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.

Spectral Transmittance and Spectral Reflectance Data

Spectral 'transmittance' data for frequently used filters and similar materials. Plastic sheets and films; photography filters; theatrical gels; machine-vision filters; various types of window glass; optical glass and some laboratory plastics and glassware. Spectral reflectance data for frequently encountered materials. Part of the 'r4photobiology' suite, Aphalo P. J. (2015) .

Convert Data from and to 'GeoJSON' or 'TopoJSON'

Convert data to 'GeoJSON' or 'TopoJSON' from various R classes, including vectors, lists, data frames, shape files, and spatial classes. 'geojsonio' does not aim to replace packages like 'sp', 'rgdal', 'rgeos', but rather aims to be a high level client to simplify conversions of data from and to 'GeoJSON' and 'TopoJSON'.

Manipulate and Visualize VCF Data

Facilitates easy manipulation of variant call format (VCF) data. Functions are provided to rapidly read from and write to VCF files. Once VCF data is read into R a parser function extracts matrices of data. This information can then be used for quality control or other purposes. Additional functions provide visualization of genomic data. Once processing is complete data may be written to a VCF file (*.vcf.gz). It also may be converted into other popular R objects (e.g., genlight, DNAbin). VcfR provides a link between VCF data and familiar R software.

Presentation-Ready Data Summary and Analytic Result Tables

Creates presentation-ready tables summarizing data sets, regression models, and more. The code to create the tables is concise and highly customizable. Data frames can be summarized with any function, e.g. mean(), median(), even user-written functions. Regression models are summarized and include the reference rows for categorical variables. Common regression models, such as logistic regression and Cox proportional hazards regression, are automatically identified and the tables are pre-filled with appropriate column headers.

Multivariate Imputation by Chained Equations

Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm as described in Van Buuren and Groothuis-Oudshoorn (2011) . Each variable has its own imputation model. Built-in imputation models are provided for continuous data (predictive mean matching, normal), binary data (logistic regression), unordered categorical data (polytomous logistic regression) and ordered categorical data (proportional odds). MICE can also impute continuous two-level data (normal model, pan, second-level variables). Passive imputation can be used to maintain consistency between variables. Various diagnostic plots are available to inspect the quality of the imputations.

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 standardizes coordinates, squared cosines, contributions, and eigenvalues from several analysis backends and produces 'ggplot2'-based factor maps, scree plots, contribution plots, clustering diagnostics, dendrograms, and silhouette plots. It also visualizes two-dimensional 'UMAP' and t-SNE embeddings and adapts precomputed or 'tidymodels' principal-component results for the same plotting interface.

Combine Multidimensional Arrays

Combine multidimensional arrays into a single array. This is a generalization of 'cbind' and 'rbind'. Works with vectors, matrices, and higher-dimensional arrays (aka tensors). Also provides functions 'adrop', 'asub', and 'afill' for manipulating, extracting and replacing data in arrays.

Data Analysis Using Regression and Multilevel/Hierarchical Models

Functions to accompany A. Gelman and J. Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2007.

Various R Programming Tools for Data Manipulation

Various R programming tools for data manipulation, including medical unit conversions, combining objects, character vector operations, factor manipulation, obtaining information about R objects, generating fixed-width format files, extracting components of date & time objects, operations on columns of data frames, matrix operations, operations on vectors, operations on data frames, value of last evaluated expression, and a resample() wrapper for sample() that ensures consistent behavior for both scalar and vector arguments.