Main description:

This book provides a complete practical guide of processing data in public health with R language. On the basis of the author's research and teaching experiences, this book serves either as a textbook for undergraduates and graduates in public health or as a tutorial for self-learning. Many first-hand examples are presented with source data, R scripts, and graphs, as well as detailed explanations, which could be easily reproduced by readers so as to better understand the data processing principles and procedures. Popular and novel R packages in public health are introduced as well.

Contents:

1 Preparing tools1.1 Learning Objectives1.2 Tools 1.3 Set up R 1.3.1 R 1.3.2 RStudio 1.4 Programming basics 1.5 R packages 1.5.1 Installation 1.5.2 Packages in public health 1.5.3 Datasets in packages 1.6 R Help 1.6.1 Built-in documents 1.6.2 Examples and demonstrations 1.6.3 Asking questions 1.7 Base R, data.table, and tidyverse 1.8 Exercises
2 Planning data 2.1 Learning Objectives 2.2 Research design 2.3 Literature review 2.3.1 Relevant R packages 2.3.2 The bibliometrix package 2.3.3 The scholar package 2.4 Establish a data plan 2.4.1 Workflow 2.4.2 Mind maps 2.4.3 Gannt charts 2.5 Exercises
3 Collecting data 3.1 Learning Objectives 3.2 Sampling data 3.2.1 Probability sampling 3.2.2 Non-probability sampling 3.3 Recording data 3.3.1 Surveys and questionnaires

4 CONTENTS
3.3.2 Desktop questionnaire platform 3.3.3 Online questionnaire platform 3.3.4 Databases and tables 3.3.5 Using secondary data 3.4 Exercises
4 Importing and exporting data 4.1 Learning Objectives 4.2 Import data manually 4.3 Using RStudio dialogues 4.4 Comma/tab separated values 4.5 Default formats in R 4.6 Other software dependent data files 4.7 Online sources 4.8 Data shipped with R packages 4.9 Exercises
5 Cleaning data 5.1 Learning Objectives 5.2 Introduction 5.3 File information 5.4 Dimensions and structure 5.5 Mislabelled variables 5.6 Incorrect data types 5.7 Text inconsistencies 5.8 Anomalies 5.9 Missing values 5.10 Other operations 5.11 Exercises
6 Describing data 6.1 Learning Objective 6.2 Categorical data 6.2.1 Contingency tables 6.2.2 Marginal statistics 6.3 Numerical data 6.3.1 Central tendency 6.3.2 Spread 6.3.3 Distribution shape 6.3.4 Extended summaries 6.4 Grouped summary 6.5 Visualization 6.6 Exercise
7 Analyzing data 7.1 Learning Objective 7.2 Probability distribution functions in R 7.3 Hypothesis tests 7.3.1 R functions and common steps 7.3.2 Student's t-test

CONTENTS 5
7.3.3 2-test 7.3.4 Analysis of Variance (ANOVA)7.4 Regressions 7.4.1 Common regression models 7.4.2 Linear regression model 7.4.3 Logistic regression model7.4.4 Cox Regression Model 7.5 Exercises
8 Visualizing data 8.1 Learning Objectives 8.2 Introduction 8.3 Plotting systems in R 8.3.1 Base R 8.3.2 ggplot2 8.3.3 plotly 8.3.4 Other systems 8.4 Pie charts 8.5 Bar charts 8.5.1 For one-way frequency tables 8.5.2 For two-way contingency tables 8.5.3 For multiple-way contingency tables 8.6 Dot charts and strip charts 8.7 Histograms, box plots, and violin charts 8.8 Scatterplots 8.8.1 For two variables 8.8.2 For pairwise variables 8.8.3 For multiple variables 8.8.4 Line charts and area charts 8.9 Export graphs 8.10 Exercises
9 Presenting data 9.1 Learning Objectives 9.2 Channels 9.3 General principles 9.4 R Markdown 9.4.1 Set up R Markdown 9.4.2 Structure of an R Markdown document 9.5 Presenting data via statements 9.5.1 Summaries 9.5.2 Hypothesis tests 9.5.3 Regressions9.6 Presenting data via tables 9.7 Presenting data via graphs 9.8 Integration of statements, tables, and graphs
10 Managing data 10.1 Learning Objectives 10.2 Introduction

6 CONTENTS
10.3 Data Management Framework 10.3.1 The prodigenr package 10.3.2 The rosr package 10.4 Raw data 10.5 Metadata10.5.1 Introduction 10.5.2 Metadata in variable names10.5.3 Metadata in labels 10.5.4 Metadata in headers or independent files 10.5.5 Metadata in R packages 10.6 Scripts 10.7 Version control 10.8 Exercises