Top Tweets for #Rdatatable
Friends near Madrid, check out this upcoming #rdatatable event!
A Zoom option is also available, if you want to join from afar.
https://t.co/k8qvbUzktL
(Presentation will be in English.)
Regular reminder that we love improving documentation and errors in {data.table} #rstats #rdatatable
question for the #rstats fam. When you were learning R, did you find some of the examples in the help docs to be cryptic? I did. Please reply to this and post any cryptic code examples from the help docs. I will ask AI to explain them and see if that makes a difference.
Tyson Barrett (@healthandstats) is presenting, "Using and Contributing to the data.table Package for Efficient Big Data Analysis" #rfinance #rstats #rdatatable /cc @rdatatable
We're on LinkedIn now! Join us if that's your jam:
https://t.co/Q2Sab3I0Sr
#rdatatable
#joinALLthesocials
@sTeamTraen Speed, is one. While I too am a very strong believer in minimizing dependencies, sometimes packages are written more efficiently than base #rstats. #rdatatable is the archetypal example of this.
@Kyle_Ireton I use it ALL THE TIME. and if you have #rdatatable loaded, there is a fast version for characters called %chin%.
Assigning values to columns looks and feels different in {data.table} compared to base R data frames.
Why, and how does this make {data.table} so much faster? π€
Toby breaks it down for us in the latest Tips & Tricks blog post:
π https://t.co/hNxnrIKJ50
#rstats #rdatatable
π¬ "I would like to use data.table but I prefer a pipeline workflow"
Is this you? You're in luck!
Check out Elio Campitelli's guest blog on The Raft about how to pipe in data.table:
π https://t.co/SrvLH5MbqH
#rstats #rdatatable #datascience
We have our first new data.table feature release in years! πππ
π Blog post about the process and the new release: https://t.co/7PMbOKD9V8
Huge thanks to all the hard working open-source devs who came together to make this happen. π
#rstats #datascience #rdatatable
2/2
@f2harrell @mdancho84 @ChristosArgyrop I agree. I maintain (as in always say) : #rdatatable - come for the speed, stay for the syntax.
β³ Only one week left to fill out the Community Survey for #rdatatable!!!
Get on it, #rstats friends - we want to know how we can best direct all our work in growing data.table to support you. πππ
π https://t.co/ToWBthbnjC
New post up on The Raft: Announcing our Travel Awards for #rdatatable Ambassadors! π βοΈ π€π½
Check it out and apply for a $2700 stipend to fund YOUR data.table related talk.
π π π
https://t.co/DnfhmXbteU
Shoutout to the learners at the #rdatatable workshop at @LatinR_Conf today!
π§π»βπ»πΊπΎ Β‘Espero que aprendan mucho! πΊπΎ π©π½βπ»
Big news for you data table fans: Our blog is now live! π₯³
https://t.co/MDSCngOS5F
Check it out for an overview of the new NSF project, as well as a paid opportunity for translations of error messages and documentation!
#rstats #rdatatable
Today, I experimented with the ivs R-package. I enjoyed the syntax and found it to be intuitive, but it was too slow for my large datasets. I will continue to use #rdatatable, but I believe this is a welcome addition to the ecosystem of the tidyverse. https://t.co/s9LPbuf6Wj
Have a directory where all your data, outputs, and code are stored. One way to access elements in those directories is with the file.path function.
1. Create a variable with the project path
2. Import the data from the data sub directory
#rstats
#rdatatable
#tidyverse
@DataCornering Thanks for including examples using #rdatatable you can all emulate summarize(across()) by using DT[, lapply(.SD), .SDcols = c("value1", "value2")]
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