writexl
Portable, light-weight data frame to xlsx exporter for R (by ropensci)
collapse
Advanced and Fast Data Transformation in R (by SebKrantz)
writexl | collapse | |
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2 | 2 | |
207 | 604 | |
1.0% | - | |
4.7 | 9.6 | |
19 days ago | 1 day ago | |
C | C | |
GNU General Public License v3.0 or later | 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.
writexl
Posts with mentions or reviews of writexl.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-11-01.
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Clippy: Really??
Bug: Clippy does not appear! https://github.com/ropensci/writexl/issues/68
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 writexl and collapse you can also consider the following projects:
php-ext-xlswriter - 🚀 PHP Extension for creating and reader XLSX files.
fastverse - An Extensible Suite of High-Performance and Low-Dependency Packages for Statistical Computing and Data Manipulation in R
jsonlite - A Robust, High Performance JSON Parser and Generator for R
epanet2toolkit - An R package for calling the Epanet software for simulation of piping networks.
xlsx - An R package to interact with Excel files using the Apache POI java library
priceR - Economics and Pricing in R
fast_excel - Ultra Fast Excel Writer for Ruby
bruceR - 📦 BRoadly Useful Convenient and Efficient R functions that BRing Users Concise and Elegant R data analyses.