A Comprehensive Guide to R Programming for Data Analytics

Erscheinungsjahr 2026
ISBN: 978-0-443-45458-5
Verlag: Elsevier Science

Buch, Englisch, 250 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 449 g

A Comprehensive Guide to R Programming for Data Analytics provides a comprehensive presentation of univariate and multivariate statistical models within the general linear model and generalized linear model framework to analyze simple and complex data using R software. This book presents popular R packages that are used in data mining (e.g., caret-classification and regression, lubridate-dates and times, string-R for string data) and visualization (e.g., ggplot, ggthemes, ggtext). The R packages used to analyze data using a particular statistical model are explained through real-world and publicly available datasets. R codes are presented in a manner that helps readers understand the program code syntax.

Examples of real-world data sets from a variety of academic disciplines are provided so that a wide audience can learn R programming to analyze data in their research. The book provides tips, recommendations, and strategies to troubleshoot common issues in R syntax, as well as definitions of key terms. Checkpoints are included to recap the concepts learned in each chapter. The book helps readers enhance their conceptual understanding and practical application of statistical models to real-world datasets, and enables readers to gain competency in R programming, which is an important skill in today’s data-driven market.

Autoren/Hrsg.

Acharya, Parul

Fachgebiete

• Mathematik | Informatik Mathematik Mathematik Allgemein Diskrete Mathematik, Kombinatorik
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmier- und Skriptsprachen
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Informationstheorie, Kodierungstheorie
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
• Mathematik | Informatik Mathematik Mathematik Allgemein Mathematische Logik
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsarchitektur

Inhaltsverzeichnis

1. Introduction to the R Platform

2. Descriptive Analysis and Data Visualization

3. Data Cleaning and Missing Data Analysis

4. T-Tests (Independent Sample, Paired Sample)

5. Analysis of Variance (ANOVA) Models (Univariate and Multivariate)

6. Categorical Data Analysis

7. Correlation & Linear Regression Models

8. Non-Linear Regression Models (Logistic, Poisson, Log-linear, Polynomial)

9. Discriminant Analysis & Canonical Correlation

10. Exploratory and Confirmatory Factor Analysis (Data Validity)

11. Reliability Analysis (Data Consistency)

12. Structural Equation Modeling (Causation Within Constructs)

13. Hierarchical Linear Modeling (Clustered Data)

14. Growth-Curve Modeling (Longitudinal Data)

15. Propensity Score Matching (Causation Under Non-Randomization)

16. Bayesian Survival Analysis

17. Time-Series Analysis (Longitudinal Data With Autocorrelation)

18. Big Data Analysis (Decision Trees, Random Forests, K-Nearest Neighbors, Support Vector Machine)

Über Autor(innen)

Acharya, Parul
Dr. Parul Acharya holds a Ph.D. in Educational Statistics with emphasis on research methods, psychometrics, data analysis, and program evaluation from the University of Central Florida. She has a multi-disciplinary academic background with degrees in Health Science, Kinesiology, Business Administration, Logistics/Supply Chain Management, Educational Statistics, and Instructional Technology. She is currently working as an Associate Professor at Columbus State University, Columbus, Georgia in the College of Education and Health Professions. She teaches graduate-level courses in research methods, statistics, data analytics, psychometrics, and program evaluation. She has served as a Chairperson and/or Statistician in 32 completed doctoral dissertations. She has published 30 peer-reviewed publications. Parul has worked as a Principal Evaluator on research projects for the National Science Foundation (NSF) and the US Department of Education (USDOE). She regularly works on NSF and USDOE review panels as a subject matter expert of assessment and program evaluation. Parul currently holds leadership positions within the special interest groups and Divisions of American Educational Research Association (AERA). Parul has published a book on research methods, and data analysis. The book is currently used in graduate level (Masters and doctorate) courses in research methodology, statistics, and data analysis (SPSS). Her research interests include: Technological issues with online teaching and student learning; STEM/STEAM-based intervention studies; Perceived Behavioral Interventions and Supports (PBIS); Individual and Contextual factors that influence productive (e.g., organizational citizenship behaviors) and counter-productive behaviors (e.g., aggression) at work, Pre-K assessment issues; Program Evaluation; Psychometrics (Scale Development and Validation); State-based assessment scores (e.g., Milestone scores, CCRPI scores, growth percentile scores). In the past, Parul has worked in the Accountability, Assessment and Research Department in large school districts within the state of Florida as a Data and Program Evaluation Analyst.