Category Archives: Uncategorized
I recently came across two very useful articles on data cleaning: Hadley Wickham’s “Tidy Data” (Journal of Statistical Software 59 ) and Jean Francois Puget’s blog post “Tidy Data in Python” (IBM developerWorks).
Wickham extends the concept of normalization to allow for easy analysis in an in-memory system like R:
Tidy data is a standard way of mapping the meaning of a dataset to its structure. A dataset is messy or tidy depending on how rows, columns and tables are matched up with observations, variables and types. In tidy data:
- Each variable forms a column.
- Each observation forms a row.
- Each type of observational unit forms a table.
This is Codd’s 3rd normal form (Codd 1990), but with the constraints framed in statistical language, and the focus put on a single dataset rather than the many connected datasets common in relational databases. Messy data is any other arrangement of the data (4).
He then discusses five common problems with real-world data, and defines three methods for tidying it–“melting, string splitting, and casting” (5). Several examples of messy and tidy datasets, as well as a case study in R, follow.
Puget’s post, as is evident from the title, expands on Wickham’s article, giving the Python equivalents of Wickham’s R code. Pandas, as it turns out, has a
melt() function–corresponding to
gather() in Wickham’s tidyr or
melt() in his reshape2–which forms a concise basis for data tidying within Python.
Over at Data Science Central is this interesting article on “data janitor work”: the fact that the biggest hurdle to large-scale data analysis is wrangling the data into a usable form. It is, of course, directly applicable to doing text analysis in the “Million Book Library.”
Data scientist[s] spend a comparatively large amount of time in the data preparation phase of a project. Whether you call it data wrangling, data munging, or data janitor work, the [New York] Times article estimates 50%-80% of a data scientists’ time is spent on data preparation. We agree. . . .
Before you start your project, define what data you need. This seems obvious, but in the world of big data, we hear a lot of people say, “just throw it all in”. If you ingest low quality data that is not salient to your business objectives, it will add noise to your results.
The more noisy the data, the more difficult it will be to see the important trends. You must have a defined strategy for the data sources you need and the particular subset of that data, which is relevant for the questions you want to ask.
Here’s an interesting think piece from Frank Pasquale in Aeon on the nature and role of data in society today.
Regulators want to avoid the irrational or subconscious biases of human decision-makers, but of course human decision-makers devised the algorithms, inflected the data, and influenced its analysis. No ‘code layer’ can create a ‘plug and play’ level playing field. Policy, human judgment, and law will always be needed. Algorithms will never offer an escape from society. . . .
An inference . . . may not be worth much on its own. But once people are so identified, it could easily be combined and recombined with other lists – say, of plus-sized shoppers, or frequent buyers of fast food – that solidify the inference. A new algorithm from Facebook instantly classifies individuals in photographs based on body type or posture. The holy grail of algorithmic reputation is the most complete possible database of each individual, unifying credit, telecom, location, retail and dozens of other data streams into a digital doppelganger.
However certain they may be about our height, or weight, or health status, it suits data gatherers to keep the classifications murky. A person could, in principle, launch a defamation lawsuit against a data broker that falsely asserted the individual concerned was diabetic. But if the broker instead chooses a fuzzier classification, such as ‘member of a diabetic-concerned household’, it looks a lot more like an opinion than a fact to courts. Opinions are much harder to prove defamatory – how might you demonstrate beyond a doubt that your household is not in some way ‘diabetic-concerned’? But the softer classification may lead to exactly the same disadvantageous outcomes as the harder, more factual one.
Stephan G. Schmid, “The ‘Hellenisation’ of the Nabataeans: A New Approach,” Studies in the History and Archaeology of Jordan 7 (2007): 407-419.
In this article, Schmid “give[s] a short overview on what is known about Nabataean material culture in its best understandable categories today and to look for whether there is any common line of development or even a model that could fit to most of these categories” (407). He notes that, although the Nabataeans are historically attested from 312 BCE, there is no evidence of a Nabataean material culture until around 100 BCE; moreover, when it appears, it is thoroughly Hellenistic. Schmid argues, following Diodorus Siculus, that the Nabataeans were “nomads or semi-nomads frequenting once or twice a year the same place for trade and business” (415) until ca. 100 BCE, after which they sedentarized. Their sedentarization lead them to develop a material culture. In the absence of an existing material culture, the Nabataeans simply “oriented their new material culture according to the mainstreams of the contemporary Hellenistic world in its Near Eastern variant” (415), into which they gradually incorporated Roman and “proper Nabataean” (416) elements.