In addition to the techniques mentioned in the previous answer, another strategy to optimize R code is to utilize the 'dplyr' package for data manipulation tasks. 'dplyr' provides efficient functions such as 'filter()', 'mutate()', and 'summarize()' that can process large datasets much faster than traditional base R methods. Implementing caching mechanisms like 'memoise' can also be beneficial by storing results of computationally intensive functions for reuse. Lastly, profiling your code using tools like 'profvis' or 'lineprof' can help identify bottlenecks that can be optimized.

One advanced technique for optimizing R code is to vectorize your operations, which allows for more efficient computation by performing operations on entire vectors or matrices instead of individual elements. Another technique is to use the 'data.table' package, which provides a high-performance alternative to data frames. Additionally, parallel computing can be employed to distribute computations across multiple cores or machines. These are just a few examples, but there are many other techniques for optimizing R code depending on the specific problem and data involved.

While the previous answers cover several important techniques, it's worth mentioning that leveraging compiled languages such as C or C++ through packages like 'Rcpp' can significantly improve the speed of critical sections in your R code. Another approach is to use the 'ff' package, which allows for seamless operation on out-of-memory data structures by utilizing disk space efficiently. Furthermore, keeping up with the latest advancements in R through conferences, online communities, and specialized blogs can help uncover new techniques for optimization.