Data sonification with R: the sound of Twitter data
What does a tweet sound like? Not the kind that flies around in the air, but the kind that zips to and from our mobile devices. I’m intensely interested in finding ways to make sense of data. Sonification of data – representing data with sound – offers one way to do that. This post steps through R code to take the text of tweets and turn them into short chirping sounds. It also uses different tones for different users so that each user has a “voice”. In other words, this post shows how to use R to make Twitter data sing.
Visualizing threaded conversation volume and intensity
click for larger view
As a researcher interested in information flows in digital environments I’m often interested in finding patterns in social trace data. For this discussion we can think of digital social trace data as the text that people post into threaded topics on forums, like on Reddit or a Wiki Talk page on Wikipedia. One way to find patterns in this kind of data is to make visualizations based on different quantifiable dimensions in the data, for example, total topic volume per day, volume per thread per day, and, possibly, the intensity of the discussion (as interpreted by qualitative researchers). In the remainder of this post I will note what we can learn from our visualization as well as its limitations and then post the R code I used to make the plot.
Read MoreR Gauge Plots
Gaston Sanchez’s post on R-Bloggers inspired me to waste a bit of time. He wanted to replicate the Google Charts widget to make gauges. I modified his code (below) in some minor ways and made a function out of it so you can alter the look and feel of your gauge. Feel free to pilfer and modify the R code…
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Using R to visually compare the volume of different information sources
A couple of weeks ago Bob wrote about a post about a research note that was recently accepted to the iConference. In it we outline the beginnings of a research project where we look at the interaction of different media platforms (Twitter and Blogs) with more traditional sources. In this post I go through the R code we used to plot, and visually compare, the volume of different information sources.
The data for this example is randomly drawn along a Pareto distribution so anyone should be able to just open the file, run it and have plots. Like I did in the last R example, I have used comments in the code to explain what I’m doing in the creation of these plots. After the code I give a brief introduction on the tool I use to select colors.
Read MoreR/iGraph Holiday network animation and How-to tips
The animation embedded in this post was done using R and the iGraph package and was, frankly, a great deal more work than I thought it would be when I started. What kept me going was a desire to express a wish for the New Year while also experimenting with some functionality that might be useful in my future research. In the following post I will provide some example code that extends my previous attempts at network animation by: 1) using the iGraph plot parameter margin to zoom in and out of different parts of the graph; 2) use the neighborhood function to highlight an information spread; and, 3) moving nodes along a path where you know the first and last point and the number of steps you want to make between them. I can imagine using the first two in my research, and the third was, well, just fun. I’m a geek. I’ll end the post with a line or two more about my motivations for creating this particular animation.
How to: network animation with R and the iGraph package & Meaning in data viz
This article lists the steps I take to create a network animation in R, provides some example source code that you can copy and modify for your own work, and starts a discussion about programming and visualization as an interpretive approach in research. Before I start, take a look at this network animation created with R and the iGraph package. This animation is of a retweet network related to #BankTransferDay. Links (displayed as lines) are retweets, nodes (displayed as points) are user accounts. For each designated period of time (in this case, an hour), retweets are drawn and then fade out over 24 hours.