Data collection, analysis, and interpretation are integral components of health research—the majority of literature focus on the same. However, quality analysis requires quality data. Unfortunately, routine academic teaching, training, and research emphasize study designs and instruments to collect good data before entry. Many applied researchers who don't analyze data believe that a computer quickly analyses the data–a fact. Thus, many applied researchers demand immediate analysis results from statistical collaborators. However, data cleaning consumes a significant chunk of time in any analysis as the same is riddled with errors and inconsistencies despite taking adequate precautions. Data scientists know that data cleaning is a laborious and time-consuming process. The data analysis tools are always shiny and new; the data cleaning process is ancient and unchanging. There are general and software-specific guidelines to be followed as different software have different interfaces and capabilities. The researcher needs to understand the general and particular approaches to exploit the vast potential of Rcmdr. And to utilize the enormous potential of Rcmdr, we need to know the data entry, cleaning, and export options of the same. Statistical software is like a fancy gadget whose output quality depends on input quality–data entry. Therefore, in this article, researchers will be learning: • How to enter data in Rcmdr? • How to clean and code data in Rcmdr? • How and what data formats Rcmdr can import? • How and what data formats Rcmdr can export?