Data visualization is creating a visual representation of your data, for example in the form of a chart or a graph. Choosing the right format to visualize your data is critical to effectively communicating the results of your study. Good visualizations can be more memorable and persuasive than pure text.
Specific code for data visualization is available on the software-specific tools (e.g. iegraph). This page discusses general principles for data visualization.
What type of data visualization should I use?
The best format for data visualization will depend on the type of data and results you wish to display, as well as the medium in which they will be displayed. For example, online interfaces allow for more dynamic visualizations than printed articles.
- |Data to Viz provides a handy decision tree.
- The Table of Visualization provides a catalogue of all data visualization types with visual examples.
- Gapminder.org visualization tools provide beautiful examples of effective visualizations.
Stata Visual Library
The DIME Analytics team has created a Stata Visual Library for Impact Evaluation which shows examples of graphs and provides the codes used to create them. You can contribute to the library our github.
Data Visualization in R
R has many options for data visualization. Here are some useful packages:
- ggplot2: this is the go-to package for static plots. Here is a list of ggplot2 visualizations with full R code.
- plotly: creates interactive graphs, and is integrated with ggplot.
- gganimate: allows users to create animated GIFs from ggplot plots.
- Leaflet: an R wrapper to one of the most popular open-source libraries for interactive maps.
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This article is part of the topic Data Analysis