What data visualization projects are best suited for R?

1 Why R for data visualization?

R is a language specifically designed for statistical analysis, with a vast selection of packages and tools for data visualization. It offers a broad array of options for creating plots, from basic histograms and scatterplots to interactive maps and dashboards. Its consistent syntax and grammar, based on the ggplot2 package, makes it easy to learn and use. It also allows for customization and creativity, as you can modify every element of your plots, add layers, annotations, themes, and extensions, or even create your own functions and graphics devices. Moreover, R integrates well with other data analysis tools such as R Markdown, Shiny, RStudio, and the tidyverse; this integration enables you to combine code, text, and graphics in dynamic reports and web applications.

2 What types of data visualization projects are best for R?

R is especially suited for data visualization projects that require exploratory data analysis, statistical graphics, publication-quality graphics, and interactive graphics. Functions like plot(), hist(), boxplot(), or ggplot() enable you to quickly and easily create plots to understand the structure, distribution, and relationships of your data. Packages like ggpubr, ggrepel, or ggsignif are great for producing graphics that display statistical information such as confidence intervals, hypothesis tests, regression lines, or clusters. To adhere to the standards and guidelines of academic journals, books, or presentations, you can use packages like cowplot, patchwork, or ggthemes. For interactive graphics that allow the user to interact with the data such as zooming, filtering, selecting, or hovering you can use packages like plotly, leaflet, or highcharter.

3 How to use ggplot2 for data visualization in R?

ggplot2 is an R package for data visualization, which is based on the grammar of graphics, a set of principles and rules that describe how to map data to visual elements. The syntax of ggplot2 is as follows: ggplot(data = <data>, mapping = aes(<mappings>)) + <geoms> + <other components> . In this syntax, data is the data frame containing the variables you want to plot, mapping is the aesthetic mapping that defines how the variables are mapped to visual elements, geoms are the geometric objects that represent the data, and other components are optional elements that modify or enhance the plot. To illustrate, let's say you wanted to create a scatterplot of fuel economy for different cars. You could use ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = class)) + geom_point() , which would produce a plot like this [insert image of scatterplot].

5 How to use Shiny for web applications with data visualization in R?

Shiny is a powerful package that enables you to create web applications with data visualization in R without any knowledge of web development. It consists of two components: a user interface (UI) and a server. The UI defines the layout and appearance of the app, while the server defines the logic and functionality of the app. These two components communicate with each other by using reactive expressions and outputs, which update the app depending on the user's input. Shiny has several features, such as creating interactive and dynamic web applications with data visualizations, using inputs and widgets to control and manipulate the data and visuals, and deploying and hosting the web applications online. For instance, a simple web application that displays a histogram of the mpg dataset, allowing users to change the number of bins and color of bars can be created with a few lines of code. This will produce an app that looks like [insert image of web app].

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?