disk.frame
r4ds
| disk.frame | r4ds | |
|---|---|---|
| 5 | 165 | |
| 593 | 4,366 | |
| 0.3% | - | |
| 0.0 | 8.4 | |
| 3 months ago | 13 days 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.
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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
r4ds
- Ask HN: Learning Maths from the Ground Up
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Any suggestions on where I can learn R studio for an affordable cost?
https://r4ds.hadley.nz is free and very good
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Help with Understanding data loading/cleaning in R.
R for Data Science teaches you the tidyverse packages, which makes data wrangling so much easier!
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Learning R & statistics
One of the best free resources is the R4DS book by Hadley Wickham. You should make sure you start with the in progress second edition. https://r4ds.hadley.nz/
- Trying to learn Rstudio
- Questions as incoming PhD political science student
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First R project
The first edition of R4DS is quite old now. Check out the soon to be released second edition: https://r4ds.hadley.nz/
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Is R dead?
R for Data Science (2nd Ed), the updated guide from Hadley Wickham
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[Career] Strong Mathematics Background, Limited "Technical" Background
The big skills gap you have is in practical data exploration and transformation, which will be a large part of any data-centric role. As much as people may have distaste for it, there is no avoiding data manipulation as critical foundational enabler of all inferential and predictive modeling work. SQL is the lingua franca here and well worth picking up the basics (joins, window functions, handling dates and times, etc.), plus learning how to implement similar transformations in R and Python. With appropriately transformed data, you then need to be able to visualize it effectively using tools like Tableau or ggplot2 in R. I would not necessarily seek courses or certificates in it but expect to be evaluated on them in technical interview screenings, so self-study accordingly. R for Data Science by Hadley Wickham is a great free resource for these topics for R.
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There’s a lot of data science books out there, any recommendations for must-reads?
I just looked and there is now a second edition! https://r4ds.hadley.nz/
What are some alternatives?
db-benchmark - reproducible benchmark of database-like ops
swirl - :cyclone: Learn R, in R.
drake - An R-focused pipeline toolkit for reproducibility and high-performance computing
fasteR - Fast Lane to Learning R!
police-settlements - A FiveThirtyEight/The Marshall Project effort to collect comprehensive data on police misconduct settlements from 2010-19.
tidytuesday - Official repo for the #tidytuesday project
awesome-R - A curated list of awesome R packages, frameworks and software.
viridis - Colorblind-Friendly Color Maps for R
opentripplanner - An R package to set up and use OpenTripPlanner (OTP) as a local or remote multimodal trip planner.
lab02_R_intro - Vežbe 2: Uvod u R
R-vs.-Python-for-Data-Science
ggplot2-book - ggplot2: elegant graphics for data analysis