Description

Book Synopsis

This book provides a practical, application-driven guide to using R for public health and health data science, accessible to both beginners and those with some coding experience. Each module starts with data as the driver of analysis before introducing and breaking down the programming concepts needed to tackle the analysis in a step-by-step manner. This book aims to equip readers by offering a practical and approachable programming guide tailored to those in health-related fields. Going beyond simple R examples, the programming principles and skills developed will give readers the ability to apply R skills to their own research needs. Practical case studies in public health are provided throughout to reinforce learning.

Topics include data structures in R, exploratory analysis, distributions, hypothesis testing, regression analysis, and larger scale programming with functions and control flows. The presentation focuses on implementation with R and assumes readers have had an

Mastering Health Data Science Using R

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    A Paperback by Alice Paul

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      Book details

      Publisher CRC Press
      Published 7 February 2025
      ISBN-13 9781032729930
      978-1032729930
      ISBN-10 1032729937
      Also in:
      Epidemiology and Medical statistics

      Description

      Book Synopsis

      This book provides a practical, application-driven guide to using R for public health and health data science, accessible to both beginners and those with some coding experience. Each module starts with data as the driver of analysis before introducing and breaking down the programming concepts needed to tackle the analysis in a step-by-step manner. This book aims to equip readers by offering a practical and approachable programming guide tailored to those in health-related fields. Going beyond simple R examples, the programming principles and skills developed will give readers the ability to apply R skills to their own research needs. Practical case studies in public health are provided throughout to reinforce learning.

      Topics include data structures in R, exploratory analysis, distributions, hypothesis testing, regression analysis, and larger scale programming with functions and control flows. The presentation focuses on implementation with R and assumes readers have had an

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