1. x = rnorm(1000) 2. plot(density(x)) --> not work 3. stem(x) --> not work 4. summary(x) --> works
For R data handling, I always use data.table for its efficiency and power.
1. library(data.table) 2. x = data.table(x=rnorm(1000)) --> not work > x = data.table(x=rnorm(1000)) ERROR: Exception calling Calloccolwrapper : Unimplemented GNU R API function 'DUPLICATE_ATTRIB'
However, I would agree with you that at this point in time it's fair to assume that R is no longer a language in and of itself - it's more akin to a collection of APIs around years of optimized C++ code, somewhat separated into a few universes (bioinformatics, time series analysis with xtz, data.table aficionados and tidyverse acolytes).
And thus making a faster R alternative should focus on 100% package compatibility along with speeding up native R code. Renjin, pqR [1], et. al. all seem to be compromises that work for some workflows but not all of them.
The project that I'm excited for is fastR [2] - bringing R, C, C++ and FORTRAN code together into one Truffle/Graal environment would be absolutely huge for this ecosystem.
However, I've tested fastR on my employer's [3] workflows and it does not work yet.
[1] http://www.pqr-project.org/ [2] https://github.com/graalvm/fastr [3] http://syberia.io/