fasteR
db-benchmark
fasteR | db-benchmark | |
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22 | 91 | |
905 | 320 | |
- | 0.0% | |
7.1 | 0.0 | |
6 months ago | 11 months ago | |
R | R | |
- | Mozilla Public License 2.0 |
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fasteR
- Matloff/fasteR: Fast Lane to Learning R (2019)
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Anyone know which pod where Eric and Greg talked about R and SPSS?
The good/bad news is that R has become so popular that there is a overabundance of resources you can use to learn it. Here are a few that helped me get started (though they may be dated at this point, ymmv): [1] [2] [3] [4] [5].
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Where to learn R?
Start with fasteR, then move to Hands on Programming with R and R for Data Science. There is considerable overlap in the early chapters so don’t be afraid to skip parts. If you want to know more about the nuts and bolts of R then try Advanced R.
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Help Please !
This tutorial is very good for starters. As is this book.
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Book suggestion for R beginner in college? Using Tidyverse DPLYR etc.
If you’re sure you need to be focussed on tidyverse then the seminal text would be R for Data Science. If you want even more basics than that then I would start with this link, and a book like Hands on Programming with R.
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STA Courses Programming in Python?
I’m taking a special topic ECS course and we use R here the profs quick start course https://github.com/matloff/fasteR
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Would love your college course PowerPoints on how to use R Studio
Recently I’ve favoured recommending this as one of the best ways to get up to speed with the main basics as quickly as possible. But be prepared that this really is just the start, and you will need to follow the recommendations here or elsewhere for further learning.
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Rgui or Rstudio? And why is my Rgui blurry?
Check out his free course on GitHub where you'll see he walks you through getting right into learning R, keeping things simple by using the R Gui command line: https://github.com/matloff/fasteR
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Programming (Stata, R & Python)
For R - the free book "R for Data Science", https://github.com/matloff/fasteR , and the data.table vignettes (I personally prefer using data.table than tidyverse, although there are some useful functions in tidyverse)
- Resources for learning R?
db-benchmark
- Database-Like Ops Benchmark
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Polars
Real-world performance is complicated since data science covers a lot of use cases.
If you're just reading a small CSV to do analysis on it, then there will be no human-perceptible difference between Polars and Pandas. If you're reading a larger CSV with 100k rows, there still won't be much of a perceptible difference.
Per this (old) benchmark, there are differences once you get into 500MB+ territory: https://h2oai.github.io/db-benchmark/
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DuckDB performance improvements with the latest release
I do think it was important for duckdb to put out a new version of the results as the earlier version of that benchmark [1] went dormant with a very old version of duckdb with very bad performance, especially against polars.
[1] https://h2oai.github.io/db-benchmark/
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Show HN: SimSIMD vs. SciPy: How AVX-512 and SVE make SIMD cleaner and ML faster
https://news.ycombinator.com/item?id=33270638 :
> Apache Ballista and Polars do Apache Arrow and SIMD.
> The Polars homepage links to the "Database-like ops benchmark" of {Polars, data.table, DataFrames.jl, ClickHouse, cuDF, spark, (py)datatable, dplyr, pandas, dask, Arrow, DuckDB, Modin,} but not yet PostgresML? https://h2oai.github.io/db-benchmark/ *
LLM -> Vector database: https://en.wikipedia.org/wiki/Vector_database
/? inurl:awesome site:github.com "vector database"
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Pandas vs. Julia – cheat sheet and comparison
I agree with your conclusion but want to add that switching from Julia may not make sense either.
According to these benchmarks: https://h2oai.github.io/db-benchmark/, DF.jl is the fastest library for some things, data.table for others, polars for others. Which is fastest depends on the query and whether it takes advantage of the features/properties of each.
For what it's worth, data.table is my favourite to use and I believe it has the nicest ergonomics of the three I spoke about.
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Any faster Python alternatives?
Same. Numba does wonders for me in most scenarios. Yesterday I've discovered pola-rs and looks like I will add it to the stack. It's API is similar to pandas. Have a look at the benchmarks of cuDF, spark, dask, pandas compared to it: Benchmarks
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Pandas 2.0 (with pyarrow) vs Pandas 1.3 - Performance comparison
The syntax has similarities with dplyr in terms of the way you chain operations, and it’s around an order of magnitude faster than pandas and dplyr (there’s a nice benchmark here). It’s also more memory-efficient and can handle larger-than-memory datasets via streaming if needed.
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Pandas v2.0 Released
If interested in benchmarks comparing different dataframe implementations, here is one:
https://h2oai.github.io/db-benchmark/
- Database-like ops benchmark
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Python "programmers" when I show them how much faster their naive code runs when translated to C++ (this is a joke, I love python)
Bad examples. Both numpy and pandas are notoriously un-optimized packages, losing handily to pretty much all their competitors (R, Julia, kdb+, vaex, polars). See https://h2oai.github.io/db-benchmark/ for a partial comparison.
What are some alternatives?
r4ds - R for data science: a book
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
swirl - :cyclone: Learn R, in R.
datafusion - Apache DataFusion SQL Query Engine
R-vs.-Python-for-Data-Science
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
databend - 𝗗𝗮𝘁𝗮, 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 & 𝗔𝗜. Modern alternative to Snowflake. Cost-effective and simple for massive-scale analytics. https://databend.com
sktime - A unified framework for machine learning with time series
DataFramesMeta.jl - Metaprogramming tools for DataFrames
arrow2 - Transmute-free Rust library to work with the Arrow format
disk.frame - Fast Disk-Based Parallelized Data Manipulation Framework for Larger-than-RAM Data
datatable - A Python package for manipulating 2-dimensional tabular data structures