Data Insight Foundations : Step-by-Step Data Analysis with R / by Nikita Tkachenko.

Format:

Book

Author/Creator:

Tkachenko, Nikita., Author.

Series:

Professional and Applied Computing Series

Language:

English

Subjects (All):

R (Computer program language).

Data mining.

Electronic data processing.

Physical Description:

1 online resource (XXII, 227 p. 109 illus., 72 illus. in color.)

Edition:

1st ed. 2025.

Place of Publication:

Berkeley, CA : Apress : Imprint: Apress, 2025.

Summary:

This book is not a comprehensive guide; if that's what you're seeking, you may want to look elsewhere. Instead, it serves as a map, outlining the necessary tools and topics for your research journey. The goal is to build your intuition and provide pointers for where to find more detailed information. The chapters are deliberately concise and to the point, aiming to expose and enlighten rather than bore you. While examples are primarily in R, a basic understanding of the language is advantageous but not essential. Several chapters, especially those focusing on theory, require no programming knowledge at all. Parts of this book have proven useful to a diverse audience, including web developers, mathematicians, data analysts, and economists, making the material beneficial regardless of one’s background The structure allows for flexible reading paths; you may explore the chapters in sequence for a systematic learning experience or navigate directly to the topics most relevant to you. What You Will Learn Data Management: Master the end-to-end process of data collection, processing, validation, and imputation using R Reproducible Research: Understand fundamental theories and achieve transparency with literate programming, renv, and Git Academic Writing: Conduct scientific literature reviews and write structured papers and reports with Quarto Survey Design: Design well-structured surveys and manage data collection effectively Data Visualization: Understand data visualization theory and create well-designed and captivating graphics using ggplot2.

Contents:

Part I: Working with Data

Chapter 1. Data Manipulation

Chapter 2: Tidy Data

Chapter 3: Relational Data

Chapter 4: Data Validation

Chapter 5: Imputation

Part II: Reproducile Research

Chapter 6: Reproducible Research

Chapter 7: Reproducible Environment

Chapter 8: Introduction to Command Line

Chapter 9: Version Control with Git and Github

Chapter 10: Style and Lint your Code

Chapter 11: Modular Code

Part III: Lit Review and Writing

Chapter 12: Literature Review

Chapter 13: Write

Chapter 14: Layout and References

Chapter 15: Collaboration and Templating

Part IV: Collecting the Data

Chapter 16: Total Survey Error (TSE)

Chapter 17: Document

Chapter 18: APIs

Part V: Presenting the Data

Chapter 19: Data Visualization Fundamentals

Chapter 20: Data Visualization

Chapter 21: A Graph for the Job

Chapter 22: Color Data

Chapter 23: Make Tables Part VI: Back Matter

Epilogue.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9798868805806

OCLC:

1513398001