pak
A fresh approach to package installation (by r-lib)
poorman
A poor man's dependency free grammar of data manipulation (by nathaneastwood)
| pak | poorman | |
|---|---|---|
| 1 | 2 | |
| 623 | 330 | |
| 1.3% | - | |
| 9.3 | 5.7 | |
| 2 days ago | 3 months ago | |
| C | R | |
| - | GNU General Public License v3.0 or later |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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
Posts with mentions or reviews of pak.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-12-15.
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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.
poorman
Posts with mentions or reviews of poorman.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-12-15.
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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?
You might find the poorman package interesting: https://github.com/nathaneastwood/poorman
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Just how widely accepted is tidyr/dplyr these days?
It's true that their packages are heavy on dependencies, and if that is a concern, you have alternatives: - poorman: no dependencies, same syntax as dplyr, but only includes basic verbs. - datawizard: low dependencies, slightly different syntax, has base-R implementations of most of dplyr / tidyr functions, plus some other goodies likes scaling, mean-centering, rank transforming, ... - And of course, data.table: 0 dependencies, ultra-fast (everything is written in optimized C under the hood), can manipulate much bigger data than the Tidyverse, and can do everything the tidyverse can when it comes to data wrangling (however, sometimes the tidyverse has convenience functions that make some operations shorter than with data.table). The downside is that data.table's syntax requires more efforts to learn / is less intuitive to read for neophytes.
What are some alternatives?
When comparing pak and poorman you can also consider the following projects:
tidytable - Tidy interface to 'data.table'
re2 - R interface to Google re2 (C++) regular expression engine
rmarkdown - Dynamic Documents for R
awesome-R - A curated list of awesome R packages, frameworks and software.
RFI - R-Fortran Interface for Modern Fortran
ggplot2 - An implementation of the Grammar of Graphics in R
r4ds - R for data science: a book
fastverse - An Extensible Suite of High-Performance and Low-Dependency Packages for Statistical Computing and Data Manipulation in R