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R For Clinical Researchers Raw Data To Reproducible Results
Published 8/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 687.35 MB | Duration: 2h 4m
Data wrangling, statistics, regression, survival analysis, and reproducible reports in R - for clinicians.
What you'll learn
Use R and RStudio to import, clean, and audit clinical research data with full reproducibility
Produce a publication-ready Table 1 and ggplot2 figures suitable for journals, abstracts, and posters
Run group comparisons, linear and logistic regression, and survival analysis (Kaplan-Meier and Cox)
Build a Quarto report that re-runs end-to-end on a fresh machine so your analysis is fully reproducible
Handle missing data, dates, and messy categorical variables without silent corruption of your dataset
Run a meta-analysis in R, including forest plots and heterogeneity assessment, for systematic reviews
Requirements
A laptop running macOS, Windows, or Linux. No prior R, coding, or advanced statistics knowledge is required - we start from a free install and build up from raw data.
Description
This course contains the use of artificial intelligence.If you are a clinician, surgical trainee, nurse, dentist, or PhD researcher who keeps outsourcing every analysis to a statistician, this course rebuilds your independence. You will learn R from a free install through to a fully reproducible report, using realistic, messy clinical-style datasets at every step.We start with R and RStudio, then move through tidyverse data cleaning, type-safe imports, and a six-point data-quality check that catches silent corruption before it touches your analysis. From there we build descriptive statistics and a publication-ready Table 1, then graduate to ggplot2 figures suitable for journals, conferences, and posters.The second half covers the methods that actually appear in clinical papers: group comparisons with proper effect sizes, linear and logistic regression with confounding control, Kaplan-Meier and Cox survival analysis, missing-data handling, multiplicity, and meta-analysis with forest plots. We finish with a Quarto capstone so your entire analysis re-runs end-to-end on a fresh machine, no more lost decimal places, mystery columns, or "the figures were right last week."Every lesson uses healthcare datasets (anonymised, fictional, and reproducible) and is taught by a UK surgical trainee and pancreatic cancer PhD student who has lived every one of the mistakes the course is designed to prevent. No prior R, no prior coding, and no prior advanced statistics required. By the end you will have a defensible analysis you can actually publish.
Doctors, surgical trainees, dentists, nurses, allied health professionals, public-health researchers, and PhD students in clinical fields who want to run their own analysis in R instead of waiting in a statistician's queue for every project.
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