drake
disk.frame
| drake | disk.frame | |
|---|---|---|
| 1 | 5 | |
| 1,330 | 593 | |
| 0.1% | 0.3% | |
| 6.1 | 0.0 | |
| 2 months ago | 3 months ago | |
| R | R | |
| GNU General Public License v3.0 only | GNU General Public License v3.0 or later |
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drake
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Your impression of {targets}? (r package)
The targets package is the official successor to Drake, and has the same primary author (Will Landau). He has explained why he created targets, which includes stronger guardrails for users and better UX.
disk.frame
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Do you code from memory? Or do you reference things?
Say hello to disk.frame.
- How can I read in only two columns from a massive 10+ GB tab file?
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Data cleaning/ analysis 100-200 million rows of data. Is this doable in R, or is there another program I should try instead?
It depends on your hardware, but it should not be a problem. You might look into disk frame (https://diskframe.com) or similar packages.
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is it possible to have my enviroment objects and work with them on my local drive instead of RAM?
If that doesn't work, the disk.frame package might help. It is new-ish and not common, but does seem to work with data on disk rather than in memory
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We Test PCIe 4.0 Storage: The AnandTech 2021 SSD Benchmark Suite
> The speeds were just stunning to say the least at 15GB/s.
That is amazing. That is around DDR4-1866 speeds, and not far from DDR4-2666 (~21 GB/s). At those speeds I would happily work with dataframes sitting on the disk rather than in memory [1, 2]. Did you benchmark RAID 0 with less than four disks?
[1] R: https://github.com/xiaodaigh/disk.frame
What are some alternatives?
targets - Function-oriented Make-like declarative workflows for R
db-benchmark - reproducible benchmark of database-like ops
easystats - :milky_way: The R easystats-project
police-settlements - A FiveThirtyEight/The Marshall Project effort to collect comprehensive data on police misconduct settlements from 2010-19.
tabulapdf - Bindings for Tabula PDF Table Extractor Library
r4ds - R for data science: a book
ncaahoopR - An R package for working with NCAA Basketball Play-by-Play Data
awesome-R - A curated list of awesome R packages, frameworks and software.
fiery - A flexible and lightweight web server
opentripplanner - An R package to set up and use OpenTripPlanner (OTP) as a local or remote multimodal trip planner.
causalglm - Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning
droll - An R package to analyze roll distributions