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 This package has been superseded by the "terra" package 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>.

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.

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.

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 . Development of this package was supported in part by the National Science Foundation Grant 1417857, the National Center for Atmospheric Research, and Colorado School of Mines. See the Fields URL for a vignette on using this package and some background on spatial statistics.

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) , Chapter 6 and the JSS paper (2021) .

"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".

Scale Functions for Visualization

Graphical scales map data to aesthetics, and provide methods for automatically determining breaks and labels for axes and legends.

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.

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).