Search Results for "complex"
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Showing 9 open source projects for "complex"
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1
dplyr
dplyr: A grammar of data manipulation
dplyr is an R package that provides a consistent and intuitive grammar for data manipulation, enabling users to filter, arrange, summarize, and transform data efficiently. Part of the tidyverse ecosystem, dplyr simplifies complex data operations through a clear and readable syntax, whether working with data frames, tibbles, or databases. It is widely used in data science and statistical analysis workflows. -
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ggraph
Grammar of Graph Graphics
ggraph adapts the Grammar of Graphics from ggplot2 for network and graph visualizations. It integrates with tidygraph/igraph data structures, providing a wide range of geoms, layouts (e.g. hive plots, circle packing), and layering methods tailored to hierarchical or relational data. -
3
JuliaConnectoR
A functionally oriented interface for calling Julia from R
...From a technical perspective, R data structures are serialized with an optimized custom streaming format, sent to a (local) Julia TCP server, and translated to Julia data structures by Julia. The results of function calls are likewise translated back to R. Complex Julia structures can either be used by reference via proxy objects in R or fully translated to R data structures. -
4
plotly
An interactive graphing library for R
This part of the book teaches you how to leverage the plotly R package to create a variety of interactive graphics. There are two main ways to creating a plotly object: either by transforming a ggplot2 object (via ggplotly()) into a plotly object or by directly initializing a plotly object with plot_ly()/plot_geo()/plot_mapbox(). Both approaches have somewhat complementary strengths and weaknesses, so it can pay off to learn both approaches. Moreover, both approaches are an implementation of... -
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5
brms
brms R package for Bayesian generalized multivariate models using Stan
brms is an R package by Paul Bürkner which provides a high-level interface for fitting Bayesian multilevel (i.e. mixed effects) models, generalized linear / non-linear / multivariate models using Stan as the backend. It allows R users to specify complex Bayesian models using formula syntax similar to lme4 but with far more flexibility (distributions, link functions, hierarchical structure, nonlinear terms, etc.). It supports model diagnostics, posterior predictive checking, model comparison, custom priors, and advanced features such as distributional regression. -
6
mlr
Machine Learning in R
...The framework provides supervised methods like classification, regression, and survival analysis along with their corresponding evaluation and optimization methods, as well as unsupervised methods like clustering. It is written in a way that you can extend it yourself or deviate from the implemented convenience methods and construct your own complex experiments or algorithms. -
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ComplexHeatmap
Make Complex Heatmaps
ComplexHeatmap is an R/Bioconductor package by Zuguang Gu et al. designed to create highly flexible, complex, richly annotated heatmaps and related visualizations. It allows arranging multiple heatmaps, adding annotations, combining heatmaps, customizing colors, layouts, and integrating other plots. Often used in genomics/bioinformatics to show expression, methylation, etc., with sidebars, annotations, clustering, etc. Highly customizable layout: combining different heatmaps, arranging and splitting, dealing with multiple heatmap merges, combining with other plots etc. ... -
8
Mastering Shiny
Mastering Shiny: a book
Mastering Shiny is a book (and its accompanying source repository) by Hadley Wickham that teaches people how to build interactive web applications using Shiny in R. It starts from basics (your first app, UI components, reactivity) and progresses to more advanced topics (dynamic UIs, modules, testing, security, performance). It is intended to help data scientists, analysts, or R users who may not have deep experience in web technologies become expert Shiny developers. The source code is open,... -
9
circlize
Circular visualization in R
...It implements many types of plots using circular layouts: chord diagrams, circular heatmaps, arcs/links between sectors, genomic data visualization, etc. It provides low-level drawing functions as well as high-level functions to build complex visualizations. It’s often used in genomics, network analysis, or other fields where relationships among categories or entities can be nicely displayed in a circular fashion. Support for circular heatmaps, multiple tracks (rings), for showing multiple layers of data per sector. Good performance and stable codebase, detailed documentation including a book on usage examples. -
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