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Capture the spirit of your ggplot2 calls. ggplot2::ggplot() stores the information needed to build the graph as a grob, but that's what the computer needs to know about in order to build the graph. As humans, we're more interested in what commands were issued in order to build the graph. For good reproducibility, the calls need to be applied to the relevant data. While this is somewhat available by deconstructing the grob, it's not the simplest approach.

https://github.com/jonocarroll/ggghostTags | rstats ggplot2 visualisation visualization plotting |

Implementation | R |

License | Public |

Platform |

It uses ggplot2 and returns a ggplot2 object.

r waffle-charts square-pie-charts rstats ggplot2 datavisualization data-visualisation data-visualizationggforce 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-extensionThis 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-scalesText labels repel away from each other, away from data points, and away from edges of the plotting area. Please submit an issue to report bugs or ask questions.

ggplot2 cran visualization text rstatsGramm is a powerful plotting toolbox which allows to quickly create complex, publication-quality figures in Matlab, and is inspired by R's ggplot2 library by Hadley Wickham. As a reference to this inspiration, gramm stands for GRAMmar of graphics for Matlab. Gramm is a data visualization toolbox for Matlab that allows to produce publication-quality plots from grouped data easily and flexibly. Matlab can be used for complex data analysis using a high-level interface: it supports mixed-type tabular data via tables, provides statistical functions that accept these tables as arguments, and allows users to adopt a split-apply-combine approach (Wickham 2011) with rowfun(). However, the standard plotting functionality in Matlab is mostly low-level, allowing to create axes in figure windows and draw geometric primitives (lines, points, patches) or simple statistical visualizations (histograms, boxplots) from numerical array data. Producing complex plots from grouped data thus requires iterating over the various groups in order to make successive statistical computations and low-level draw calls, all the while handling axis and color generation in order to visually separate data by groups. The corresponding code is often long, not easily reusable, and makes exploring alternative plot designs tedious.

matlab visualization stats plot data-visualization statisticsggpage 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-visualizationggplot2 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-visualisationUpSetR generates static UpSet plots. The UpSet technique visualizes set intersections in a matrix layout and introduces aggregates based on groupings and queries. The matrix layout enables the effective representation of associated data, such as the number of elements in the aggregates and intersections, as well as additional summary statistics derived from subset or element attributes. For further details about the original technique see the UpSet website. You can also check out the UpSetR shiny app. Here is the source code for the shiny wrapper.

upset upsetr visualization gehlenborglab rstats ggplot23d plotting for Python in the Jupyter notebook based on IPython widgets using WebGL.

ipython-widget jupyter jupyter-notebook visualisation volume-rendering virtual-reality plotting dataviz scientific-visualization webgl threejs rendering-3d-volumes quiverXChart is a light-weight and convenient library for plotting data designed to go from data to chart in the least amount of time possible and to take the guess-work out of customizing the chart style.

charts tool graph visualization chart-library-java charts-library charting-libraryA compendium of ‘geoms’, ‘coords’, ‘stats’, scales and fonts for ‘ggplot2’, including splines, 1d and 2d densities, univariate average shifted histograms, a new map coordinate system based on the ‘PROJ.4’-library and the ‘StateFace’ open source font ‘ProPublica’.

geom ggplot2 r rstats ggplot-extension ggplot2-geom ggplot2-scalesGosl is a Go library to develop Artificial Intelligence and High-Performance Scientific Computations. The library tries to be as general and easy as possible. Gosl considers the use of both Go concurrency routines and parallel computing using the message passing interface (MPI). Gosl has several modules (sub-packages) for a variety of tasks in scientific computing, image analysis, and data post-processing.

scientific-computing visualization linear-algebra differential-equations sparse-systems plotting mkl parallel-computations computational-geometry graph-theory tensor-algebra fast-fourier-transform eigenvalues eigenvectors hacktoberfest machine-learning artificial-intelligence optimization optimization-algorithms linear-programmingThe goal of patchwork is to make it ridiculously simple to combine separate ggplots into the same graphic. As such it tries to solve the same problem as gridExtra::grid.arrange() and cowplot::plot_grid but using an API that incites exploration and iteration.

rstats ggplot2 visualizationggraph 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 rA weekly data project aimed at the R ecosystem. An emphasis will be placed on understanding how to summarize and arrange data to make meaningful charts with ggplot2, tidyr, dplyr, and other tools in the tidyverse ecosystem. We will have many sources of data and want to emphasize that no causation is implied. There are various moderating variables that affect all data, many of which might not have been captured in these datasets. As such, our guidelines are to use the data provided to practice your data tidying and plotting techniques. Participants are invited to consider for themselves what nuancing factors might underlie these relationships.

In fact, since ggmap’s built on top of ggplot2, all your usual ggplot2 stuff (geoms, polishing, etc.) will work, and there are some unique graphing perks ggmap brings to the table, too.

An 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 webglgganimate extends the grammar of graphics as implemented by ggplot2 to include the description of animation. It does this by providing a range of new grammar classes that can be added to the plot object in order to customise how it should change with time. In this example we see the use of transition_time() which can be used with continuous variables such as year. With this transition it is not necessary to provide transition and state length as the "transition variable" provides this directly (e.g. it should take twice as long to transition between 1980 and 1990 compared to 2000 to 2005). We also see the use of string literal interpolation in titles. gganimate lets you specify variables to evaluate inside titles and different transitions provide different type of information to use.

rstats ggplot2 animation transitionStop plotting your data - annotate your data and let it visualize itself. HoloViews is an open-source Python library designed to make data analysis and visualization seamless and simple. With HoloViews, you can usually express what you want to do in very few lines of code, letting you focus on what you are trying to explore and convey, not on the process of plotting.

visualization analysis bokeh matplotlib interactive data-science exploratory-data-analysis pandas
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