Chapter 1 Introduction

1.1 The Gift of Data

Humanists make stories, arguments, and narratives. “Data” is a word that, nowadays, sounds like it comes from a different vocabulary altogether. It creates not stories, but proofs, discoveries, and statistical inference. Reading belongs to the humanities; data analysis belongs to sciences. To this way of thinkin, “humanities data” isn’t quite an oxymoron, but it comes close.

One language of data that insists on treating data as a resource. One mantra emphasizes it as a natural resource; data is to be “mined,” or data is “the new . That makes data seem pristine, but a flip side, probably more attractive to humanists, emphasizes data instead as detritus: as the developer Maciej Ceglowski (2015) puts it, it is”a waste product, a bunch of radioactive, toxic sludge that we don’t know how to handle."1

You can engage in data analysis from either of these angles. But they both force us to have an opinion about data that is slightly odd, aligned with one of the internecine skirmishes in the great culture wars of the 21st century; between the technologists who see salvation in data, and the social critics who fear the ways it simplifies the world. Many people find it meaningful to identify themselves as “loving data” or being scared of it or being suspicious of it. To be on “data’s” side is to align with science, and rationalism; to be against it is to seek the remaining blocks of human experience, connection, and unreduced knowledge that are melting like glaciers in a technocratic greenhouse. This book is a technical manual to occupying a middle ground, where data exists as part of–not contrary to–the humanities. To understand what “data” really means in a humanistic context, it can be helpful to think not statstically but etymologically.

“Data” in Latin is simply the plural of the past participle of the verb ‘do,’ to give. (Every first-year Latin students memorize the foursome “do-dare-dedi-datum”). Grammatically, it means the same thing as ‘given’ or ‘donation.’ Some scholars ((drucker_graphesis_2015?)) have argued that to call data given misrepresents the social process that makes data available to us, the force with which data has been ripped from the world; Johanna Drucker, following Christopher Chippendale,2 argues that we should prefer the term ‘capta’ instead, to remind us that someone has gone out and seized this information from the world. Humanities data is always in some sense about people; it represents information about people extracted through force, flattery, or subterfuge. Data-as-capta is a analytic framework that suggests an important responsibility: the humanist must always carry an awareness of the situatedness of the data they work with, and the social relations and power that make it possible for it to exist.

If you are on the front lines of a war between the sciences and the humanities, this may be a helpful tack to take. But there is another way, too, that is closer to the etymology of ‘data’ in the university tradition. David Rosenberg has shown how the original uses of ‘data’ in English language books come from theology and mathematics, where data means something that your argument rests on. Rosenberg writes that this tradition was already evaporating by 1800: “It had become usual to think of data as the result of an investigation rather than its premise. While this semantic inversion did not produce the twentieth-century meaning of data, it did make it possible.” Rosenberg (2013).

In this etymology, though, is an insight we should keep. The gift of data is not from the world to the researcher. It is from the reader to the writer. To talk of resources, capta, or science misses that fact. What is given not the relationship between the scholar and her sources, but between the writer and her audience. “Data,” in this sense, means the premises which the author asks her audience to concede–or to give up–or to take as a given–before the argument begins. The system of geometry is built up from four fundamental axioms which we take to be true. Any rhetorical argument proceeeds from some assumptions, that which we ‘take for granted.’ To do ‘data analysis,’ in this sense, is to work out the system of implications of some of set of evidence; and it is only useful if anyone will accept your evidence to begin with.

To write about data is to solicit a gift from your readers; the willingness to entertain your premises while you describe them. And depending what field you are in, the way that you solicit this gift will be radically different. In the humanities–as in much public writing directed at non-scientists– it is not reasonable to expect others to accept your data because it’s numerical; you must, instead, lead them along to the idea that data has something to say. (A better word than gift might be concession.)

To work with data in this sense is not always to perform scientific inference. It is to plumb the relationships of the written record and the enumerated record to the people who were reduced to writing and numbers; and it’s to engage in the careful working out of the implications of that record. One key component of rhetoric that is rarely thought of as data analysis is the reductio ad absurdam: the rhetorical form that demonstrates that two premises (two givens; two data) can not reasonably co-exist, because they produce some outcome which is self-evidently ridiculous. This is a claim we’ll explore.

1.2 Transformational thinking

As I have written elsewhere, digital humanists do not need to understand algorithms; instead, they need to understand the underlying transformations that algorithms execute.@schmidt_digital_2016 These transformations describe the sorts of things you can do to a dataset. Many of them you probably already know in principle: sorting a list, taking an average, making a line chart. For those, this text simply aims to give you a way to command a computer to execute those tasks in a language that’s flexible to help you think about stringing multiple simple transformations together.

Other transformations are more exotic, but have showed their worth in either decades of research in the digital humanities or in intense exploration inside computer science over the last decade. I have tried to be judicious in wat I present from this sphere, but there is good reason to understand things like the vector space model underlying modern machine learning, the concept of the ‘bootstrap’ as a general purpose statistical test, or the transformations involved in the fundamental information-theoretic metric of pointwise mutual information.

1.3 Code

This course will have you writing code in the R language. There is an extensive debate about whether digital humanists need to learn to code. If you have a lot of money to pay other people, you can probably get away without it. But the fact of the matter is simply that if you want to either do data analysis in the humanities, coding will often be the only way to realize your personal vision; and if you want to build resources in the humanities that others might want analyze, you’ll need to know what sophisticated users want to do with your tools to make them work for them.

I have no expectation that anyone will come out of this a full-fledged developer. By doing some actual scripting, you’ll come to see that debates over learning to code brush create a false binary; everyone is working We’ll be focusing in particular in developing skills less in full-fledged “programming,” but in “scripting.” That means instructing a computer in every stage of your work flow; using a language rather than a Graphical User Interface (GUI). This takes more time at first, but has some major advantages over working in a GUI:

  1. Your work is saved and open for inspection.
  2. If you want to discover an error, you can correct it without losing the work done after.
  3. If you want to amend your process (analyze a hundred books instead of ten, for instance) but do the same analysis, you can alter the code only slightly.
  4. You can deploy a wide variety of methods on the same set of data. While the initial overhead to coding is high, when you read about some fancy new method you can often test it quickly inside R rather than having to figure out some different piece of software.
  5. You can deploy the same methods on a wide variety of data. The tidy data abstraction we’re working with gives a vocabulary for thinking about documents, resources, and anything that can be counted; by creatively re-combining them, you can interpret new artifacts in interesting ways.

  1. I got this reference from Zoe LeBlanc.↩︎

  2. Chippindale, C. (2000). Capta and Data: On the True Nature of Archaeological Information. American Antiquity, 65(4), 605-612. doi:10.2307/2694418↩︎