autoplotly: An R Package for Automatic Generation of Interactive Visualizations for Statistical Results

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Authors Yuan Tang
Journal/Conference Name J. Open Source Software
Paper Category
Paper Abstract Often times users only want to quickly iterate the process of exploring data, building statistical models, and visualizing the model results, especially the models that focus on common tasks such as clustering and time series analysis. Some of these packages provide default visualizations using base R’s plot() for the data and models they generate. However, they look out-of-fashion and these components require additional transformation and clean-up before using them in ggplot2 (Wickham 2009) and each of those transformation steps must be replicated by others when they wish to produce similar charts in their analyses. Creating a central repository for common/popular transformations and default plotting idioms would reduce the amount of effort needed by all to create compelling, consistent and informative charts. The ggfortify (Tang, Horikoshi, and Li 2016; Horikoshi and Tang 2018) package provides a unified ggplot2 plotting interface autoplot() to many statistics and machine learning packages and functions in order to help these users achieve reproducibility goals with minimal effort. ggfortify package has a very easy-to-use and uniform programming interface that enables users to use one line of code to visualize statistical results of many popular R packages using ggplot2 as building blocks. This helps statisticians, data scientists, and researchers avoid both repetitive work and the need to identify the correct ggplot2 syntax to achieve what they need. Users are able to generate beautiful visualizations of their statistical results produced by popular packages with minimal effort. plotly (Sievert et al. 2018) is an R package built on top of Javascript visualization framework plotly.js that become popular building blocks for creating interactive visualizations in R. Its ggplotly() function can convert graphics created by ggplot2 to interactive plots with plotly style.
Date of publication 2018
Code Programming Language R

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