A drawback of R compared to Stata and, within R, of data.table compared to tidyverse, is that documentation is spread out all over the place. You end up doing a lot of googling and spending time at Stack overflow. The problem is many R users now think the tidyverse and dplyr in particular is all there is. I started with base R and it is still what i use for many things. But when I began to switch my data “wrangling” from Stata to R, I got started on dplyr, only to learn (from Julian Hinz) that data.table is much faster. After initial struggles I realized that the syntax of data.table fit much better with my intuitions of the right way to do things. I am in the minority on this topic but not alone. In an effort to convince more people to take advantage of the powerful syntax and awesome speed of data.table, I am posting this set of resources.

PS. This now goes to 11! Here’s a great set of lecture slides from Grant McDermott. If you don’t follow him already, you should.

https://raw.githack.com/uo-ec510-2020-spring/lectures/master/05-datatable/05-datatable.html#1

These items are not ordered for beginners. I started with number 8 (which, as the title suggests, was probably not optimal!). But 4 and then 3 might be ideal starting points. According to my notes 9 (and its sequels) are good starting places.

1. https://stackoverflow.com/questions/21435339/data-table-vs-dplyr-can-one-do-something-well-the-other-cant-or-does-poorly This is not actually a good starting point at all but if you’re already a dplyr user you might look at this to decide how interested you might be in data.table
2. https://atrebas.github.io/post/2019-03-03-datatable-dplyr/ Comprehensive correspondence between dplyr and data.table ways to do things
3. https://github.com/chuvanan/rdatatable-cookbook. Here’s a set of chapters showing how to do common tasks in data.table.
4. https://github.com/Rdatatable/data.table/wiki//talks/useR2019_Arun.pdf Great slides introduce data.table’s three key components.
   Also illustrates .SD with patterns, a very powerful method.
5.  Frequently Asked Questions: http://datatable.r-forge.r-project.org/datatable-faq.pdf
6. The cheat sheet to print out and and pin to the wall somewhere nearby. https://s3.amazonaws.com/assets.datacamp.com/blog_assets/datatable_Cheat_Sheet_R.pdf
7. Many posts have shown how much faster data.table is than the alternatives. This one compares with dplyr, base R, and Python Pandas. https://github.com/szilard/benchm-dplyr-dt And here is some code showing how they did each task in data.table compared to dplyr: https://github.com/szilard/benchm-dplyr-dt/blob/master/bm.Rmd
8. http://brooksandrew.github.io/simpleblog/articles/advanced-data-table/ Advanced tips and tricks with data.table
9. https://www.r-bloggers.com/data-table-by-example-part-1/
10. https://cran.r-project.org/web/packages/data.table/vignettes/datatable-reference-semantics.html Explains about “modifying by reference” which is one of the trickiest topics and most important to understand. Here you will see why “DT <- copy(DT)”  will be an essential line in your functions to avoid unwanted side-effects. More discussion here: https://stackoverflow.com/questions/13756178/writings-functions-procedures-for-data-table-objects

I recently found out about this resource https://lost-stats.github.io/Data_Manipulation/data_manipulation.html which is really helpful for those interested in learning how to implement basic data and estimation tasks in both Stata and R.  Unfortunately, the R implementations rely on dplyr, a part of the tidyverse. While I understand that many people prefer this approach, my concern is that new users of R will think dplyr is the only way to work with data in R. There is an alternative: data.table.

To give an example I wrote a short script (collapse) based on the code given on lost-stats for how to do the same thing in data.table. The task is to take the “storms” data set (provided in dplyr) and aggregate three variables by the storm name, year, month, and day. The original data set collapses from 10,010 observations to 2777 observations. This must be one of the most common operations most of us do with data.

I then benchmarked it and found data.table is 100 times faster on this task for this data set. I also show to use RStata to run Stata code in R (if you have Stata). Stata was also very fast, even running it from within R (about 50 times faster than dplyr). One think I like about the way R does it is that you continue to have in memory both the pre- and post-collapse versions of the data.