3) Use new project mode from RStudio This helps to clean workflow management
5) Avoiding duplicates , remove prior copies and use gc() Memory management is key to use of R in production analytics.
6) Think object oriented. Forget other languages Think slice and dice and using $ and  and using apply versus for loops.
7) Use ? and ?? before you google and ask for help on Stack Overflow Seriously dude R has a lot of documentation! A Lot! Use it . Also see CRAN Views!
8) You are not too old to learn dplyr on Datacamp Skilling up and reskilling is part of being a data science hacker
9) Subscribe to R-bloggers and never miss out on a new package that helps solve your problems R has 8000+ packages and 150000 + functions. All you need is one function to cut down your analysis time and go home early
10) Profiling code, benchmark functions and byte compilation seperate grown up from the kids data scientists. Hadley says- http://adv-r.had.co.nz/Rcpp.html Hadley says-http://adv-r.had.co.nz/Profiling.html Enough said!