There is a huge difference between R (and Matlab, SAS, or other statistical languages) and relatively low-level languages such as C/C++/C#/Java in exactly this regard. The latter category is used more often for stable end-products, where speed and performance can be crucial, whereas the former category is used more often for model testing and prototyping.
The statistical languages have many basic features, including those you mention, basic statistics and data viewing, as well as much else that will make prototyping a much easier and quicker process. Some of the features I use most in may day-to-day model development and backtesting are:
- Optimization (importance of good optimization algos is not to be underestimated!)
- Linear algebra (eigenvalues, singular value decomposition)
- Interpolation (cubic splines)
- Filtering (FIR, IIR, EMA)
- Read/write to CSV/Excel/databases/other formats
- Advanced graphics (bar charts, histograms, box plots, scatter, 3-D)
- Date/time manipulation and time-series support
- Vector/matrix manipulation (data manipulation)
- Large library of less-commonly used algorithms (e.g. Expectation-Maximization) available as packages
I'm sure many of these features could also be found in low-level languages, but the level of tight integration makes them much easier to use in the statistical languages.