Question

Describe the advantages of using R to perform basic statistical analysis, as compared to using Microsoft Excel's Data Analysis add-in Descriptive Statistics tool. Provide specific examples that justify the advantages you have described.

Answer 1

The advantages of using R to perform basic statistical analysis, as compared to using Microsoft Excel's Data Analysis add-in Descriptive Statistics tool are :-

1. R can handle very large datasets

Excel is limited in that there are only so many rows and columns per spreadsheet. So when you run out of rows/columns, you’re forced to move to a new tab or a new file. While it’s debatable that needing that many rows or columns of data is unlikely in most circumstances, there are cases where datasets grow over time and eventually the excel spreadsheet will not be able to contain all of that data.

Bottom line: The Excel spreadsheet is finite and this limits the datasets you can use.

2. R can automate and calculate much faster than Excel

Point 1 brings us to Point 2: I can’t tell you the number of times I’ve had a gigantic file crash because it contains up to 20 tabs chock-full of data, including a Pivot Table, a tab that contains over 6 years’ worth of pricing for 3,000+ products, and countless formulas throughout. Naturally, the file crashes due to the fact that Excel can handle a certain amount of data, but can barely function properly when you use it to capacity. This creates a serious problem when you start losing data because the file seems unable to save when you add any more data to it.

Bottom line: R is able to not only handle huge datasets but can still run efficiently while doing so.

3. R source code is reproducible

Research any number of R advocate blogs and you’ll find this point is a big one. R source codes can be used repeatedly and with very different datasets in ways that Excel formulas and VBA source codes cannot. There are statistical source codes available that can be applied to any dataset with only a few changes to code and reference data that can then be reapplied several times over very easily. While VBA can run virtually anything R can, it can be much more time consuming, and also limited similarly to Excel. R also has an advantage in that it shows the data and analysis separately, while Excel shows them together (data within formulas).This allows the user to view the data more clearly to correct any errors or see the progression of the data.

Bottom line: Reproducibility of R source code is much more advanced and easy to use than Excel or VBA.

4. Community libraries worth of R source code are available to all

R has been growing in usage and popularity over the past several years and with that, the number of users adding new functions to the available packages and libraries has also increased. This allows any R users access to not only basic statistical functions, but to an increasing number of complex new functions that may be applicable to their data. This creates a community of R users who are extending their knowledge easily to other R users who may require a similar solution to their data.

Bottom line: R promotes sharing of functions to expand libraries with new and different reproducible statistical functions.

5. R provides more complex and advanced data visualization

Excel can produce several types of basic graphs once you chop up and select the exact data you want to analyze. R is designed to much more easily produce graphs without all the pre-graph work, as well as provide more types of graphs than you’d ever know what to do with. Take a look here (http://shinyapps.stat.ubc.ca/r-graph-catalog/ ) to see the types of graphs R can create. Of course, Excel is perfectly sufficient when it comes to showing simple, straightforward data analysis, but R can take very complicated data and turn it into much easier to understand visual representation.

Bottom line: R can provide advanced data visualization for more complex datasets.