When working in data analysis and with big datasets, it is vital to determine the tools that would be best for each job. For this article, we will discuss both R and Excel, two major software packages vital to data analysis.

For most surface-level data analysis, Excel is the easier option to use. Most people learn the surface-level Excel functions in their daily lives, and it is built to be as intuitive as possible. Additionally, the inbuilt visualization options are very in-depth and interactable, making formatting graphs for the American Psychological Association and other publications much easier than with other programs. It is entirely possible to clean, format, and visualize data entirely in Excel.

However, for large data sets and more complicated data analysis techniques, it is better to use R. R is much more powerful, capable of running machine learning (ML) algorithms, regressions with multiple variables, ANOVA, and visualization of thousands of data points. However, R has a steeper learning curve, being much closer to a programming language than a spreadsheet program.

Our data analysts at Data in Plain English are proficient in both Excel and R, giving you the most options possible for your data analysis at whatever level you need.