Versatility is definitively a major advantage in R.

I'm using SPSS a lot, but some calculations need extensive macro's. In R, most of these specialized functions are available from other users, so there is a huge benefit in time that you gain by not having to write everything yourself.

A second advantage I find important is the community support and the extensive help you can find for most R code.

The initial steep learning curve might be its major disadvantage for some people, but ones you start mastering R, it gives you all possibilities you need, even in advanced statistics.

I guess .. license price?

I have worked with both, and depending on the issue it does not matter which one you choose; results are the same.

In another thread there was a question about plots: Is R better than Octave/Matlab for plotting various graphs for relatively smaller statistical scientific data? (https://www.researchgate.net/post/Is_R_better_than_Octave_Matlab_for_plotting_various_graphs_for_relatively_smaller_statistical_scientific_data)

Hey Martin, thanks for the response. I am aware the price is one factor but many people might have so many reasons and compiling these reasons are one of the main objective. These might include cost, efficeincy, easiness, speed etc. Lets check who else has any specific reason.

Hi Muhammad, i think that in order to compare both softwares, you'd have to specify the tools that you will use in the analysis; i.e.,Matlab contains a lot of toolboxes that have been developed along much time (for signal processing, real-time acquisition...), and packages for R are still being developed. Definitely, it would depend on your research demands.

Cost and versatility. If you are familiar with coding. R can essentially do whatever you want it to do so long as you can tell it to do what you want. Until R, I was used to drop-down based software, but actually find R quite intuitive and user friendly - it's just a matter of getting past the initial learning curve. But I think its versatility is an underrated aspect of R. For example, a colleague of mine has developed a way of selecting an "optimal" alpha level for hypothesis tests (rather than the arbitrary alpha=0.05) and has written R code that anyone can use to employ his method.

Versatility is definitively a major advantage in R.

I'm using SPSS a lot, but some calculations need extensive macro's. In R, most of these specialized functions are available from other users, so there is a huge benefit in time that you gain by not having to write everything yourself.

A second advantage I find important is the community support and the extensive help you can find for most R code.

The initial steep learning curve might be its major disadvantage for some people, but ones you start mastering R, it gives you all possibilities you need, even in advanced statistics.