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Tools for Splitting, Applying and Combining Data
A set of tools that solves a common set of problems: you need to break a big problem down into manageable pieces, operate on each piece and then put all the pieces back together. For example, you might want to fit a model to each spatial location or time point in your study, summarise data by panels or collapse high-dimensional arrays to simpler summary statistics. The development of 'plyr' has been generously supported by 'Becton Dickinson'.
A Simple and Robust JSON Parser and Generator for R
A reasonably fast JSON parser and generator, optimized for statistical data and the web. Offers simple, flexible tools for working with JSON in R, and is particularly powerful for building pipelines and interacting with a web API. The implementation is based on the mapping described in the vignette (Ooms, 2014). In addition to converting JSON data from/to R objects, 'jsonlite' contains functions to stream, validate, and prettify JSON data. The unit tests included with the package verify that all edge cases are encoded and decoded consistently for use with dynamic data in systems and applications.
Geographic Data Analysis and Modeling
Reading, writing, manipulating, analyzing and modeling of spatial data. This package has been superseded by the "terra" package < https://CRAN.R-project.org/package=terra>.
Client Library for SpatioTemporal Asset Catalog
Provides functions to access, search and download spacetime earth
observation data via SpatioTemporal Asset Catalog (STAC). This package
supports the version 1.0.0 (and older) of the STAC specification
(< https://github.com/radiantearth/stac-spec>).
For further details see Simoes et al. (2021)
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".
Read, Write and Edit xlsx Files
Simplifies the creation of Excel .xlsx files by providing a high level interface to writing, styling and editing worksheets. Through the use of 'Rcpp', read/write times are comparable to the 'xlsx' and 'XLConnect' packages with the added benefit of removing the dependency on Java.
Data Validation Infrastructure
Declare data validation rules and data quality indicators;
confront data with them and analyze or visualize the results.
The package supports rules that are per-field, in-record,
cross-record or cross-dataset. Rules can be automatically
analyzed for rule type and connectivity. Supports checks implied
by an SDMX DSD file as well. See also Van der Loo
and De Jonge (2018)
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.