Difference between revisions of "Data visualization"

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R has many options for data visualization. Here are some useful packages:
 
R has many options for data visualization. Here are some useful packages:
  
* [https://www.rdocumentation.org/packages/ggplot2/versions/3.1.0/ ggplot2]: this is the go-to package for static plots. Here is a list of [http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html/ 50 ggplot2 visualizations with full R code].
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* [https://www.rdocumentation.org/packages/ggplot2/versions/3.1.0/ ggplot2]: this is the go-to package for static plots. Here is a list of [http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html 50 ggplot2 visualizations with full R code].
 
* [https://plot.ly/r/ plotly]: creates interactive graphs, and is integrated with ggplot.  
 
* [https://plot.ly/r/ plotly]: creates interactive graphs, and is integrated with ggplot.  
 
* [https://github.com/thomasp85/gganimate/ gganimate]: allows users to create animated GIFs from ggplot plots.
 
* [https://github.com/thomasp85/gganimate/ gganimate]: allows users to create animated GIFs from ggplot plots.

Revision as of 21:58, 28 January 2019

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.


Read First

Specific code for data visualization is available on the software-specific tools (e.g. iegraph). This page discusses general principles for data visualization.

Guidelines

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.

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 on 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 50 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.
  • Highcharter: this package is a wrapper for the Highcharts JavaScript library.
  • Leaflet: an R wrapper to one of the most popular open-source libraries for interactive maps.
  • R2D3: a wrapper for JavaScript's D3 library, that creates animated graphs.

Back to Parent

This article is part of the topic Data Analysis


Additional Resources