Getting Started with Ch 1 R
1. R useful for clinical research and big data use
2 Installing R and RStudio
3 RStudio Configuration and Preferences
Understanding the 4 R Essential Concepts
5 Data Indexing and Slicing
6 Download and save example data
7 packages
8 R I can do it too!

Chapter 2: Getting Started with Data Analysis
1. Getting started with a project
2 Loading data
3. Browse the data
4 Data indexing and slicing
Handling 5 factors
6 Descriptive Statistics
7 Data Correction and Missing Values
8 apply function
9 if, for functions
10 Handling duplicate results
11 Creating Table 1

Chapter 3 Data Handling
1 tidyverse
2 select
3 filters
4 mutate
5 arrange
6 summarise
7 group_by

Chapter 4 Splitting and Merging Data
1 merge
2 Merge using tidyverse
3 tidy data

Chapter 5 Data Visualization
1 R base graph
2 ggplot2 package
3 Various options of ggplot2
4 ggpubr package
5. Package for plotting correlations

Chapter 6: Medical Statistics Related to Clinical Research
1 Regression analysis
2 Generalized linear analysis
3 ROC-related analyses
4 Survival Analysis
5 Time-dependent Cox proportional hazards model

Chapter 7: Study Follow-Up 1: Aspirin and Hepatobiliary Cancer
1 Research Background and Overview
2 Analysis Plan
3 Data Analysis
4 Univariate analysis
5 Multivariate analysis
6 Presentation of results

Chapter 8: Study Follow-Up 2: Biomarkers and Liver Cancer
1 Research Background and Overview
2 Analysis Plan
3 Data Analysis
4 ROC curves
5 Cut-Off

Chapter 9 Study Follow-Up 3: ALT Normalization and Liver Cancer Risk
1 Research Background and Overview
2 Analysis Plan
3 Data Preprocessing and Exploration
4 Data Analysis: Summary of Baseline Characteristics
5 Data Analysis: Liver Cancer Incidence
6 Data Analysis: ALT Normalization
7 Data Analysis: Finding Risk Factors for Liver Cancer
8 Data Analysis: Subgroup Analysis
Present in one table

The utility of the R program as a data analysis tool is truly enormous.
It allows you to work quickly by avoiding repetitive tasks, provides reliable analysis results due to excellent reproducibility, and allows you to implement various graphs with excellent visualization functions.
You can also connect with countless others online, share code, and create your own functions to expand your analytical skills.
The author, a practicing physician, says that while analyzing clinical research data, he felt the limitations of SPSS, which he had previously used as an analysis tool, and that he later encountered R through the Internet, which opened up a new world of data analysis.
Above all, it is said that work speed has greatly improved and it has been a great help in creating efficient work habits.
This is why I wrote this book to share the benefits of R data analysis that I have experienced with many people.

Of course, unlike SPSS, which has a GUI, R requires you to input code line by line and become familiar with functions and commands to perform analysis.
R has a gentle learning curve, so it takes a relatively long time to become proficient at using it.
This book is a compilation of the core concepts of R analysis that the author, who knows better than anyone the difficulties of R data analysis, has learned through trial and error.
The book consists of a total of nine chapters.
Chapters 1 through 5 cover in detail how to handle and visualize basic data using R.
Chapter 6 covers medical statistics knowledge for analyzing clinical research data.
This article explains statistical analysis methods (regression analysis, generalized linear analysis, ROC-related analysis, survival analysis, etc.) that are widely used in clinical research and paper writing.
Chapters 7 through 9 systematically cover the entire process of establishing an analysis plan based on actually published clinical studies, understanding/preprocessing the data structure, and then selecting an appropriate analysis method to perform the analysis.

The goal of this book is to guide readers to learn R more effectively and quickly and to apply it in practice.
Accordingly, we reduced the basic theoretical content, introduced useful functions of R that can provide practical help to researchers, and performed practical training using clinical data that reflects reality as much as possible.
In addition, we tried to help readers understand the various functions and grammar of R by including explanations and examples as much as possible during the practice.

Key Features
ㆍThe outstanding utility of the R program as a data analysis tool (fast working speed, excellent reproducibility,
A book containing excellent visualizations, etc.
ㆍA book that systematically organizes the core content of R data analysis, reducing theoretical content.
ㆍClinical medicine researchers, healthcare researchers, easier and faster R data analysis
A must-read for researchers looking to get started!
ㆍR data analysis book specialized for clinical research data analysis
ㆍPractical analysis report utilizing abundant clinical data