disk.frame
tidyverse
| disk.frame | tidyverse | |
|---|---|---|
| 5 | 2 | |
| 593 | 1,599 | |
| 0.2% | 0.9% | |
| 0.0 | 4.9 | |
| 3 months ago | 5 months ago | |
| R | R | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
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
tidyverse
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Discrimination of R in companies
What’s the original? Tidyverse, as it exists now, had it’s initial release in 2016. Pandas initial release was in 2009. AFAIK, ggplot2 and reshape are the only individual Tidyverse packages older than that.
- R packages installation error
What are some alternatives?
db-benchmark - reproducible benchmark of database-like ops
targets - Function-oriented Make-like declarative workflows for R
drake - An R-focused pipeline toolkit for reproducibility and high-performance computing
tidyqpcr - quantitative PCR analysis with the tidyverse
police-settlements - A FiveThirtyEight/The Marshall Project effort to collect comprehensive data on police misconduct settlements from 2010-19.
janitor - simple tools for data cleaning in R
r4ds - R for data science: a book