Data Analysis in Excel and R: A Comparative Evaluation

The proposed study is relevant since a comparative analysis of Excel and R data analyzers is necessary due to the fact that the development of large and complex data in various domains has escalated the demand for data analysis tools that can produce consistent, reliable, and reproducible data. Although Excel is still popular because of its accessibility and simplicity in learning, the available literature raises questions about the statistical accuracy of manually processing data and the high probability of error by the user in the spreadsheet-based analysis. It has also been revealed in research that spreadsheets have undiscovered errors more often and that people who use them have a tendency to be overconfident about the output, casting doubt on their suitability in utilizing Excel in higher levels of analysis. More rigorous data analysis R is an ideal programming environment with more complex statistical modeling and enhanced reproducibility in contrast to programming environments like R.Thus, this study will also determine whether Excel and R are effective in general data analysis tasks. The data utilized in the investigation comprises 5901 records and applies the following same procedures in either of the two tools: data cleaning, descriptive statistics, correlation analysis, regression modeling, and visualization. The findings indicate that Excel is only efficient and error-free to the extent of basic analysis and simple outputs but is inefficient, and errors are more likely to occur in cases where the tasks are more complex. This indicates that R is more useful in terms of analytical capacity and reliability, whereas Excel can be applied in introductory and simple data analysis.