Data Insight Foundations

This book serves as your foundational guide to mastering data analysis with R. From setting up R and RStudio to diving deep into advanced data manipulation, validation, and visualization techniques, the book lays out a structured path for readers of all levels. Beginning with basic R operations, the book explores the power of the Tidyverse for efficient data management. It clarifies the principles of tidy data and its significance in organizing information for analysis, setting the stage for more complex concepts.

Relational data is covered along with the pivotal role of data validation in ensuring data integrity. You will learn how to address missing values and imputation strategies. The book underscores the importance of reproducible research, presenting tools and practices such as renv to achieve consistency across environments. Version control with git and GitHub introduces you to collaborative and change management practices, while chapters on coding practices advocate for clarity and maintainability.

A dive into literature review processes, text editing technologies, and the integration of code into documents with Quarto and knitr walks you through the process of document creation. Collaboration is facilitated through trackdown, merging the simplicity of Google Docs with the complexity of data analysis tasks. The journey through formatting requirements, survey design, API usage, and data visualization equips you with the skills to conduct thorough research and present findings effectively. This book serves as a gateway to the vast possibilities within data analysis with R, aiming to inspire confidence and competence as you take on your own data analysis projects.

What You Will Learn

• Understand fundamental theories underpinning reproducible data analysis
• Master the end-to-end process of insights creation: data collection, processing, analysis, visualization and reporting
• Program in R for each stage of the analysis journey
• Write clean, efficient code to process data for analysis
• Conduct scientific data collection through well-designed surveys
• Generate captivating interactive and static reports

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.