Analyzing and Visualizing Data

Data Visualization is Art


Enjoy the artwork made from data by artists such as Atticus Bones, Gregory Matthews, Antonio Sanchez, Gaston Sanchez, Alisa Singer, Marcus Volz, Nadieh Bremer, Olivier O’Brien, James Cheshire, Paivi Julin, and Yan Holtz. Find more data artwork on Data to Art.

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Getting Started with Simple Tools


The easiest way to create infographics is analyzing and visualizing data with Google Sheets and embedding the graphs in Google Drawings. With this intuitive drawing tool you can add text, shapes, or images to create infographics from the scratch and export them as pdf, jpg, png, or svg. If you prefer Microsoft tools, you can also use Excel and PowerPoint with its helpful SmartArt features.

A simple method of visualizing your data with only a few clicks is Mondrian. Since the outcome is not very pretty, Mondrian is more useful for exploring than for presenting your data.

Another tool many students and organizations use is Tableau Public.


Data Visualization with R


For more sophisticated purposes, I recommend learning to code with R because you can create very individual graphics, and R is a free, increasingly popular software among small businesses. Download both R and RStudio for free and start learning with Quick-R or DataCamp. Additionally, The Programming Historian offers a collection of tutorials for historians to learn programming with R and Python.

If you need to know more about specific graphs or functions, the R-Cookbook is a great reference guide. The R Graph Gallery gives you an overview of and examples for chart types depending on your research objective. Chart types range from distribution, correlation, ranking, evolution, and flow charts to maps or extracts. Besides classic 2D visualizations you can also create 3D, animated, and interactive charts using R. Stack Overflow offers practical solutions in case your code does not work.

Here are a few visualizations I created with R. The plots represent historical data for executions in the U.S. between 1608 and 2002. (Data: ICPSR 8451, Executions in the United States, 1608-2002)

Below you see RStudio Desktop, an integrated development environment for R. This is where you code your plots.


You can view or download two examples of R codes I wrote for a word cloud and two maps like the one below. Oftentimes there are many different ways to produce charts, but these codes worked for me and give you an idea how coding with R works.

Left: Word cloud representing data from headlines of advertisements showing Native Hawaiians that were published in Time Magazine between 1925 and 2015.

Right: Density plot representing data from the project Fighting Generikee showing where critics raised protest against Native inspired marketing practices most frequently (data not final).



The University of Zürich developed an interactive graphic overview of the various statistical methods. Unforunately, the UZH provides information only in German.



The interactive deciding assistant helps you to find the best method for your project depending on your objective.