Visualization of a Correlation Matrix using 'ggplot2'

The 'ggcorrplot' package can be used to visualize easily a correlation matrix using 'ggplot2'. It provides a solution for reordering the correlation matrix and displays the significance level on the plot. It also includes a function for computing a matrix of correlation p-values.

Data Visualization Tools for Statistical Analysis Results

Unified plotting tools for statistics commonly used, such as GLM, time series, PCA families, clustering and survival analysis. The package offers a single plotting interface for these analysis results and plots in a unified style using 'ggplot2'.

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 gradient boosting (XGBoost) and transformer-based methods (Niederhametner et al., ). General background and practical guidance on imputation are provided in the Springer book by Templ (2023) .

Functions for Visualising Simple Graphs (Networks), Plotting Flow Diagrams

Visualises simple graphs (networks) based on a transition matrix, utilities to plot flow diagrams, visualising webs, electrical networks, etc. Support for the book "A practical guide to ecological modelling - using R as a simulation platform" by Karline Soetaert and Peter M.J. Herman (2009), Springer. and the book "Solving Differential Equations in R" by Karline Soetaert, Jeff Cash and Francesca Mazzia (2012), Springer. Includes demo(flowchart), demo(plotmat), demo(plotweb).

Model Visualisation Toolbox for 'easystats' and 'ggplot2'

Provides plotting utilities supporting packages in the 'easystats' ecosystem (< https://github.com/easystats/easystats>) and some extra themes, geoms, and scales for 'ggplot2'. Color scales are based on < https://materialui.co/>. References: Lüdecke et al. (2021) .

Visualizations of High-Dimensional Data

Gives access to data visualisation methods that are relevant from the data scientist's point of view. The flagship idea of 'DataVisualizations' is the mirrored density plot (MD-plot) for either classified or non-classified multivariate data published in Thrun, M.C. et al.: "Analyzing the Fine Structure of Distributions" (2020), PLoS ONE, . The MD-plot outperforms the box-and-whisker diagram (box plot), violin plot and bean plot and geom_violin plot of ggplot2. Furthermore, a collection of various visualization methods for univariate data is provided. In the case of exploratory data analysis, 'DataVisualizations' makes it possible to inspect the distribution of each feature of a dataset visually through a combination of four methods. One of these methods is the Pareto density estimation (PDE) of the probability density function (pdf). Additionally, visualizations of the distribution of distances using PDE, the scatter-density plot using PDE for two variables as well as the Shepard density plot and the Bland-Altman plot are presented here. Pertaining to classified high-dimensional data, a number of visualizations are described, such as f.ex. the heat map and silhouette plot. A political map of the world or Germany can be visualized with the additional information defined by a classification of countries or regions. By extending the political map further, an uncomplicated function for a Choropleth map can be used which is useful for measurements across a geographic area. For categorical features, the Pie charts, slope charts and fan plots, improved by the ABC analysis, become usable. More detailed explanations are found in the book by Thrun, M.C.: "Projection-Based Clustering through Self-Organization and Swarm Intelligence" (2018) .

Treemap Visualization

A treemap is a space-filling visualization of hierarchical structures. This package offers great flexibility to draw treemaps.

Interface to 'D3' Visualizations

Suite of tools for using 'D3', a library for producing dynamic, interactive data visualizations. Supports translating objects into 'D3' friendly data structures, rendering 'D3' scripts, publishing 'D3' visualizations, incorporating 'D3' in R Markdown, creating interactive 'D3' applications with Shiny, and distributing 'D3' based 'htmlwidgets' in R packages.

Path Diagrams and Visual Analysis of Various SEM Packages' Output

Path diagrams and visual analysis of various SEM packages' output.

Trellis Graphics for R

A powerful and elegant high-level data visualization system inspired by Trellis graphics, with an emphasis on multivariate data. Lattice is sufficient for typical graphics needs, and is also flexible enough to handle most nonstandard requirements. See ?Lattice for an introduction.