Drunk driving with spreadsheets

A new anti-spreadsheet aphorism for data scientists
not so standard deviations data science statistics

Recently, while listening to an episode of the podcast “Not So Standard Deviations”, by Roger Peng and Hilary Parker, the line “Doing data analysis with spreadsheets is like driving drunk” (attributed to statistician Philip Stark) stood out to me. This short phrase gets at the very notion of how very irresponsible the use of spreadsheets is for many of the routine tasks of data science. That is, spreadsheets provide a high level of accessibility to the data that is so central to the insights extracted by data scientists – and, it is this high level of control over the data itself that makes their use so very dangerous. Given that the role of data scientists (and applied statisticians) most often is to extract insights from a data set – insights that may obviously be easily manipulated by (incidental) changes to the data itself – manual manipulation of data seems quite irresponsible. In stark contrast to the manipulative power provided by spreadsheets, typical data structures used in scientific computing (for example the dataframe used in R, and Python’s pandas) make the summarization and modeling of data easier, at the expense of hindering manipulation of the data set itself.

After opening a data set in spreadsheet software, it takes just a single click to edit the raw data itself; however, after loading a data set into a structure like a dataframe, making purposeful changes to the raw data often requires a set of commands that would be quite difficult to execute in error. By making the data inaccessible via mere clicks, the dataframe structure provides a level of removal from the raw data that minimizes potential incidental changes to the information that is to be analyzed. In the same manner in which drunk driving inhibits motor skills, increasing potential for incidental, damaging consequences, the overeager use of spreadsheets brings the data scientist simply too close to introducing errors into the raw data. It clearly follows that, in an age of computationally reproducible research, the use of spreadsheets is indeed quite irresponsible when the goal of data science is the extraction of insight from information – that is, the use of spreadsheets may often lead the vulnerable data scientist off of the road to valid insights.

Renjin: put some Java in your R

Quick look at using R with some Java 'under the hood'
R data science statistics computing

Taking blogdown for a test drive

Trying out RStudio's new blogging framework
R data science tools productivity

R graphics - ggplot vs base

May 2, 2016
R graphics data science not so standard deviations
comments powered by Disqus