A narrative review with a step-by-step guide to R software for clinicians: Navigating medical data analysis in cancer research

Cancer causes immense suffering globally, and data constitute the cornerstone of cancer research. Analyzing data is pivotal, but manual analysis of vast datasets within constrained time frames is challenging and error-prone. Even minor inaccuracies can lead to false interpretations, affecting lives. This review explores the free, open-source, and widely acclaimed R software. Our goal was to facilitate data analysis and visualization in the scientific writing of clinical projects. R offers a wide range of features and packages for tasks like data manipulation, cleaning, analysis, and creating informative graphs, including traditional statistics, hypothesis testing, regression, time series, survival analysis, machine learning, and medical image analysis. These capabilities aid in accurate data analysis, facilitating a deeper understanding of cancer mechanisms and predicting outcomes. To prepare this review, we performed an online literature search in Scopus, PubMed, and Google for articles and books related to R software published between March 2012 and January 2024, using specific keywords such as “medical data analysis,” “RStudio,” “statistical software,” “clinical data management,” “R programming,” and “research tools.” Articles, books, and online sources lacking full-text options in English or complete information were excluded. A total of 66 articles and book chapters were retrieved, 22 were excluded, and 44 were included in this review. Through this article, our goal was to provide a user-friendly guide to employing R software for fundamental analysis with dummy data, making it accessible even to non-programmers. This will empower individuals to perform statistical analyses independently, contributing to cancer research with flexibility and accuracy.