6/11/2023 0 Comments Display graphs in rmarkdown githubIn addition, if you are looking for a particular graph, most of the sections contain a 'More.' link that leads to a range of extra visualisations. These examples are appended with read-to-run code blocks that will help you get started very quickly. To see examples of open-source graphics made with the R plotly package, use this link. Since the package also depends on the ggplot2 package we have explored in the previous blog post, we can actually interact with the charts and graphs we have made by using the ggplotly() function that converts a ggplot2 object into a plotly object! The plotly package in R was created as an interface to the Javascript Library plotly.js therefore you can create many interactive web-based graphics out of it. Plotly was known for providing scientific charting libraries that has been increasingly popular over the last few years and you can discover its place in many popular programming languages and platforms such as Python, R, Javascript, Angular, React.JS, and. You might wonder what htmlwidgets is - "A framework for creating HTML widgets that render in various contexts including the R console, 'R Markdown' documents, and 'Shiny' web applications." In fact, any interactive visualisations produced that can be saved as a standalone HTML file via the htmlwidgets::saveWidget() function, including packages like plotly, leaflet, and ggiraph that we are going to learn about. In the first two sections we will explore Plotly and Shiny which are among the most downloaded visualisation packages, and then followed by other interactive packages in later sections.Äownload statistics of visualisation packages up to (source: R pkg download stats) If you have any suggestions or want me to include any particular package feel free to send me an email! You can find all the source code in this Github repository. Same as in the previous blog post, I will be using the Hadfield Green Roof 5-year dataset and assume you already have some experience with R. In this blog post we will explore some of the most popular packages in R for Interactive Visualisations and some of which you might have already noticed. Welcome to the third blog post of the series Exploring packages in R.
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