Data Insight Foundations : Step-by-Step Data Analysis with R

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¥7,645

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Data Insight Foundations : Step-by-Step Data Analysis with R

Tkachenko, Nikita

ウェブストア価格 ¥8,549（本体¥7,772）
APress（2025/04発売）
外貨定価 US$ 42.99
ポイント 77pt

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製本 Paperback:紙装版/ペーパーバック版／ページ数 227 p.
言語 ENG
商品コード 9798868805790

Full Description

This book is an essential guide designed to equip you with the vital tools and knowledge needed to excel in data science. Master the end-to-end process of data collection, processing, validation, and imputation using R, and understand fundamental theories to achieve transparency with literate programming, renv, and Git--and much more. Each chapter is concise and focused, rendering complex topics accessible and easy to understand.

Data Insight Foundations caters to a diverse audience, including web developers, mathematicians, data analysts, and economists, and its flexible structure allows enables you to explore chapters in sequence or navigate directly to the topics most relevant to you.

While examples are primarily in R, a basic understanding of the language is advantageous but not essential. Many chapters, especially those focusing on theory, require no programming knowledge at all. Dive in and discover how to manipulate data, ensure reproducibility, conduct thorough literature reviews, collect data effectively, and present your findings with clarity.

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.

Who this Book is For

Career professionals such as research and data analysts transitioning from academia to a professional setting where production quality significantly impacts career progression. Some familiarity with data analytics processes and an interest in learning R or Python are ideal.

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.