Search Results for "ns-2"

{ggstatsplot} is an extension of {ggplot2} package for creating graphics with details from statistical tests included in the information-rich plots themselves. In a typical exploratory data analysis workflow, data visualization and statistical modeling are two different phases: visualization informs modeling, and modeling in its turn can suggest a different visualization method, and so on and so forth. Bayesian hypothesis-testing. The central idea of {ggstatsplot} is simple: combine these two phases into one in the form of graphics with statistical details, which makes data exploration simpler and faster. ...

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 the Grammar of Graphics and both are powered by the JavaScript graphing library plotly.js, so many of the same concepts and tools that you learn for one interface can be reused in the other. ...

Python project with goal to provide FOSS library to extract, analyse and visualise data in a 3D fashion. The instance will connect to a data source, ods sheet, csv, sql DB, pyodbc the instance will analyse and/or transform the data to be presented to the visualisation functionality the instance will visualise the data in a 3D fashion, likely using third party FOSS

...The workflow ensures meeting the primary goals that 1) the reporting of statistical results is consistent with the actual statistical results (dynamic report generation), 2) the analysis exactly reproduces at a later point in time even if the computing platform or software is changed (computational reproducibility), and 3) changes at any time (during development and post-publication) are tracked, tagged, and documented while earlier versions of both data and code remain accessible.