A Comprehensive Guide to R Programming for Data Analytics
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Product details:
- Publisher Elsevier Science
- Date of Publication 1 November 2026
- ISBN 9780443454585
- Binding Paperback
- No. of pages250 pages
- Size 235x191 mm
- Weight 450 g
- Language English 700
Categories
Long description:
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.
Table of Contents:
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)
