Support for Parallel Computation, Logging, and Function Automation

Support for parallel computation with progress bar, and option to stop or proceed on errors. Also provides logging to console and disk, and the logging persists in the parallel threads. Additional functions support function call automation with delayed execution (e.g. for executing functions in parallel).

Extension of `data.frame`

Fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns, friendly and fast character-separated-value read/write. Offers a natural and flexible syntax, for faster development.

Simple Features for R

Support for simple feature access, a standardized way to encode and analyze spatial vector data. Binds to 'GDAL' for reading and writing data, to 'GEOS' for geometrical operations, and to 'PROJ' for projection conversions and datum transformations. Uses by default the 's2' package for geometry operations on geodetic (long/lat degree) coordinates.

ANSI Control Sequence Aware String Functions

Counterparts to R string manipulation functions that account for the effects of ANSI text formatting control sequences.

Seamless R and C++ Integration

The 'Rcpp' package provides R functions as well as C++ classes which offer a seamless integration of R and C++. Many R data types and objects can be mapped back and forth to C++ equivalents which facilitates both writing of new code as well as easier integration of third-party libraries. Documentation about 'Rcpp' is provided by several vignettes included in this package, via the 'Rcpp Gallery' site at < https://gallery.rcpp.org>, the paper by Eddelbuettel and Francois (2011, ), the book by Eddelbuettel (2013, ) and the paper by Eddelbuettel and Balamuta (2018, ); see 'citation("Rcpp")' for details.

Asynchronous Disk-Based Representation of Massive Data

Storing very large data objects on a local drive, while still making it possible to manipulate the data in an efficient manner.

A 'dplyr' Back End for Databases

A 'dplyr' back end for databases that allows you to work with remote database tables as if they are in-memory data frames. Basic features work with any database that has a 'DBI' back end; more advanced features require 'SQL' translation to be provided by the package author.

Functional Data Analysis

These functions were developed to support functional data analysis as described in Ramsay, J. O. and Silverman, B. W. (2005) Functional Data Analysis. New York: Springer and in Ramsay, J. O., Hooker, Giles, and Graves, Spencer (2009). Functional Data Analysis with R and Matlab (Springer). The package includes data sets and script files working many examples including all but one of the 76 figures in this latter book. Matlab versions are available by ftp from < https://www.psych.mcgill.ca/misc/fda/downloads/FDAfuns/>.

Flexibly Reshape Data

Flexibly restructure and aggregate data using just two functions: melt and cast.

Export Data Frames to Excel 'xlsx' Format

Zero-dependency data frame to xlsx exporter based on 'libxlsxwriter' < https://libxlsxwriter.github.io>. Fast and no Java or Excel required.