R Programming For Meta-Analysis
Published 7/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 3h 11m | Size: 3.4 GB
Learn R from scratch for systematic reviews and meta-analysis: tidyverse, meta, metafor, and reproducible Quarto
What you'll learn
Set up R and RStudio, or run R in the browser with Posit Cloud, and manage reproducible projects with renv
Write clean R: variables, atomic types, vectors, data structures, and special values (NA, NaN, NULL, Inf)
Import, clean, and reshape messy research data with here, readr, dplyr, and tidyr
Visualise health and research data with ggplot2 and build reproducible reports with Quarto
Run a full systematic review and meta-analysis in R: forest and funnel plots, heterogeneity, meta-regression, robvis, PRISMA, NMA
Requirements
No prior R or programming experience required; we start from installation
A computer with internet access (Windows, Mac, or Linux), or just a web browser via Posit Cloud
Description
This course contains the use of artificial intelligence.
This course teaches you R from the very beginning and takes you all the way to running a complete systematic review and meta-analysis in health research, with no prior programming experience required.
You will start by setting up R and RStudio, or running R entirely in your browser with Posit Cloud, and learning to organise your work in reproducible projects with renv. From there you will build solid R foundations: variables, vectors, data structures, and how to handle tricky special values such as NA, NaN, NULL, and Inf.
Next you will learn the tidyverse workflow that working analysts use every day. You will import messy research data, clean and reshape it with dplyr and tidyr, and visualise it with ggplot2. You will also produce fully reproducible reports and manuscripts with Quarto.
The heart of the course is a complete evidence synthesis pipeline in R. You will search the literature with litsearchr, deduplicate with synthesisr, screen with revtools, calculate effect sizes with esc and metafor, and run pairwise meta-analysis with the meta package. You will create publication-ready forest and funnel plots, assess heterogeneity, run meta-regression, build risk-of-bias plots with robvis, and draw PRISMA 2020 flow diagrams.
Finally you will explore advanced methods, including publication bias and influence diagnostics, network meta-analysis with netmeta, and a Bayesian network meta-analysis preview, before completing a capstone where you replicate a published meta-analysis and write a reproducible Quarto manuscript with renv.
By the end of this course, you will be able to run a reproducible systematic review and meta-analysis in R with confidence, and produce figures and reports that are ready for publication.
Who this course is for
Health researchers, clinicians, and students who want to run systematic reviews and meta-analyses in R
Anyone new to R, or researchers moving from Excel, RevMan, or SPSS to reproducible, code-based evidence synthesis
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