Examples: visualization, C++, networks, data cleaning, html widgets, ropensci.

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factoextra — by Alboukadel Kassambara, 2 months ago

Extract and Visualize the Results of Multivariate Data Analyses

Provides easy-to-use functions to extract and visualize the output of multivariate data analyses, including 'PCA' (Principal Component Analysis), 'CA' (Correspondence Analysis), 'MCA' (Multiple Correspondence Analysis), 'FAMD' (Factor Analysis of Mixed Data), 'MFA' (Multiple Factor Analysis), and 'HMFA' (Hierarchical Multiple Factor Analysis) from different R packages. It also includes helpers for simplifying clustering analysis workflows and provides 'ggplot2'-based data visualization.

inlabru — by Finn Lindgren, 2 months ago

Bayesian Latent Gaussian Modelling using INLA and Extensions

Facilitates spatial and general latent Gaussian modeling using integrated nested Laplace approximation via the INLA package (< https://www.r-inla.org>). Additionally, extends the GAM-like model class to more general nonlinear predictor expressions, and implements a log Gaussian Cox process likelihood for modeling univariate and spatial point processes based on ecological survey data. Model components are specified with general inputs and mapping methods to the latent variables, and the predictors are specified via general R expressions, with separate expressions for each observation likelihood model in multi-likelihood models. A prediction method based on fast Monte Carlo sampling allows posterior prediction of general expressions of the latent variables. Ecology-focused introduction in Bachl, Lindgren, Borchers, and Illian (2019) .

mapview — by Tim Appelhans, 8 months ago

Interactive Viewing of Spatial Data in R

Quickly and conveniently create interactive visualisations of spatial data with or without background maps. Attributes of displayed features are fully queryable via pop-up windows. Additional functionality includes methods to visualise true- and false-color raster images and bounding boxes.

ggspatial — by Dewey Dunnington, 8 months ago

Spatial Data Framework for ggplot2

Spatial data plus the power of the ggplot2 framework means easier mapping when input data are already in the form of spatial objects.

ipumsr — by Derek Burk, a month ago

An R Interface for Downloading, Reading, and Handling IPUMS Data

An easy way to work with census, survey, and geographic data provided by IPUMS in R. Generate and download data through the IPUMS API and load IPUMS files into R with their associated metadata to make analysis easier. IPUMS data describing 1.4 billion individuals drawn from over 750 censuses and surveys is available free of charge from the IPUMS website < https://www.ipums.org>.

tidyfst — by Tian-Yuan Huang, 4 months ago

Tidy Verbs for Fast Data Manipulation

A toolkit of tidy data manipulation verbs with 'data.table' as the backend. Combining the merits of syntax elegance from 'dplyr' and computing performance from 'data.table', 'tidyfst' intends to provide users with state-of-the-art data manipulation tools with least pain. This package is an extension of 'data.table'. While enjoying a tidy syntax, it also wraps combinations of efficient functions to facilitate frequently-used data operations.

FeatureExtraction — by Ger Inberg, 2 months ago

Generating Features for a Cohort

An R interface for generating features for a cohort using data in the Common Data Model. Features can be constructed using default or custom made feature definitions. Furthermore it's possible to aggregate features and get the summary statistics.

editrules — by Edwin de Jonge, 5 months ago

Parsing, Applying, and Manipulating Data Cleaning Rules

Please note: active development has moved to packages 'validate' and 'errorlocate'. Facilitates reading and manipulating (multivariate) data restrictions (edit rules) on numerical and categorical data. Rules can be defined with common R syntax and parsed to an internal (matrix-like format). Rules can be manipulated with variable elimination and value substitution methods, allowing for feasibility checks and more. Data can be tested against the rules and erroneous fields can be found based on Fellegi and Holt's generalized principle. Rules dependencies can be visualized with using the 'igraph' package.

fst — by Mark Klik, 4 years ago

Lightning Fast Serialization of Data Frames

Multithreaded serialization of compressed data frames using the 'fst' format. The 'fst' format allows for full random access of stored data and a wide range of compression settings using the LZ4 and ZSTD compressors.

and — by Alexander Rossell Hayes, a month ago

Construct Natural-Language Lists with Internationalization

Construct language-aware lists. Make "and"-separated and "or"-separated lists that automatically conform to the user's language settings.