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importr is a pretty high level function, trading performance for ease-of-use. It does a lot beside just loading an R package. It also maps all R objects in that package to Python (rpy2) objects. That effort is lost when doing importr('lattice') in your script, if the result is not used.
Beside that, importing packages in R itself is not without a cost (for the larger R packages with S4 class definitions, you this can be noticeable when the script is short). rpy2 can’t do much about this.
Using R variables such as R_PROFILE is possible, but this was not enabled by default before very recently. How to enable it is on SO (here).
Now, here importr is taking “only” 25% of the response time. Optimization efforts focusing on this will not be able to make it more than 25% faster (and that’s a very optimistic limit). Interpolating data into a string to evaluate it as R code after that is not very optimal (as warned in the documentation for rpy2 ). Consider calling the R function through rpy2, passing the data as anything exporting the buffer interface (for example).