- 39

tidybayes is an R package that aims to make it easy to integrate popular Bayesian modeling methods into a tidy data + ggplot workflow. Composing data for use with the model. This often means translating data from a data.frame into a list , making sure factors are encoded as numerical data, adding variables to store the length of indices, etc. This package helps automate these operations using the compose_data function, which automatically handles data types like numeric, logical, factor, and ordinal, and allows easy extensions for converting other datatypes into a format the model understands by providing your own implementation of the generic as_data_list.

http://mjskay.github.io/tidybayeshttps://github.com/mjskay/tidybayes

Tags | r tidy-data bayesian-data-analysis r-package visualization ggplot2 stan jags |

Implementation | R |

License | GPL |

Platform |

RStan is the R interface to Stan. RStan's source code repository is hosted here on GitHub. Stan's source repository is defined as a submodule. See how to work with stan submodule in rstan repo.

stan mcmc bayesian-inference bayesian-data-analysis bayesian-statistics r r-packageThe brms package provides an interface to fit Bayesian generalized (non-)linear multivariate multilevel models using Stan, which is a C++ package for performing full Bayesian inference (see http://mc-stan.org/). The formula syntax is very similar to that of the package lme4 to provide a familiar and simple interface for performing regression analyses. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include non-linear and smooth terms, auto-correlation structures, censored data, missing value imputation, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Multivariate models (i.e. models with multiple response variables) can be fitted, as well. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with posterior predictive checks, leave-one-out cross-validation, and Bayes factors. As a simple example, we use poisson regression to model the seizure counts in epileptic patients to investigate whether the treatment (represented by variable Trt) can reduce the seizure counts and whether the effect of the treatment varies with the baseline number of seizures a person had before treatment (variable log_Base4_c). As we have multiple observations per person, a group-level intercept is incorporated to account for the resulting dependency in the data.

brms stan bayesian-inference multilevel-models statistical-models r-packageUsing tidy data principles can make many text mining tasks easier, more effective, and consistent with tools already in wide use. Much of the infrastructure needed for text mining with tidy data frames already exists in packages like dplyr, broom, tidyr and ggplot2. In this package, we provide functions and supporting data sets to allow conversion of text to and from tidy formats, and to switch seamlessly between tidy tools and existing text mining packages. Check out our book to learn more about text mining using tidy data principles. This function uses the tokenizers package to separate each line into words. The default tokenizing is for words, but other options include characters, n-grams, sentences, lines, paragraphs, or separation around a regex pattern.

text-mining r tidyverse tidy-data natural-language-processingThere are interfaces available in R, Python, MATLAB, Julia, Stata, Mathematica, and for the command line. There are separate repositories in the stan-dev GitHub organization for the interfaces, higher-level libraries and lower-level libraries.

stan bayesian-inference bayesian bayesian-methods bayesian-statistics bayesian-data-analysisFor more details on the workflow and theory underpinning naniar, read the vignette Getting started with naniar. For a short primer on the data visualisation available in naniar, read the vignette Gallery of Missing Data Visualisations.

missing-data data-visualisation ggplot2 missingness tidy-data r-packageggforce is a package aimed at providing missing functionality to ggplot2 through the extension system introduced with ggplot2 v2.0.0. Broadly speaking ggplot2 has been aimed primarily at explorative data visualization in order to investigate the data at hand, and less at providing utilities for composing custom plots a la D3.js. ggforce is mainly an attempt to address these "shortcoming" (design choices might be a better description). The goal is to provide a repository of geoms, stats, etc. that are as well documented and implemented as the official ones found in ggplot2. The inclusion of any geom, stat, position etc in ggforce is not necessarily a recommendation of their use. ggplot2 has been successful in being opinionated about what functionality should be available. This is good as it insulates the user from making bad decisions when analyzing their data (to a certain degree), but it also makes it difficult to develop novel visualizations using the ggplot2 API. ggforce on the other hand positions itself closer to the "anything goes - the user is responsible for the quality of the output". Be very aware of this responsibility! Bad visualizations lie about, distorts, and obscure the data behind them, both to you and the ones you share your visualizations with.

visualization rstats ggplot2 ggplot-extensionggpage is a package to create pagestyled visualizations of text based data. It uses ggplot2 and final returns are ggplot2 objects. In this new version I have worked to include a lot of use cases that wasn’t available in the first version. These new elements are previewed in the vignette.

ggplot2 r rstats datavisualization dataviz data-visualizationAn R package for creating interactive web graphics via the open source JavaScript graphing library plotly.js.NOTE: The CRAN version of plotly is designed to work with the CRAN version of ggplot2, but at least for the time being, we recommend using the development versions of both plotly and ggplot2 (devtools::install_github("hadley/ggplot2")).

r ggplot2 data-visualization d3js shiny plotly webglAn R package for creating interactive web graphics via the open source JavaScript graphing library plotly.js. Moreover, since ggplotly() returns a plotly object, you can apply essentially any function from the R package on that object. Some useful ones include layout() (for customizing the layout), add_traces() (and its higher-level add_*() siblings, for example add_polygons(), for adding new traces/data), subplot() (for combining multiple plotly objects), and plotly_json() (for inspecting the underlying JSON sent to plotly.js).

webgl ggplot2 r shiny plotly data-visualization rstats r-package d3jsThis is a very focused package that provides typography-centric themes and theme components for ggplot2. It’s a an extract/riff of hrbrmisc created by request. The core theme: theme_ipsum (“ipsum” is Latin for “precise”) uses Arial Narrow which should be installed on practically any modern system, so it’s “free”-ish. This font is condensed, has solid default kerning pairs and geometric numbers. That’s what I consider the “font trifecta” must-have for charts. An additional quality for fonts for charts is that they have a diversity of weights. Arial Narrow (the one on most systems, anyway) does not have said diversity but this quality is not (IMO) a “must have”.

r rstats ggplot2 datavisualization visualization data-visualization ggplot2-themes ggplot-extension ggplot2-scalesggplot2 by Hadley Wickham is an excellent and flexible package for elegant data visualization in R. However the default generated plots requires some formatting before we can send them for publication. Furthermore, to customize a ggplot, the syntax is opaque and this raises the level of difficulty for researchers with no advanced R programming skills. The 'ggpubr' package provides some easy-to-use functions for creating and customizing 'ggplot2'- based publication ready plots.

ggraph is an extension of ggplot2 aimed at supporting relational data structures such as networks, graphs, and trees. While it builds upon the foundation of ggplot2 and its API it comes with its own self-contained set of geoms, facets, etc., as well as adding the concept of layouts to the grammar. All of the tree concepts has been discussed in detail in dedicated blog posts that are also available as vignettes in the package. Please refer to these for more information.

graph-visualization ggplot2 visualization network-visualization rTidy data describes a standard way of storing data that is used wherever possible throughout the tidyverse. If you ensure that your data is tidy, you’ll spend less time fighting with the tools and more time working on your analysis. gather() takes multiple columns, and gathers them into key-value pairs: it makes “wide” data longer.

r tidy-dataIt uses ggplot2 and returns a ggplot2 object.

r waffle-charts square-pie-charts rstats ggplot2 datavisualization data-visualisation data-visualizationFor a detailed introduction, please see vignette("broom"). broom tidies 100+ models from popular modelling packages and almost all of the model objects in the stats package that comes with base R. vignette("available-methods") lists method availabilty.

r tidy-data modelingThe broom package takes the messy output of built-in functions in R, such as lm, nls, or t.test, and turns them into tidy data frames.

to interactively run the IPython Notebooks in the browser. This repository contains some Python demos for the book Bayesian Data Analysis, 3rd ed by Gelman, Carlin, Stern, Dunson, Vehtari, and Rubin (BDA3).

bayesian-data-analysis bayesian-inference bayesian mcmc stanggplot2 is a system for declaratively creating graphics, based on The Grammar of Graphics. You provide the data, tell ggplot2 how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details. It’s hard to succinctly describe how ggplot2 works because it embodies a deep philosophy of visualisation. However, in most cases you start with ggplot(), supply a dataset and aesthetic mapping (with aes()). You then add on layers (like geom_point() or geom_histogram()), scales (like scale_colour_brewer()), faceting specifications (like facet_wrap()) and coordinate systems (like coord_flip()).

r visualisation data-visualisationThis introductory data science course that is our (working) answer to these questions. The courses focuses on data acquisition and wrangling, exploratory data analysis, data visualization, and effective communication and approaching statistics from a model-based, instead of an inference-based, perspective. A heavy emphasis is placed on a consitent syntax (with tools from the tidyverse), reproducibility (with R Markdown) and version control and collaboration (with git/GitHub). We help ease the learning curve by avoiding local installation and supplementing out-of-class learning with interactive tools (like learnr tutorials). By the end of the semester teams of students work on fully reproducible data analysis projects on data they acquired, answering questions they care about. This repository serves as a "data science course in a box" containing all materials required to teach (or learn from) the course described above.

rstats r education teaching data-scienceWith the DiagrammeR package you can create, modify, analyze, and visualize network graph diagrams. The output can be incorporated into RMarkdown documents, integrated with Shiny web apps, converted to other graph formats, or exported as image files. This package is made possible by the htmlwidgets R package, which provides an easy-to-use framework for bringing together R and JavaScript.

r network-graph graph visualization property-graph graph-functions
We have large collection of open source products. Follow the tags from
Tag Cloud >>

Open source products are scattered around the web. Please provide information
about the open source projects you own / you use.
**Add Projects.**