertainty
   5. Simple Forms of Regression Model
   6. Data-Based Judgments - Frequentist, in a Bayesian World
   7. Information Statistics and Bayesian Methods with Bayes Factors
   8. Resampling Methods for SEs, Tests, and Confidence Intervals
   9. Organizing and Managing Work, and Tools That Can Assist
   10. The Changing Environment for Data Analysis
   11. Further, or Supplementary, Reading
   12. Exercises
2. Generalizing from Models
   1. Model Assumptions
   2. t-Statistics, Binomial Proportions, and Correlations
   3. Extra-Binomial and Extra-Poisson Variation
   4. Contingency Tables
   5. Issues for Regression with a Single Explanatory Variable
   6. Empirical Assessment of Predictive Accuracy
   7. One- and To-Way Comparisons
   8. Data with a Nested Variation Structure
   9. Bayesian Estimation - Further Commentary, and Approaches
   10. Recap
   11. Further Reading
   12. Exercises
3. Multiple Linear Regression
   1. Basic Ideas: the allbacks Book Weight Data
   2. The Interpretation of Model Coefficients
   3. Choosing the Model, and Checking It Out
   4. Robust Regression, Outliers, and Influence
   5. Assessment and Comparison of Regression Models
   6. Problems with Many Explanatory Variables
   7. Errors in x
   8. Multiple Regression Models - Additional Points
   9. Recap
   10. Further Reading
   11. Exercises
4. Exploiting the Linear Model Framework
   1. Levels of a Factor - Using Indicator Variables
   2. Block Designs and Balanced Incomplete Block Designs
   3. Fitting Multiple Lines
   4. Methods for Fitting Smooth Curves
   5. Quartile Regression
   6. Further Reading and Remarks
   7. Exercises
5. Generalized Linear Models, and Survival Analysis
   1. Generalized Linear Models
   2. Logistic Multiple Regression
   3. Logistic Models for Categorical Data - an Example
   4. Models for Counts - Poisson, Quasipoisson, and Negative Binomial
   5. Fitting Smooths
   6. Additional Notes on Generalized Linear Models
   7. Models with an Ordered Categorical or Categorical Response
   8. Survival Analysis
   9. Transformations for Proportions and Counts
   10. Further Reading
   11. Exercises
6. Time Series Models
   1. Time Series - Some Basic Ideas
   2. Regression Modeling with ARIMA Errors
   3. Nonlinear Time Series
   4. Further Reading
   5. Exercises
7. Multilevel Models, and Repeated Measures
   1. Corn Yield Data - Analysis Using aov()
   2. Analysis Using lefte4: :lmer 0
   3. Survey Data, with Clustering
   4. A Multilevel Experimental Design
   5. Within- and Between-Subject Effects
   6. A Mixed Model with a Betabinomial Error
   7. Observation-Level Random Effects - the moths Dataset
   8. Repeated Measures in Time
   9. Further Notes on Multilevel Models
   10. Recap
   11. Further Reading
   12. Exercises
8. Tree-Based Classification and Regression
   1. Tree-Based Methods - Uses and Basic Notions
   2. Splitting Criteria, with Illustrative Examples
   3. The Practicalities of Mee Construction - Two Examples
   4. From One Tree to a Forest - a More Global Optimality
   5. Additional Notes - One Tree, or Many Trees?
   6. Further Reading and Extensions
   7. Exercises
9. Multivariate Data Exploration and Discrimination
   1. Multivariate Exploratory Data Analysis
   2. Principal Component Scores in Regression
   3. Cluster Analysis
   4. Discriminant Analysis
   5. High-Dimensional Data - RNA-Seq Gene Expression
   6. High-Dimensional Data from Expression Arrays
   7. Balance and Matching - Causal Inference from Observational Data
   8. Multiple Imputation
   9. Further Reading
   10. Exercises
10. Epilogue
11. Appendix A The R System: a Brief Overview
    1. Getting Started with R
    2. R Data Structures
    3. Functions and Operators
    4. Calculations with Matrices, Arrays, Lists, and Data Frames
    5. Brief Notes on R Graphics Packages and Functions
    6. Plotting Characters, Symbols, Line Types, and Colors

Who is this course for?

1. Scientists wish to analyze their own data appropriately.
2. Learners who seek the connection between statistical theory and practical data handling.
3. Professionals willing to improve their analytical skills.

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