Difference between revisions of "Data visualization"
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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. | 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. This page discusses general principles for data visualization. | ||
== Read First == | == Read First == | ||
Specific code for data visualization is available on the software-specific tools (e.g. [[iegraph]]) | * The DIME Analytics team has created a [https://worldbank.github.io/Stata-IE-Visual-Library 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 [https://github.com/worldbank/Stata-IE-Visual-Library on our github]. | ||
* Specific code for data visualization is available on the software-specific tools (e.g. [[iegraph]]). | |||
== Guidelines == | == Guidelines == | ||
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* Gapminder.org [https://www.gapminder.org/tools/#$chart-type=bubbles|interactive visualization tools] provide beautiful examples of effective visualizations. | * Gapminder.org [https://www.gapminder.org/tools/#$chart-type=bubbles|interactive visualization tools] provide beautiful examples of effective visualizations. | ||
=== | === Fun with colors === | ||
=== Adapting visualizations to different contexts === | |||
== Data Visualization in R == | |||
R has many options for data visualization. Here are some useful packages: | R has many options for data visualization. Here are some useful packages: | ||
Revision as of 18:51, 30 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. This page discusses general principles for data visualization.
Read First
- 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.
- Specific code for data visualization is available on the software-specific tools (e.g. iegraph).
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.
- Data to Viz provides a handy decision tree.
- The Periodic Table of Visualization provides a catalogue of all data visualization types with visual examples.
- Gapminder.org visualization tools provide beautiful examples of effective visualizations.
Fun with colors
Adapting visualizations to different contexts
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
- Harvard Business Review Article on Visualizations that Really Work
- Stata Cheat sheets on Data visualization and customizing data visualization are useful reminders of relevant stata code.