pak
tidytable
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pak | tidytable | |
---|---|---|
1 | 26 | |
622 | 434 | |
3.2% | - | |
9.3 | 8.2 | |
4 days ago | 20 days ago | |
C | R | |
- | 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.
pak
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Why is {dplyr} so huge, and are there any alternatives or a {dplyr} 'lite' that I can use for the basic mutate, group_by, summarize, etc?
For installation, check out pak https://github.com/r-lib/pak, it's able to install in parallel.
tidytable
- Tidyverse 2.0.0
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fuzzyjoin - "Error in which(m) : argument to 'which' is not logical"
If you need speed, you should consider using dtplyr (or tidytable), or even dbplyr with duckdb.
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tidytable v0.10.0 is now on CRAN - use tidyverse-like syntax with data.table speed
What do you think of this instead?
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Offering several functions to create the same object in my package
Here's an example - I use this in a package I've built called tidytable. Here is the as_tidytable() function I use that uses method dispatch.
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Dplyr performance issues (Late 2022)
If you're having performance issues with dplyr you can also try out tidytable
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R Dialects Broke Me
Iād say tidytable is a better option these days as it supports more functions. Although I think dtplyr has improved on this front recently, but still lags. The author of tidytable contributes to dtplyr as well.
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Why is mlr3 so under-marketed?
I know you said it 'feels much faster' which isn't exactly a data oriented comparison, but tidymodels performs very well. You can see one of the dplyr functions as step_* in tidymodels, for example mutate vs. step_mutate under recipes library. The author of tidytable, which uses data.table, had some revisions due to this conversation, just as an example.
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Why is {dplyr} so huge, and are there any alternatives or a {dplyr} 'lite' that I can use for the basic mutate, group_by, summarize, etc?
Tidytable is what you might be looking for: https://markfairbanks.github.io/tidytable/, this will require a bit of refactoring (e.g group-bys happen as arguments in summarise/mutate). You'll get data.table like speed in a very compact & complete package.
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Programming with R {dplyr}
People can also use tidytable and keep the same workflow they're already used to š
- tidytable v0.8.1 is on CRAN - it also comes with a new logo! Need data.table speed with tidyverse syntax? Check out tidytable.
What are some alternatives?
poorman - A poor man's dependency free grammar of data manipulation
dtplyr - Data table backend for dplyr
rmarkdown - Dynamic Documents for R
tidypolars - Tidy interface to polars
awesome-R - A curated list of awesome R packages, frameworks and software.
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
RFI - R-Fortran Interface for Modern Fortran
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
r4ds - R for data science: a book
tidyr - Tidy Messy Data
root - The official repository for ROOT: analyzing, storing and visualizing big data, scientifically
Tidier.jl - Meta-package for data analysis in Julia, modeled after the R tidyverse.