Visualizing the Performance of Scoring Classifiers

ROC graphs, sensitivity/specificity curves, lift charts, and precision/recall plots are popular examples of trade-off visualizations for specific pairs of performance measures. ROCR is a flexible tool for creating cutoff-parameterized 2D performance curves by freely combining two from over 25 performance measures (new performance measures can be added using a standard interface). Curves from different cross-validation or bootstrapping runs can be averaged by different methods, and standard deviations, standard errors or box plots can be used to visualize the variability across the runs. The parameterization can be visualized by printing cutoff values at the corresponding curve positions, or by coloring the curve according to cutoff. All components of a performance plot can be quickly adjusted using a flexible parameter dispatching mechanism. Despite its flexibility, ROCR is easy to use, with only three commands and reasonable default values for all optional parameters.

Visualizing Categorical Data

Visualization techniques, data sets, summary and inference procedures aimed particularly at categorical data. Special emphasis is given to highly extensible grid graphics. The package was package was originally inspired by the book "Visualizing Categorical Data" by Michael Friendly and is now the main support package for a new book, "Discrete Data Analysis with R" by Michael Friendly and David Meyer (2015).

Visualizations of Paired Comparisons

Convert a logical vector or a vector of p-values or a correlation, difference, or distance matrix into a display identifying the pairs for which the differences were not significantly different. Designed for use in conjunction with the output of functions like TukeyHSD, dist (stats), simint, simtest, csimint, csimtest (multcomp), friedmanmc, kruskalmc (pgirmess).

Classification and Visualization

Miscellaneous functions for classification and visualization, e.g. regularized discriminant analysis, sknn() kernel-density naive Bayes, an interface to 'svmlight' and stepclass() wrapper variable selection for supervised classification, partimat() visualization of classification rules and shardsplot() of cluster results as well as kmodes() clustering for categorical data, corclust() variable clustering, variable extraction from different variable clustering models and weight of evidence preprocessing.

Spatial Visualization with ggplot2

A collection of functions to visualize spatial data and models on top of static maps from various online sources (e.g Google Maps and Stamen Maps). It includes tools common to those tasks, including functions for geolocation and routing.

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.

Graph Plotting Methods, Psychometric Data Visualization and Graphical Model Estimation

Weighted network visualization and analysis, as well as Gaussian graphical model computation. See Epskamp et al. (2012) .

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.

Visualization and Imputation of Missing Values

Provides methods for imputation and visualization of missing values. It includes graphical tools to explore the amount, structure and patterns of missing and/or imputed values, supporting exploratory data analysis and helping to investigate potential missingness mechanisms (details in Alfons, Templ and Filzmoser, ). The quality of imputations can be assessed visually using a wide range of univariate, bivariate and multivariate plots. The package further provides several imputation methods, including efficient implementations of k-nearest neighbour and hot-deck imputation (Kowarik and Templ 2013, ), iterative robust model-based multiple imputation (Templ 2011, ; Templ 2023, ), and machine learning–based approaches such as robust GAM-based multiple imputation (Templ 2024, ) as well as random forest and gradient boosting (XGBoost) imputation (Niederhametner et al., ). General background and practical guidance on imputation are provided in the Springer book by Templ (2023) .

Visualize R Data Structures with Trees

A set of tools for inspecting and understanding R data structures inspired by str(). Includes ast() for visualizing abstract syntax trees, ref() for showing shared references, cst() for showing call stack trees, and obj_size() for computing object sizes.