pak
A fresh approach to package installation (by r-lib)
collapse
Advanced and Fast Data Transformation in R (by SebKrantz)
pak | collapse | |
---|---|---|
1 | 2 | |
623 | 600 | |
1.3% | - | |
9.3 | 9.6 | |
2 days ago | 12 days ago | |
C | C | |
- | 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.
collapse
Posts with mentions or reviews of collapse.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-05-01.
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is there a package using data.table that provides functions for descriptive stats, missingness etc?
The ask is a little unclear. You might be interested in collapse and more generally in other packages in the fastverse. I guess it's also worth pointing out that data.table already provides alternative methods for certain base R descriptive stats functions (e.g., mean, etc.) that are automatically used when applied to datatables.
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Benchmarking for loops vs apply and others
If you are looking for performance I would recommend to check the collapse package. The following line "collapse" = collapse::fsum(df_datatable$x, g=df_datatable$g) is around 2x faster than base::rowsum, and the dplyr style syntax doesn't add that much of an overhead "collapse dplyr" = df_datatable |> fgroup_by(g) |> fsum(x)
What are some alternatives?
When comparing pak and collapse you can also consider the following projects:
tidytable - Tidy interface to 'data.table'
fastverse - An Extensible Suite of High-Performance and Low-Dependency Packages for Statistical Computing and Data Manipulation in R
poorman - A poor man's dependency free grammar of data manipulation
writexl - Portable, light-weight data frame to xlsx exporter for R
rmarkdown - Dynamic Documents for R
epanet2toolkit - An R package for calling the Epanet software for simulation of piping networks.