fastverse
collapse
Our great sponsors
fastverse | collapse | |
---|---|---|
3 | 2 | |
213 | 599 | |
1.9% | - | |
6.8 | 9.6 | |
19 days ago | 8 days ago | |
R | C | |
GNU General Public License v3.0 only | 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.
fastverse
-
Looking for a book for better coding - preferably functional
For writing faster code, the first thing you want to try is making sure it's properly vectorised. See The R Inferno for this. Some problems are more difficult than others to vectorise. When vectorisation is impossible, you probably want to interface with C++. First, check if there's already a fast package that serves your needs. If your problem is too specific, consider writing your own C++ code with Rcpp.
-
Vectorized function VS Loops
I understand the sentiment and I'm not trying to convince you to start writing optimised code to save ~2ms. There's a ton of optimised tools that I don't use myself because the time benefit is immaterial for what I do.
- Fastverse High-Performance and Low-Dependency Package for Data Manipulation in R
collapse
-
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.
-
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?
targets - Function-oriented Make-like declarative workflows for R
writexl - Portable, light-weight data frame to xlsx exporter for R
engsoccerdata - English and European soccer results 1871-2022
epanet2toolkit - An R package for calling the Epanet software for simulation of piping networks.
tweetbotornot2 - 🔍🐦🤖 Detect Twitter Bots!
priceR - Economics and Pricing in R
MODIStsp - An "R" package for automatic download and preprocessing of MODIS Land Products Time Series
bruceR - 📦 BRoadly Useful Convenient and Efficient R functions that BRing Users Concise and Elegant R data analyses.
drake - An R-focused pipeline toolkit for reproducibility and high-performance computing
tableone - R package to create "Table 1", description of baseline characteristics with or without propensity score weighting
awesome-R - A curated list of awesome R packages, frameworks and software.
r-yaml - R package for converting objects to and from YAML