Update parallel vignette to address {data.table} multithreading

[jdblischak]

This PR updates the parallel vignette to better compare the results from sequential and parallel processing. We thank @michaelmayer2 for alerting us to the fact that the default multithreading of {data.table} made the sequential processing timings confusing.

I made the following updates:

• Updated the variable names to reflect whether they were created by sequential or parallel processing
• Two important code chunks with the results had set eval=FALSE. I replaced the results table with a histogram to demonstrate the difference in durations for the two different enrollment strategies. I used all.equal() to demonstrate that set.seed() worked as expected
• I set data.table::setDTthreads(threads = 1) so that the sequential processing time was completely sequential. I didn't need to do any extra for the parallel processing because {data.table} automatically sets threads = 1 when it runs inside a forked process. Thus this will behave like we want when run in a non-interactive R session on a Linux machine (namely in GitHub Actions to produce the pkgdown page and on CRAN to produce the bundled vignette). The multithreading behavior gets more complex when run in Windows or from RStudio, but I don't think we need to worry about this

[nanxstats]

I appreciate the suggestions and efforts to improve the correctness and readability of this parallel vignette - it's often a challenge because it's more convoluted than it should be. Now this is the definition of ✨clarity✨