Repository files navigation

Thefastverse is a suite of complementary high-performance packages for statistical computing and data manipulation in R. Developed independently by various people,fastverse packages jointly contribute to the objectives of:

• Speeding up R through heavy use of compiled code (C, C++, Fortran)
• Enabling more complex statistical and data manipulation operations in R
• Reducing the number of dependencies required for advanced computing in R

Thefastverse package is a meta-package providing utilities for easy installation, loading and managementof these packages. It is an extensible framework that allows users to (permanently) add or remove packages to create a 'verse' of packages suiting their general needs, or even create separate 'verses' of their own.

fastverse packages are jointly attached withlibrary(fastverse), and several functions starting withfastverse_ help manage dependencies, detect namespace conflicts, add/remove packages from thefastverse and update packages. Thevignette provides a concise overview of the package.

Thefastverse installs with 4 core packages^[Before v0.3.0matrixStats andfst were part of the core fastverse, but were removed following a poll in November 2022 which established that more than 50% of users don't use them actively.] (5 dependencies in total) which provide broad C/C++ based statistical and data manipulation functionality and have carefully managed APIs.

• data.table: Enhanced data frame class with concise data manipulation framework offering powerful aggregation, flexible split-apply-combine computing, reshaping, (rolling) joins, rolling statistics, set operations on tables, fast csv read/write, and various utilities such as transposition of data.

• collapse: Fast grouped and weighted statistical computations, time series and panel data transformations, list-processing, data manipulation functions, summary statistics and various utilities such as support for variable labels. Class-agnostic framework designed to work with vectors, matrices, data frames, lists and related classes includingxts,data.table,tibble,plm,sf.

• kit: Parallel (row-wise) statistical functions, vectorized and nested switches, and some utilities such as efficient partial sorting.

• magrittr: Efficient pipe operators and aliases for enhanced R programming and code un-nesting.

# Install the CRAN versioninstall.packages("fastverse")# Install (Windows/Mac binaries) from R-universeinstall.packages("fastverse",repos="https://fastverse.r-universe.dev")# Install from GitHub (requires compilation)remotes::install_github("fastverse/fastverse")

Note that the GitHub/r-universe version is not a development version, development takes place in the 'development' branch.

Users can, via thefastverse_extend() function, freely attach extension packages. Settingpermanent = TRUE adds these packages to the corefastverse. Another option is adding a.fastverse config file with packages to the project directory. Separate verses can be created withfastverse_child(). See thevignette for details.

High-performing packages for different data manipulation and statistical computing topics are suggested below. The total (recursive) dependency count is indicated for each package.

• xts andzoo: Fast and reliable matrix-based time series classes providing fully identified ordered observations and various utilities for plotting and computations (1 dependency).

• roll: Fast rolling and expanding window functions for vectors and matrices (3 dependencies).

  Notes:xts/zoo objects are preserved byroll functions and bycollapse's time series and data transformation functions^[collapse functions can also handle irregular time series.]. Asxts/zoo objects are matrices, allmatrixStats functions apply to them as well.xts objects can also easily be converted to and fromdata.table, which also has some fast rolling functions likefrollmean andfrollapply.

• lubridate: Facilitates 'POSIX-' and 'Date' based computations (2 dependencies).

• anytime: Anything to 'POSIXct' or 'Date' converter (2 dependencies).

• fasttime: Fast parsing of strings to 'POSIXct' (0 dependencies).

• nanotime: Provides a coherent set of temporal types and functions with nanosecond precision -
  based on the 'integer64' class (7 dependencies).

• clock: Comprehensive library for date-time manipulations using a new family of orthogonal date-time classes (durations, time points, zoned-times, and calendars) (6 dependencies).

• timechange: Efficient manipulation of date-times accounting for time zones and daylight saving times (1 dependency).

  Notes: Date and time variables are preserved in manydata.table andcollapse operations.data.table additionally offers an efficient integer based date class 'IDate' with some supporting functionality.xts andzoo also provide various functions to transform dates, andzoo provides classes 'yearmon' and 'yearqtr' for convenient computation with monthly and quarterly data. Packagemondate also provides a class 'mondate' for monthly data.

• stringi: Main R package for fast, correct, consistent, and convenient string/text manipulation (backend tostringr andsnakecase) (0 dependencies).

• stringr: Simple, consistent wrappers for common string operations, based onstringi (3 dependencies).

• snakecase: Convert strings into any case, based onstringi andstringr (4 dependencies).

• stringfish: Fast computation of common (base R) string operations using the ALTREP system (2 dependencies).

• stringdist: Fast computation of string distance metrics, matrices, and fuzzy matching (0 dependencies).

• matrixStats: Efficient row-and column-wise (weighted) statistics on matrices and vectors, including computations on subsets of rows and columns (0 dependencies).

• Rfast andRfast2: Heterogeneous sets of fast functions for statistics, estimation and data manipulation operating on vectors and matrices. Missing values and object attributes are not (consistently) supported (4-5 dependencies).

• vctrs provides basic many basic programming functions for R vectors (including lists and data frames) implemented in C (such as sorting, matching, replicating, unique values, concatenating, splitting etc. of vectors). These are often significantly faster than base R equivalents, but generally not as aggressively optimized as equivalent functions found incollapse,kit,Rfast ordata.table (4 dependencies).

• parallelDist: Multi-threaded distance matrix computation (3 dependencies).

• coop: Fast implementations of the covariance, correlation, and cosine similarity (0 dependencies).

• rsparse: Implements many algorithms for statistical learning on sparse matrices - matrix factorizations, matrix completion, elastic net regressions, factorization machines (8 dependencies). See also packageMatrixExtra.

• fastmatrix provides a small set of functions written in C or Fortran providing fast computation of some matrices and operations useful in statistics (0 dependencies).

• matrixTests efficient execution of multiple statistical hypothesis tests on rows and columns of matrices (1 dependency).

• rrapply: Therrapply() function extends baserapply() by including a condition or predicate function for the application of functions and diverse options to prune or aggregate the result (0 dependencies).

• dqrng: Fast uniform, normal or exponential random numbers and random sampling (i.e. fasterrunif,rnorm,rexp,sample andsample.int functions) (3 dependencies).

• fastmap: Fast implementation of data structures based on C++, including a key-value store (fastmap), stack (faststack), and queue (fastqueque) (0 dependencies).

• fastmatch: A fastermatch() function (drop-in replacement forbase::match, andbase::%in%), that keeps the hash table in memory for much faster repeated lookups (0 dependencies).

• hutilscpp provides C++ implementations of some frequently used utility functions in R (4 dependencies).

  Notes:Rfast has a number of like-named functions tomatrixStats. These are simpler but typically faster and support multi-threading. Some highly efficient statistical functions can also be found scattered across various other packages, notable to mention here areHmisc (60 dependencies) andDescTools (17 dependencies).

• sf: Leading framework for geospatial computing and manipulation in R, offering a simple and flexible spatial data frame and supporting functionality (12 dependencies).

• s2: Provides R bindings forGoogle's s2 C++ library for high-performance geometric calculations onthe sphere (3D, geographic/geodetic CRS). Used as a backend tosf for calculations on geometries with geographic/geodetic CRS, but usings2 directly can provide substantial performance gains (2 dependencies).

• geos: Provides an R API to theOpen Source Geometry Engine (GEOS) C-library, which can be used to very efficiently manipulate planar (2D/flat/projected CRS) geometries, and a vector format with which to efficiently store 'GEOS' geometries. Used as a backend tosf for calculations on geometries with projected CRS, but usinggeos directly can provide substantial performance gains (2 dependencies).

• stars: Spatiotemporal data (raster and vector) in the form of dense arrays, with space and time being array dimensions (16 dependencies).

• terra: Methods for spatial data analysis with raster and vector data. Processing of very large (out of memory) files is supported (1 dependency).

• dggridR: Provides discrete global grids for R: allowing accurate partitioning of the earths surface intoequally sized grid cells of different shapes and sizes (37 dependencies).

  Notes:collapse can be used for efficient manipulation and computations onsf data frames.sf also offers tight integration withdplyr.

• dygraphs: Interface to 'Dygraphs' interactive time series charting library (12 dependencies).

• lattice: Trellis graphics for R (0 dependencies).

• grid: The grid graphics package (0 dependencies).

• ggplot2: Create elegant data visualizations using the Grammar of Graphics (27 dependencies).

• scales: Scale functions for visualizations (11 dependencies).

  Notes:latticeExtra provides extra graphical utilities base onlattice.gridExtra provides miscellaneous functions forgrid graphics (and consequently forggplot2 which is based ongrid).gridtext provides improved text rendering support forgrid graphics. Many packages offerggplot2 extensions, (typically starting with 'gg') such asggExtra,ggalt,ggforce,ggmap,ggtext,ggthemes,ggrepel,ggridges,ggfortify,ggstatsplot,ggeffects,ggsignif,GGally,ggcorrplot,ggdendro, etc.. Users in desperate need for greater performance may also find the (unmaintained)lwplot package useful that provides a faster and lighter version ofggplot2 withdata.table backend.

• tidytable: A tidy interface todata.table that isrlang compatible. Quite comprehensive implementation ofdplyr,tidyr andpurr functions. Package uses a classtidytable that inherits fromdata.table. Thedt() function makesdata.table syntax pipeable (12 total dependencies).

• dtplyr: A tidy interface todata.table built around lazy evaluation i.e. users need to callas.data.table(),as.data.frame() oras_tibble() to access the results. Lazy evaluation holds the potential of generating more performantdata.table code (20 dependencies).

• tidyfst: Tidy verbs for fast data manipulation. Coversdplyr and sometidyr functionality. Functions have_dt suffix and preservedata.table object. Acheatsheet is provided (7 dependencies).

• tidyft: Tidy verbs for fast data operations by reference. Best for big data manipulation on out of memory data using facilities provided byfst (7 dependencies).

• tidyfast: Fast tidying of data. Coverstidyr functionality,dt_ prefix, preservesdata.table object (2 dependencies).

• maditr: Fast data aggregation, modification, and filtering with pipes anddata.table. Minimal implementation with functionslet() andtake() for most common data manipulation tasks. Also provides Excel-like lookup functions (2 dependencies).

• table.express also o buildsdata.table expressions fromdplyr verbs, without executing them eagerly. Similar todtplyr but less mature (17 dependencies).

• r-polars provides an R-port to the impressively fastpolars DataFrame's library written in Rust (1 dependencies).

• tidypolars is a python library built on top ofpolars that gives access to methods and functions familiar to R tidyverse users.

• Tidier.jl provides a Julia implementation of the tidyverse mini-language in Julia. Powered by theDataFrames.jl library.

• fst: A compressed data file format that is very fast to read and write. Full random access in both rows and columns allows reading subsets from a '.fst' file (2 dependencies).

• qs provides a lightning-fast and complete replacement for thesaveRDS andreadRDS functions in R. Itsupports general R objects with attributes and references - at similar speeds tofst - but does not provide on-disk random access to data subsets likefst (4 dependencies).

• arrow provides both a low-level interface to the Apache Arrow C++ library (a multi-language toolbox for accelerated data interchange and in-memory processing) including fast reading / writing delimited files, efficient storage of data as.parquet or.feather files, efficient (lazy) queries and computations, and sharing data between R and Python (14 dependencies). It provides methods for severaldplyr functions allowing highly efficient data manipulation on arrow datasets. Check out theuseR2022 workshop on working with larger than memory data with apache arrow in R, and theapache arrow R cookbook as well as theawesome-arrow-r repository.

• duckdb: DuckDB is a high-performance analytical database system that can be used on in-memory or out-of memory data (including csv,.parquet files, arrow datasets, and it's own.duckdb format), and that provides a rich SQL dialect and optimized query execution for data analysis (1 dependency). It can also be used with thedbplyr package that translatesdplyr code to SQL.This Article by Christophe Nicault (October 2022) demonstrates the integration ofduckdb with R andarrow. Also see theofficial docs.

• vroom provides fast reading of delimited files (23 dependencies).

  Notes:data.table providesfread andfwrite for fast reading of delimited files.

• nCompiler: Compiles R functions to C++, and covers basic math, distributions, vectorized math and linear algebra, as well as basic control flow. R and Compiled C++ functions can also be jointly utilized in the a class 'nClass' that inherits from R6. An in-progressuser-manual provides an overview of the package.

• ast2ast: Also compiles R functions to C++, and is very straightforward to use (it has a single functiontranslate() to compile R functions), but less flexible thannCompiler (e.g. it currently does not support linear algebra).Available on CRAN (6 dependencies).

• odin: Implements R to C translation and compilation, but specialized for differentialequation solving problems.Available on CRAN (8 dependencies).

• armacmp translates linear algebra code written in R to C++ using the Armadillo Template Library. The package can also be used to write mathematical optimization routines that are translated and optimized in C++ usingRcppEnsmallen.

• r2c provides compilation of R functions to be applied over many groups (e.g. grouped bivariate linear regression etc.).

• FastR is a high-performance implementation of the entire R programming language, that can JIT compile R code to run on theGraal VM.

• inline allows users to write C, C++ or Fortran functions and compile them directly to an R function for use within the R session.Available on CRAN (0 dependencies).

  Notes: Many of these projects are experimental and not available as CRAN packages.

• R's C API is the most natural way to extend R and does not require additional packages. It is further documented in theWriting R Extensions Manual, theR Internals Manual, ther-internals repository and sometimes referred to in theR Blog (and some other Blogs on the web). Users willing to extend R in this way should familiarize themselves with R's garbage collection andPROTECT Errors.

• Rcpp provides seamless R and C++ integration, and is widely used to extend R with C++. Compared to the C API compile time is slower and object files are larger, but users don't need to worry about garbage collection and can use modern C++ as well as a rich set of R-flavored functions and classes (0 dependencies).

• cpp11 provides a simpler, header-only R binding to C++ that allows faster compile times andseveral other enhancements (0 dependencies).

• tidyCpp provides a tidy C++ wrapping of the C API of R - to make the C API more amenable to C++ programmers (0 dependencies).

• JuliaCall Provides an R interface to the Julia programming language (11 dependencies). Other interfaces are provided byXRJulia (2 dependencies) andJuliaConnectoR (0 dependencies).

• rextendr provides an R interface to the Rust programming language (29 dependencies).

• rJava provides an R interface to Java (0 dependencies).

  Notes: There are many Rcpp extension packages binding R to powerful C++ libraries, such as linear algebra throughRcppArmadillo andRcppEigen, thread-safe parallelism throughRcppParallel etc.

• See theHigh-Performance and Parallel Computing Task View and thefutureverse.

Please notify me of any other packages you think should be included here. Such packages should be well designed, top-performing, low-dependency, and, with few exceptions, provide own compiled code. Please note that thefastverse focuses on general purpose statistical computing and data manipulation, thus I won't include fast packages to estimate specific kinds of models here (of which R also has a great many).