Shiny apps should satisfy the needs of middle managers. Now what about top management?
If you are a GitHub user or have simply been paying attention to the Web and developer landscapes the last 10 years, you'll know about the Markdown language for generating formatted documents in HTML and other tag-based markup languages. RStudio includes a Markdown implementation and extends it to include embedded R code chunks and both LaTeX and MathML in the R Markdown package. You can also create interactive R Markdown documents using Shiny and publish them to your own Shiny server or to shinyapps.io. For an example, see Figure 11.
The power of R
As we've seen, R is a useful tool for data scientists and statisticians, and its somewhat nonstandard scripting language will be of interest to programmers who might otherwise resort to Python (with NumPy, Pandas, and StatsModels); SQL (for data held in a database); or SAS (and its GUI derivative, JMP) for their data analysis. Compared to Excel, R has considerably more statistical and graphing power, especially if you add packages for your particular needs, and it's much more auditable. It's far easier to validate an R script than a spreadsheet full of formulas.
With the addition of RStudio as an IDE, developing R applications can be quite productive. RStudio Server allows companies to take advantage of the huge RAM and many processors available in big server hardware, Shiny turns R into a Web application server, and R Markdown allows you to use R for reports.
On the other hand, the great power of R and the large number of R packages available can make for a fairly intimidating learning curve. It helps a lot to have some statistics background when learning and using R, but that's true for all data science. As can be said for any other programming language with many libraries available, your best strategy for learning R is to take it one step at a time.
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