Top R Libraries for Data Visualization in 2024
Last Updated :
20 Mar, 2024
When you are talking about data analysis, don’t forget data visualization! It is a very important part of data analysis that can reveal hidden trends and provide more insight into the data. Data visualization can provide information just by looking at them whereas it would take much more time to obtain that same information from spreadsheets or text reports. And that is why Data Visualization is so popular. In this article, we will discuss the Top R Libraries for Data Visualization.
Since R is one of the most popular programming languages in data analysis, it’s not a shock that there are many R libraries for data visualization. These libraries are so popular because they allow data analysts to create the visualizations they want from their data easily by conveniently providing both the interface and the tools all in one place! Then the only important thing is knowing what the visualization wants to convey to the users and creating that using all the tools available. What more could a data analyst want?!
Top R Libraries for Data Visualization in 2024
R is a popular programming language among analysts, researchers, and programmers in the Data Science field. The reason for this is a wide ecosystem of libraries that are readily available for use, allowing data to be presented efficiently to diverse stakeholders. So let’s check out some of these Top R Libraries for Data Visualization that are commonly used these days.
1. ggplot2
ggplot2 is an R data visualization library that is based on The Grammar of Graphics. ggplot2 can create data visualizations such as bar charts, pie charts, histograms, scatterplots, error charts, etc. using high-level API. It also allows you to add different types of data visualization components or layers in a single visualization. Once ggplot2 has been told which variables to map to which aesthetics in the plot, it does the rest of the work so that the user can focus on interpreting the visualizations and take less time to create them. But this also means that it is not possible to create highly customized graphics in ggplot2. But there are a lot of resources in the RStudio community and Stack Overflow that can provide help in ggplot2 when needed. Just like dplyr, if you want to install ggplot2, you can install the tidyverse or you can just install ggplot2 using install.packages(“ggplot2”)
2. Plotly
Plotly is a free open-source graphing library that can be used to form data visualizations. Plotly is an R package that is built on top of the Plotly JavaScript library (plotly.js) and can be used to create web-based data visualizations that can be displayed in Jupyter notebooks or web applications using Dash or saved as individual HTML files. Plotly provides more than 40 unique chart types like scatter plots, histograms, line charts, bar charts, pie charts, error bars, box plots, multiple axes, sparklines, dendrograms, 3-D charts, etc. Plotly also provides contour plots, which are not that common in other data visualization libraries. In addition to all this, Plotly can be used offline with no internet connection. You can install Plotly from CRAN using install. packages(‘plotly’) or install the latest development version from GitHub using devtools::install_github(“ropensci/plotly”).
3. Esquisse
Esquisse is a data visualization tool in R that allows you to create detailed data visualizations using the ggplot2 package. You can create all sorts of scatter plots, histograms, line charts, bar charts, pie charts, error bars, box plots, multiple axes, sparklines, dendrograms, 3-D charts, etc. using Esquisse and also export these graphs or access the code for creating these graphs. Esquisse is such a famous and easily used data visualization tool because of its drag-and-drop ability which makes it popular even among beginners. You can install Esquisse from CRAN using install.packages(“esquisse”) or install the development version from GitHub using remotes::install_github(“dreamRs/esquisse”).
4. Lattice
Lattice is a data visualization tool that is primarily used to implement Trellis graphs in R. These Trellis graphs are used to view many complicated and multi-variable data sets at the same time so they can be compared. Since all these different plots end up looking like a Trellis, this is called a Trellis graph. Since Lattice is a high-level data visualization library, it can handle many of the typical graphics without needing many customizations. In case you want to extend the capabilities of Lattice, they can download the LatticeExtra package which is an extended version. You can install Lattice from CRAN using install.packages(“lattice”) or install the development version from GitHub using remotes::install_github(“deepayan/lattice”).
5. RGL
The RGL package in R is created specifically for making 3-D data visualizations and data plots. It has many graphics commands that work in 3 dimensions but is modeled loosely after the classic 2-D graphics in R. RGL is also inspired by the grid package in R but it is incompatible with it. However, seasoned R coders can easily use RGL because of an existing familiarity with the grid. And RGL is very cool! It has a lot of options for 3-D shapes, various lighting effects, creating new shapes, and also animations. You can install RGL from CRAN using install.packages(“rgl”).
6. Dygraphs
The dygraphs package is an R interface to the JavaScript charting library dygraphs that are used to provide various charts for visualizing data sets. This package can be used for creating various interactive visualizations with zooming, and panning options along with default mouse-over labels. dygraphs also provide support for various graph overlays such as point annotations, shaded regions, event lines, etc. You can also plot the xts time series objects automatically. However, all of these features do not come at the expense of speed in dygraph. Rather, it can provide maximal interactivity even with millions of data points in the visualization. You can install RGL from CRAN using install.packages(“dygraphs”).
7. Leaflet
Just like dygraphs, the Leaflet package is an R interface to the JavaScript Leaflet library that is extremely popular. The Leaflet is very useful in creating interactive but lightweight maps that are seen on various websites such as the Washington Post, the New York Times, etc. There are many useful features in this package such as interactive panning and zooming in the charts, the option to combine Polygons, Lines, Popups, etc. to create charts, embed maps in knitr, create maps in Mercator projections that are non-spherical, and so on. The Leaflet package can be used at the R console after installing it from CRAN using the command install.packages(“leaflet”).
Conclusion
All of these R Libraries for Data Visualization are fantastic choices for creating data visualizations. The packages listed above are only a few of the several good solutions for data visualization in R. Each of these libraries has advantages and downsides, and they may or may not be employed in your next data science project, but it is good to know that they and similar libraries exist. Each of these libraries has benefits, and you can select the ideal one based on the type of visualization or data science project you intend to build. Now that you’ve learned about these libraries, use them to create visually appealing and helpful data visualizations.
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