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Data insight foundationsstep-by-step data analysis with R /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data insight foundationsby Nikita Tkachenko.
Reminder of title:	step-by-step data analysis with R /
Author:	Tkachenko, Nikita.
Published:	Berkeley, CA :Apress :2025.
Description:	xxii, 227 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	R (Computer program language)
Online resource:	https://doi.org/10.1007/979-8-8688-0580-6
ISBN:	9798868805806$q(electronic bk.)

Data insight foundationsstep-by-step data analysis with R /
Tkachenko, Nikita.

Data insight foundationsstep-by-step data analysis with R /[electronic resource] :by Nikita Tkachenko. - Berkeley, CA :Apress :2025. - xxii, 227 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9798868805806$q(electronic bk.)

Standard No.: 10.1007/979-8-8688-0580-6doiSubjects--Topical Terms:

210846
R (Computer program language)

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

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505 0 $a 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.
520 $a 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.
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