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Create Elegant Data Visualisations Using the Grammar of Graphics
A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.
Tools for Spatial Data
For curve, surface and function fitting with an emphasis
on splines, spatial data, geostatistics, and spatial statistics. The major
methods
include Gaussian spatial process prediction (known as Kriging), cubic and thin plate splines, and compactly supported
covariance functions for large data sets. The spline and spatial process
methods are
supported by functions that can determine the smoothing parameter
(nugget and sill variance) and other covariance function parameters by cross
validation and also by maximum likelihood. For spatial process prediction
there is an easy to use function that also estimates the correlation
scale (range parameter). A major feature is that any covariance function
implemented in R and following a simple format can be used for
spatial prediction. As included are fast approximations for prediction
and conditional simulation for larger data sets.
There are also many useful functions for plotting
and working with spatial data as images. This package also contains
an implementation of sparse matrix methods for large spatial data
sets based the R sparse matrix package spam. Use
help(fields) to get started and for an overview. All package graphics functions
focus on extending base R graphics and are easy to interpret and modify.
The fields source
code is deliberately commented and provides useful explanations of
numerical details as a companion to the manual pages. The commented
source code can be viewed by expanding the source code version of this package
and looking in the R subdirectory. The reference for fields can be generated
by the citation function in R and has DOI
"Finding Groups in Data": Cluster Analysis Extended Rousseeuw et al.
Methods for Cluster analysis. Much extended the original from Peter Rousseeuw, Anja Struyf and Mia Hubert, based on Kaufman and Rousseeuw (1990) "Finding Groups in Data".
Data and Function to Work with Emojis
Contains data about emojis with relevant metadata, and functions to work with emojis when they are in strings.
Scale Functions for Visualization
Graphical scales map data to aesthetics, and provide methods for automatically determining breaks and labels for axes and legends.
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.
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,
Flexibly Reshape Data
Flexibly restructure and aggregate data using just two functions: melt and cast.
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 works with any database that has a 'DBI' back end; more advanced features require 'SQL' translation to be provided by the package author.
Flexibly Reshape Data: A Reboot of the Reshape Package
Flexibly restructure and aggregate data using just two functions: melt and 'dcast' (or 'acast').