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22 free tools for data visualization and analysis

Sharon Machlis | Jan. 3, 2012
There are many tools around to help turn data into graphics, but they can carry hefty price tags.

Skill level: Advanced beginner. Knowledge of data analysis concepts is more important than technical prowess; power Excel users who understand data-cleaning needs should be comfortable with this.


Runs on: Windows, Mac OS X (if it appears to do nothing after loading on a Mac, point a browser manually to ), Linux.

Learn more: These three screencasts give a good overview of why and how you'd use Refine; there's also fairly detailed documentation on the Google Code project area.

Statistical analysis

Sometimes you need to combine graphical representation of your data with heftier numerical analysis.

The R Project for Statistical Computing

What it does: R is a general statistical analysis platform (the authors call it an "environment") that runs on the command line. Need to find means, medians, standard deviations, correlations? R can handle that and much more, including "linear and generalized linear models, nonlinear regression models, time series analysis, classical parametric and nonparametric tests, clustering and smoothing," according to the project website.

R also graphs, charts and plots results. There are numerous add-ons to this open-source project that significantly extend functionality. For users who prefer a GUI, Peter Aldhous, San Francisco bureau chief for New Scientist magazine, suggests RExcel, which offers access to the R engine through Excel.

What's cool: There is a great deal of functionality in R, including quite a number of visualization options as well as numerical and spatial analysis.

Drawbacks: The fact that R runs on the command line means that users will have to take the time to learn which commands do what, and not all users will be comfortable with a text-only interface. In addition, Aldhous says those dealing with large data sets may hit a memory barrier (if so, there's a commercial option from Revolution Analytics).

Skill level: Intermediate to advanced. Comfort with command-line prompts and a knowledge of statistics are a musts for the core application.

Runs on: Linux, Mac OS X, Unix, Windows XP or later.

Learn more: Try R for Statistics: First Steps (PDF) by Peter Aldhous, Hands-on R, a step-by-step tutorial (PDF) by Jacob Fenton, and the project's own An Introduction to R. The R Statistics blog has a number of visualization samples.

Visualization applications and services

These tools offer a number of different visualization options. While some stick to conventional charts and graphs, many offer a range of other choices such as treemaps and word clouds. A few offer geographical mapping as well, although if you're interested in maps, our sections on GIS/mapping focus specifically on that.

Google Fusion Tables

What it does: This is one of the simplest ways I've seen to turn data into a chart or map. You can upload a file in several different formats and then choose how to display it: table, map, heatmap, line chart, bar graph, pie chart, scatter plot, timeline, storyline or motion (animation over time). It's somewhat customizable, allowing you to change map icons and style info windows.


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