Introduction
Data analytics are an increasingly important component of decision making in any business. Counting words, material objects, and data can lead to a better understanding of the sources and the subject under study, allowing businesses to make more precise and calculated decisions.
R and Excel are two exemplary programs of such process.
Excel offers an easy intuitive way to process things like arithmetic and tabulations, while R’s programmatic focus introduces concepts like data structures, iteration, and functions.
Either program - when used at its maximum - will prove to be powerful, well beyond the ability of the average man.
R
- programming language
- manipulates and organizes poorly structured data
- computes regressions, clustering, text mining and time series analysis
- readily shareable in a web-based interactive format
- unique and impressive visuals
- free and open source
Excel
- intuitive UI
- easy to use
- quick for one-time analysis
- excels at handling huge sets of simple data sets
- basic visuals
- paid software
Conclusion
Both R and Excel are great tools to analyze data.
Excel is already well known and used by many institutions, while R is relatively new, with more advanced and powerful functions. When choosing between the two platforms, it is important to first understand the scope of the project to select the most efficient platform for the work in question.
In regards to which platform is best, it is really dependent on the project, but if you simply want to look at which platform is more powerful, it is without a doubt:
