Ask HN: Big Data Performance Issues: R vs. Stata?
4 comments
Don’t know what your hardware is, but there are libraries that can do some computations in parallel.
You’ll want to look into using a parallel backend in R. It will depend on the system architecture.
As for libraries that may help potentially, speedglm, biglm,
There’s also some options from bioconductor: https://bioconductor.org/packages/release/data/experiment/vi...
You could also try glmnet with a parallel backend.
You’ll want to look into using a parallel backend in R. It will depend on the system architecture.
As for libraries that may help potentially, speedglm, biglm,
There’s also some options from bioconductor: https://bioconductor.org/packages/release/data/experiment/vi...
You could also try glmnet with a parallel backend.
It’s possible that if you could spin up an open source relational database like Postgres or MySQL and load your data set, you could do everything but the models and viz themselves in SQL. SQL will handle 3 mil rows in a snap. If you are transforming the contents of the cells then R is potentially nicer, but if you are mainly summarizing, grouping, subsetting, etc SQL is fast and readable and you can “dump” a CSV that’s fast to read in R or Stata. Your R or Stata code will be really short, really just a load step, a viz / pretty tables step, and then a model run step. It’ll be easy to swap out languages according to the team/prof/project requirements.
data.table is typically much faster than dpylr or custom code for large datasets. Figure out what parts of your preprocessing are taking the most time, and try to rewrite those using data.table.
R has some non-default packages for linear regression [0]. If your usecase is supported by one of those then I'd imagine it would be faster than Stata.
[0] https://stackoverflow.com/questions/25416413/is-there-a-fast...
R has some non-default packages for linear regression [0]. If your usecase is supported by one of those then I'd imagine it would be faster than Stata.
[0] https://stackoverflow.com/questions/25416413/is-there-a-fast...
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My question is about the relative performance of R and STATA for logit models. Since I have a large data set, I experience quite slow computation times for my code, in both languages.
-R: I am using tableone() package which isn't particularly fast, I suspect, and I use dplyr and data.table for munging. If I wrote my own function for making tables, do you think it would be any faster? I could also try to utilize data.table for a higher % of the coding process?
-STATA: Do I really need to do my regressions in STATA? I have heard they are much slower in R, especially given that our fixed effects model has around 150 hospital sites to adjust each model for. Ideally, couldn't I do this within R so I don't have to pipe the data from STATA back into R? Or is there an easy way to export STATA output into R to then munge into tables?
Thanks for your help. I am stressing on this project and I want to level up the automation. I just spent 6 hours hand-filling cells for a table and I vowed to never do so again!