Top 10 R Packages For Data Visualisation
As per study reports, data scientists and practitioners prefer R as the language for statistical modelling after Python language. Also, R dominates the preference scale, with a combined figure of 81.9% utilisation for statistical modelling among those surveyed. Below here, we listed the top 10 libraries in R for data visualisation one must know. (The list is in alphabetical order). 1| Colourpicker About: Colourpicker is a tool for Shiny framework and for selecting colours in plots. This tool supports various options, such as alpha opacity, custom colour palettes, and more. The most common uses of this tool include the utilisation of the colourInput() function to create a colour input in Shiny as well as the use of the plotHelper() function/RStudio Addin to select colours for a plot. Know more here. 2| Esquisse About: The esquisse package allows a user to interactively explore data by visualising it with the ggplot2 package. It allows a user to draw bar graphs, curves, scatter plots, histograms, export the graphs, and retrieve the code generating the graph. With the help of esquisse, one can quickly visualise the data according to their type as well as export to PNG or PowerPoint, and retrieve the code to reproduce the chart. Know more here. 3| ggplot2 About: ggplot is a popular package that is based on the grammar of graphics. The idea behind this library is that one can build every graph from the same components, such as a dataset, a coordinate system, and more. The package provides graphics language for creating intuitive and intricate plots. It allows a user to create graphs that represent both univariate and multivariate numerical and categorical data. Know more here. 4| ggvis About: ggvis is a data visualisation package for R that allows to declaratively describe data graphics with a syntax similar in spirit to ggplot2. It allows creating rich interactive graphics locally in Rstudio or in the browser as well as leverage the infrastructure of the Shiny package to publish interactive graphics usable from any browser. The goal of ggvis is to make it easy to build interactive graphics for exploratory data analysis. Know more here. 5| ggforce About: The ggforce is a package aimed at providing missing functionality to ggplot2 through the extension system introduced with ggplot2 v2.0.0. The goal of this package is to provide a repository of geoms, stats, among others. Using ggforce, one can enhance almost any ggplot by highlighting data groupings and focusing attention on interesting features of the plot. Know more here. 6| Lattice About: Lattice is a powerful high-level data visualisation system for R that is designed with an emphasis on multivariate data and allows to create multiple small plots easily. The lattice package attempts to improve on base R graphics by providing better defaults and the ability to display multivariate relationships easily. Particularly, the package supports the creation of trellis graphs that show a variable or the relationship between variables, conditioned on one or more other variables. Know more here. 7| Plotly About: Plotly is an open-source R package for creating interactive web-based graphs via the open-source JavaScript graphing library plotly.js. The Plotly’s R graphing library helps in creating interactive, publication-quality graphs including line plots, scatter plots, area charts, bar charts, error bars, etc. One can use Plotly for R to make, view and distribute charts and maps online as well as offline. Know more here. 8| patchwork About: patchwork is a package that expands the API to allow for the arbitrarily complex composition of plots by providing mathematical operators for combining multiple plots. The goal of patchwork is to make it simple to incorporate separate ggplots into the same graphic. You can install patchwork from CRAN using install.packages(‘patchwork’). Know more here. 9| quantmod About: quantmod is an R package that provides a framework for quantitative financial modelling and trading. It provides a rapid prototyping environment that makes modelling easier by removing the repetitive workflow issues surrounding data management and visualisation. Know more here. 10| RGL About: The RGL package is used to produce interactive 3-D plots using OpenGL. The library contains high-level graphics commands modelled loosely after classic R graphics and working in three dimensions. It also includes a low-level structure inspired by the grid package. RGL provides medium to high-level functions for 3D interactive graphics, including functions modelled on base graphics as well as functions for constructing representations of geometric objects. Know more here.
As per study reports, data scientists and practitioners prefer R as the language for statistical modelling after Python language. Also, R dominates the preference scale, with a combined figure of 81.9% utilisation for statistical modelling among those surveyed. Below here, we listed the top 10 libraries in R for data visualisation one must know. (The […]
As per study reports, data scientists and practitioners prefer R as the language for statistical modelling after Python language.
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["AI", "Aim", "Plotly", "RAG", "Python", "R", "JavaScript", "Go", "Java", "API"]
A Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box.