Data Visualization Libraries in R

1. ggplot2

ggplot2 is an R data visualization library that is based on The Grammar of Graphics. ggplot2 can create data visualizations for data exploration, such as histograms, scatterplots, error charts, etc., and for data explanation, such as bar charts, pie charts, scatterplots, etc. It also allows you to add different types of data visualization components or layers to a single visualization. One advantage of ggplot2 is that you only need to specify the variables and all the layers for the plot, and it easily creates what you want. But this also means that there is not much room for detailed customization 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. Leaflet

The Leaflet package is an R interface to the JavaScript Leaflet library that is extremely popular. Leaflet can be used to create interactive but lightweight maps that are very popular for displaying to other viewers. Leaflet also has many options that make it interactive, such as 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”).

3. Esquisse

Esquisse can be used in R with the help of ggplot2 to create detailed data visualizations. These include any charts you can imagine such as scatter plots, histograms, line charts, bar charts, pie charts, error bars, box plots, multiple axes, sparklines, dendrograms, 3-D charts, etc. Esquisse also allows its users to 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 that 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”).

Data is becoming the backbone of our current society. Companies can use data to predict their customer reactions, the success of their products and services, and the areas they need to work on. Data can also be used to understand many social and natural phenomena in the world such as social media trends, mass migration, global warming, etc. However, while data scientists can understand all these using various analytical procedures and statistical modeling of the data, it’s a very different thing to convey these findings to other people. That’s where data visualization is extremely important!

Data visualization allows data scientists to understand the layout and patterns in the data in visual form. It also allows them to demonstrate the findings from their data in simple terms to other people who are not experienced in data science. Therefore, data visualization is the language that is used to convey the data to regular people. Many data visualization libraries across programming languages can be used for this task. This article deals with the most popular data visualization libraries in Python, R, and Javascript. You can use these libraries to create basic charts like bar charts, pie charts, histograms, scatterplots, sparklines, dendrograms, contour plots, choropleth maps, etc. If you want to be more creative, you can even add animations to your charts and create data stories. So what are you waiting for? Check out these libraries and begin your data visualization journey by creating your first chart!