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

    Data Insight Foundations by Tkachenko, Nikita;

    Step-by-Step Data Analysis with R

      • GET 8% OFF

      • The discount is only available for 'Alert of Favourite Topics' newsletter recipients.
      • Publisher's listprice EUR 42.79
      • The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.

        18 151 Ft (17 287 Ft + 5% VAT)
      • Discount 8% (cc. 1 452 Ft off)
      • Discounted price 16 699 Ft (15 904 Ft + 5% VAT)

    18 151 Ft

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    Availability

    Not yet published.

    Why don't you give exact delivery time?

    Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.

    Product details:

    • Edition number First Edition
    • Publisher Apress
    • Date of Publication 1 April 2025
    • Number of Volumes 1 pieces, Book

    • ISBN 9798868805790
    • Binding Paperback
    • No. of pages227 pages
    • Size 254x178 mm
    • Language English
    • Illustrations 37 Illustrations, black & white; 72 Illustrations, color
    • 700

    Categories

    Short description:

    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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    Long 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. 

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    Table of 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.

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