Logistic regression analysis in R for manuscript writing

Name: Logistic regression analysis in R for manuscript writing
Rating: 4.7 (3 reviews)

Learn descriptive analysis, logistic regression, Poisson regression, linear regression, and manuscript-style result inte

New

Created byMd Ahshanul Haque

Last updated 5/2026

English

What you'll learn

Run logistic regression in R to assess the association between an exposure and a binary outcome.
Interpret odds ratios, 95% confidence intervals, and p-values in simple research language.
Prepare unadjusted and adjusted logistic regression models using RStudio.
Create publication-ready regression tables and write results for manuscript-style reporting.

Course content

7 sections • 33 lectures • 1h 36m total length

Welcome and Course Overview2:45
In this lecture, students will get an overview of the course and understand what they will learn throughout the program. The lecture introduces R, RStudio, and the main topics covered in the course, including data analysis, visualization, biostatistics, regression, and machine learning applications for health and research data.
Opening RStudio | new script for writing R code2:36
In this lecture, students will learn how to open RStudio, understand the main RStudio panels, create a new R script, and prepare the workspace for writing and running R code.
Installing and Loading R Packages2:42

Sample Excel data | Please download the sample Excel dataset2:04
Please download the sample Excel dataset before starting the analysis. This file will be used throughout the course for data import, preparation, summary tables, and logistic regression analysis.
Importing Excel Data into R1:28
Learn how to import an Excel dataset into RStudio using the read_excel() function and prepare it for analysis.
Selecting Variables for Analysis3:03
Learn how to select only the important variables needed for the analysis using simple R code.
Dropping Unnecessary Variables (Optional)1:34
Learn how to remove unnecessary variables from the dataset to keep the analysis clean and focused.
Defining Factor Variables for categorical variable2:52
Learn how to convert categorical variables into factors so they can be used correctly in summary tables and regression analysis.
Adding Variable Labels2:40
Learn how to add clear variable labels to make the dataset and output tables easier to understand.

Univariate Summary Table | gtsummary in R-Studio2:00
Learn how to create a basic summary table for all selected variables in RStudio.
Bivariate Summary Table | Cross tab1:43
Learn how to compare participant characteristics by sugar consumption or another grouping variable.
Formatting Mean ± SD and n (%)3:45
Learn how to format continuous and categorical variables using mean ± SD and n (%)
Setting Decimal Places2:12
Learn how to control decimal places for summary statistics and make the table cleaner
Adding p-values | Inferential analysis | Chi-Square and t-test4:08
Learn how to add p-values to compare groups and support inferential analysis
Exporting the Table to Microsoft Word2:58
Learn how to save and export the final table as a Microsoft Word file for reporting or manuscript writing

Creating a Basic Boxplot3:22
Learn how to create a simple boxplot in RStudio using continuous and categorical variables.
Customizing Boxplot Properties1:25
Learn how to customize boxplot color, outline, width, outliers, and other visual properties.
Adding Axis Labels and legend2:38
Learn how to add clear axis labels, plot titles, and legend labels for better interpretation.
Saving and Exporting the Boxplot2:48
Learn how to save and export the final boxplot for reports, presentations, or manuscripts.

Understanding Objective 1: Association Analysis1:16
Understand the research objective, study design, outcome variable, exposure variable, and why logistic regression is appropriate.
Running Simple Logistic Regression in R | Estimating the Odds Ratio2:54
Run a simple logistic regression model to assess the crude association between sugar consumption and heart disease.
Interpreting Logistic Regression Results, Odds Ratio1:40
Interpret odds ratio, 95% confidence interval, and p-value in simple research language.
Running Multiple Logistic Regression in R3:41
Run an adjusted logistic regression model by including potential covariates in the analysis
Preparing an Unadjusted Odds Ratio Table | Publication-ready table8:10
Description: Create a publication-ready table for unadjusted odds ratios with confidence intervals and p-values.
Preparing the Final Publication-Ready Table4:03
Description: Finalize a clean regression table suitable for reporting research findings.
Exporting the Final Regression Table to Word1:59
Write the logistic regression findings clearly for the results section of a manuscript.

Preparing the Word File for Results Writing2:08
Learn how to set up the Microsoft Word file before writing the result section.
Writing General Characteristics Results3:38
Learn how to describe Table 1 using simple manuscript-style language.
Describing the Outcome Variable by Diagram1:58
Learn how to describe the figure showing heart disease by sugar consumption and income level.
Writing Logistic Regression Results2:50
Learn how to write adjusted logistic regression findings using odds ratios, 95% confidence intervals, and p-values.

Requirements

Basic computer skills and an interest in learning regression analysis in R. No advanced statistics or programming experience is required.

Description

This short hands-on course is designed for students, researchers, and beginners who want to learn practical regression analysis in R for manuscript-style reporting. Instead of memorizing theory, you will learn by doing a real data analysis project step by step in RStudio.

You will start with the basics, including opening RStudio, loading R packages, importing Excel data, selecting variables, dropping unnecessary variables, defining factor variables, and adding clear variable labels. Then, you will create descriptive and inferential summary tables, format mean ± SD and n (%), set decimal places, add p-values, finalize tables, and export results to Microsoft Word.

The course is organized around three practical research objectives. First, you will assess the association between sugar consumption and heart disease using logistic regression and odds ratio. Second, you will estimate the effect of sugar consumption on heart disease using Poisson regression and risk ratio. Third, you will assess the effect of sugar consumption on BMI using linear regression and mean difference.

By the end of this course, you will be able to prepare data, run regression models, create publication-ready tables, interpret odds ratios, risk ratios, regression coefficients, confidence intervals, and p-values, and write clear results for manuscript-style reporting. This course is simple, practical, and focused on real research data analysis skills.

Who this course is for:

Students, researchers, and beginners who want to learn logistic regression in R and prepare regression results for manuscript-style reporting.

Logistic regression analysis in R for manuscript writing

What you'll learn

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Course content

Getting Started with R and RStudio3 lectures • 8min

Data Import and Preparation for analysis in R-studio6 lectures • 14min

Data Summary in RStudio: Descriptive and Inferential Analysis6 lectures • 17min

Boxplot Visualization in RStudio4 lectures • 10min

Bar Chart Visualization in RStudio3 lectures • 13min

Objective: Association Analysis Using Logistic Regression | odds ratio7 lectures • 24min

Writing Results for the Manuscript4 lectures • 11min

Requirements

Description

Who this course is for: