If you're reading this, you probably already know that scholarly publishing is broken. Yes, it "works" for some people, some of the time. If you're a for-profit publisher you're probably raking in a 30-40% profit margin (Taylor). I doubt you think that's a "broken" model. But if you're an employee of a university, it's broken and you're broke ... even if you don't know it.
Making maps in R is something that I have been curious about for some time. If you ask a geographer, R probably isn’t at the top of their list for creating maps. Dedicated GIS programs, such as ArcGIS, are most certainly a better choice if your sole interest is in visualizing geographic data. However, creating maps in R has some distinct advantages. For example, R code can easily be shared, collaboratively edited, and versioned using GitHub.
The Humanities Intensive Learning & Teaching Institute is delighted to announce that HILT2016 registration is now open. HILT will be held June 13-16, 2016 with special events on June 17th at Indiana University-Purdue University Indianapolis. HILT is a partnership of IUPUI, IUPUI Libraries, the Maryland Institute for Technology in the Humanities, and MATRIX: Center for Digital Humanities and Social Sciences.
As the head of Bibliographic and Metadata Services (BAMS), I coordinate the metadata creation for the vast array of digital collections produced by the IUPUI University Library Center for Digital Scholarship. The involvement of catalogers in the process of metadata creation has brought an expertise in description to and enriched our digital collections. Additionally, this work has provided variety to the work of our catalogers and helped them to learn new skills that will take them into the future.
Lately, I have been spending more time playing around with R. As an R beginner and someone interested in data visualization, one of my favorite packages so far is ggplot2. This package vastly simplifies the process of plotting data and the results are rather aesthetically pleasing. One of the really powerful features of ggplot2 is the way in which it makes visually encoding multiple dimensions of a dataset much easier.
In this brief tutorial, I will plot some data generated using Excel. The data (available here) represent 150 individuals and contains information on their gender, income, time spent commuting to work, student loans, and education level. I fabricated the data so that patterns will emerge in the resulting visualization that mimic what you might expect to see in the real world, but the data are totally fake.
The following presupposes some basic familiarity with R. If you are brand new, you may want to start with a basic R tutorial – there are dozens freely available on the internet.
