R-Pakete für Statistik und Data Science
For R-Paket für statistische Datenanalyse, the strongest matches are tidyverse/dplyr (dplyr is a core R package for data manipulation), hadley/ggplot2 (ggplot2 is a core R package for statistical graphics) and tidyverse/ggplot2 (ggplot2 is the premier data visualization package for R). ropensci/plotly and catboost/catboost round out the shortlist. Each is ranked by relevance to your query, popularity and recent activity.
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R-Pakete für Statistik und Data Science
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- tidyverse/dplyr
tidyverse/dplyr
5,034Auf GitHub ansehendplyr is an R data manipulation library that provides a grammar for transforming tabular data frames. It functions as an in-memory data frame processor and a relational data algebra tool, using a consistent set of verbs to filter, select, and summarize data. The project includes a SQL translation engine that converts high-level data manipulation expressions into optimized queries. This allows users to perform transformations directly on remote relational databases and cloud storage without pulling data locally. The library covers a broad range of tabular operations, including column mutation
dplyr is a core R package for data manipulation and transformation, tightly integrated with the tidyverse, making it an essential tool for data science workflows, though it does not directly handle statistical modeling, visualization, or machine learning.
RTidy Data StructuringColumn Selection Utilities - hadley/ggplot2
hadley/ggplot2
6,948Auf GitHub ansehenggplot2 is an R data visualization library and statistical graphics engine. It implements a grammar of graphics that functions as a declarative plotting framework, allowing users to specify what a plot should contain rather than how to draw it. The system builds visualizations by mapping data variables to visual aesthetics through a structured set of layering rules. This approach enables the composition of complex graphics by stacking independent components, such as geometric objects and scales, on top of a shared coordinate system. The framework supports scientific plotting and exploratory
ggplot2 is a core R package for statistical graphics and exploratory data analysis, fitting squarely within the data science ecosystem you are seeking, though its primary strength is visualization rather than the full range of modeling and manipulation features you listed.
RData Visualization - tidyverse/ggplot2
tidyverse/ggplot2
6,948Auf GitHub ansehenggplot2 is a data visualization library for R based on a formal grammar of graphics. It provides a declarative plotting framework that allows users to create complex graphics by combining geometric objects, statistical summaries, and coordinate systems. The system is distinguished by a layered approach to composition, where visualizations are built incrementally by stacking independent geometric, statistical, and coordinate layers. It utilizes a hierarchical styling engine to manage non-data elements such as backgrounds, fonts, and margins, and includes a multi-panel faceting tool for splitti
ggplot2 is the premier data visualization package for R, built on the grammar of graphics, and is a core part of the tidyverse ecosystem—it delivers declarative, reproducible plotting that perfectly fits the visualization and reproducible research aspects of the intent, though it does not itself handle data manipulation, statistical modeling, or machine learning.
RDeclarative Visualization GrammarsLayered Visualization CompositionChart Composition Layers - ropensci/plotly
ropensci/plotly
2,671Auf GitHub ansehenAn interactive graphing library for R
ropensci/plotly is an interactive graphing library for R that directly addresses your needs for data visualization and interactive documents, making it a useful tool for data science and reproducible research within the R ecosystem.
RHtml WidgetsR VisualizationR Visualization Packages - catboost/catboost
catboost/catboost
8,808Auf GitHub ansehenCatBoost is a gradient boosting machine learning library used to train decision tree ensembles for regression, classification, and ranking tasks. It functions as a high-performance framework that provides a categorical data processor for transforming non-numeric features, a distributed trainer for large-scale datasets, and GPU acceleration to speed up model construction. The library distinguishes itself through native handling of categorical data and text features, removing the need for manual encoding. It includes a specialized model interpretability tool that leverages SHAP values and featu
CatBoost offers R users a high-performance gradient boosting library for classification, regression, and ranking tasks, fitting the machine learning part of statistical computing, but it is narrowly focused on that one algorithm and does not cover data manipulation, visualization, tidyverse integration, or the broader reproducible research tooling the search requests.
C++Gradient BoostingGradient Boosting LibrariesModel Training
