Discover and read the best of Twitter Threads about #rdatatable
Most recents (3)
Since various SW benchmarks are going around today... A short thread on why I use #rstats.
Put simply, it offers by far the fastest & most efficient tools for the work I do (i.e. mostly data wrangling & applied econometrics).
Put simply, it offers by far the fastest & most efficient tools for the work I do (i.e. mostly data wrangling & applied econometrics).
(Disclaimer: This thread is *not* tying to get you to change from your preferred SW. You should use whatever you feel comfortable with. But I will try to highlight some objective facts that matter to me.)
For data wrangling, nothing comes close to consistently matching the performance of #rdatatable. Benchmarks here: h2oai.github.io/db-benchmark/ 
As 2019 is ending, I want to share the work that occupied me for all of 2019 (and much of 2018). I had hoped that it would be completed by now, but there were roadblocks. On the flip side, I've met amazing people and learned cool skills working on this eartharxiv.org/5tg68
We reconstructed history of river discharge (also known as streamflow) of major rivers in Asia over the past eight centuries. We found that streamflow in the region is coherent, with floods and droughts often occurring at adjacent catchments and basins at the same time.
This result suggests that water management in the region be coordinated among adjacent basins.
25 days of disk.frame (1/25)
Use {disk.frame} for your larger-than-RAM data manipulation needs:
* no longer be limited by the amt of RAM
* be able to use familiar tools such as dplyr verbs and data.table syntax
See diskframe.com
#rdiskframe #diskframe #rstats
Use {disk.frame} for your larger-than-RAM data manipulation needs:
* no longer be limited by the amt of RAM
* be able to use familiar tools such as dplyr verbs and data.table syntax
See diskframe.com
#rdiskframe #diskframe #rstats
25 days of #diskframe (2/25)
Have a large CSV? `csv_to_disk.frame` is your friend:
* if you CSV is too large, consider setting in_chunk_size = 1e6 to load chunk by chunk
* a vector of CSV files as 1st argument to read them all
* uses data.table::fread, so is fast!
#rdiskframe
Have a large CSV? `csv_to_disk.frame` is your friend:
* if you CSV is too large, consider setting in_chunk_size = 1e6 to load chunk by chunk
* a vector of CSV files as 1st argument to read them all
* uses data.table::fread, so is fast!
#rdiskframe
25 days of #rdiskframe (3/25)
If you use RStudio, you can use <tab> to auto-complete disk.frame column names for you.
For more #diskframe convenience features, see diskframe.com/articles/conve…
#rstats
If you use RStudio, you can use <tab> to auto-complete disk.frame column names for you.
For more #diskframe convenience features, see diskframe.com/articles/conve…
#rstats
