I am not a fan of MS Windows software. However, the reality is that majority of people use it and it is often easier for me to live with this than to convert the sorounding souls. Additionally I am find ing out lately that majority of my favourite open-source programs work on all major platforms: MS Windows, Mac, and UNIX/Linux. Then there is no problem for me to work on any of these systems. MS Excel tool is similarly very widely used by mases, but not really powerfull when it comes to data analysis. However, we must admit that it is jolly usefull for handling medium sized data and that majority of people perform analyses with Excel. For those that can not live without Excel and have some working knowledge of R I suggest to take a look at the RExcel and its demo video (28 min!). It seems to be very nicely integrated - Excel keeps tracks of all the dependencies, while R takes care of the computations. Of course you can do the same stuff in R, by reruning the script for each change, but the interactivity offered by the Excel has its own merit. Regarding the quality of graphics it is obvious that Excel plots can not match with R. But if you need some interactivity (imagine you would like to study the effect of a particular parameter on distribution density), then Excel plots are good enough.
smoothScatter function is now available also in base R - in the recommended package graphics that is shipped with R. Originally this function was in the genepplotter package from Bioconductor. I really like it since it can nicely plot large datasets. Bellow is an example figure (also available at R Graph Gallery). Simply start R and type ?smoothScatter to get familiar with this function.