Decision Mechanics

Insight. Applied.

R analysis of frequency of swear words in Irish English by gender and age

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profanity comparison charts by gender and age

Linguist Martin Schweinberger has used base R to perform a sociolinguistic analysis of swear word use in Irish English. The data analyzed is from the Irish component of the International Corpus of English.

I’m particularly tickled by the fact that the script contains a set of regular expressions that define swear words. For instance

search.pattern2 <- c (" [A|a]rse [s|d]{0 ,1} ")

Turns out that while young women swear less than young men—the latter seem to go through a period of more intense swearing—by the time people get beyond 34, there’s gender harmony in the use of profanity.

Real-world datasets for learning data science in R

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R comes with a range datasets that can be used when learning the basics or trying out a new approach/package. mtcars and weather` are popular choices.

However, most of the common datasets are “toy” examples. They are great for practising basic techniques, but are useless when it comes to realistically simulating data science tasks.

The dslabs package provides datasets more suited to exploring data science techniques. It includes

  • admissions Gender bias among graduate school admissions to UC Berkeley (example of Simpson’s paradox)
  • divorce_margarine Divorce rate and margarine consumption data (illustrates spurious causation)
  • gapminder Health and income outcomes
  • heights Self-reported heights
  • murders US gun murders by state for 2010
  • na_example Randomly generated count data with missing values
  • outlier_example Randomly generated adult male heights (in feet) with outliers
  • polls_us_election_2016 Fivethirtyeight 2016 Poll Data
  • research_funding_rates Gender bias in research funding in the Netherlands
  • rfalling_object Simulate falling object data
  • take_poll Models results from taking a poll

Data organization in spreadsheets

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Karl Broman and Kara Woo offer some good advice on organizing data in spreadsheets.

They advocate confining the use of spreadsheets to data entry and storage—moving calculations and visualizations to other tools. This certainly avoids some of the biggest problems with using spreadsheets.

However, spreadsheets don’t enforce any discipline. It’s up to the user to be vigilant and do the right thing. People are bad at maintaining consistency. Computers are excellent at it. Why make it hard for ourselves?

Spreadsheets. Just say no.

7 problems with spreadsheets

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7 problems with spreadsheets

Using Excel in business critical operations is risky to the point of recklessness.

Phocas have just published an article highlighting seven problems with spreadsheets. The problems are

  1. susceptibility to human errors
  2. difficulties in troubleshooting
  3. lack of agility
  4. lack of collaborative features
  5. lack of support for rapid decision-making
  6. degree of complexity for the average business user
  7. security risks

See the original article for details.