Systematic Literature Review with R

In person 

Systematic literature reviews are powerful tools for bringing together existing research, especially in fields like art, design, and the social sciences. However, researchers in these areas often face challenges when managing large volumes of literature due to limited access to streamlined digital methods. 

This practical workshop offers a clear and accessible introduction to the systematic review process, with a special focus on creative and qualitative disciplines. Using R, an open-source programming language, participants will learn how to automate key steps in the workflow, from forming research questions and designing search strategies to retrieving data from academic databases and screening articles by title and abstract. 

The session is intended to be approachable and interactive, with live coding and practical activities that make the process easier to understand and apply. By the end of the workshop, participants will leave with a reusable R script for literature screening and the skills to begin a structured, efficient, and field-relevant review. Whether new to digital tools or looking to sharpen their research methods, this workshop offers a supportive environment to build confidence and competence in systematic reviewing. 

This course will be taught by Dandian Yang. 

After taking part in this event, you may decide that you need some further help in applying what you have learnt to your research. If so, you can book a Data Surgery meeting with one of our training fellows. 

More details about Data Surgeries. 

Those who have registered to take part will receive an email with full details on how to get ready for this course. 

If you’re new to this training event format, or to CDCS training events in general, read more on what to expect from CDCS training. Here you will also find details of our cancellation and no-show policy, which applies to this event. 

Level  

This workshop requires the following pre-knowledge:   

• Familiarity with working with R
• Familiarity with handling and wrangling data and installing packages 

Suggested Pre-reading

• Booth, A., Martyn-St James, M., Clowes, M., & Sutton, A. (2021). Systematic approaches to a successful literature review.
• Parums, D. V. (2021). Review Articles, Systematic Reviews, Meta-Analyses, and the Updated Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 Guidelines. Medical Science Monitor: International Medical Journal of Experimental and Clinical Research, 27, e934475-1. 

Learning Outcomes 

• Conceptual understanding of purpose and core principles of systematic literature reviews; differences between systematic, scoping, and narrative review approaches.
• Formulate clear and structured research questions using systematic frameworks
• Use R to automate key steps in the review workflow.
• Retrieve bibliographic data from academic databases and reference managers using R packages.
• Clean, merge, and organise literature datasets for screening and analysis.
• Apply R-based tools for de-duplication, title and abstract screening, and basic text mining. 

Skills  

• Develop clear, structured research questions for systematic reviews.
• Understand the differences between systematic, scoping, and narrative reviews.
• Use R to import, clean, and manage bibliographic data.
• Automate de-duplication, screening, and basic text analysis.
• Apply transparent and reproducible workflows for evidence synthesis. 

Explore More Training 

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• Getting Started with Python for Research
• Getting Started with R for Research

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