Decision Mechanics

Insight. Applied.

The 5 most downloaded R packages

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DataCamp have published an article on the five R packages with the most (direct) downloads. This is based on their leaderboard.

Packages 3-5 are currently swapping positions. As I write this (18 November 2016) the top five are

  • dplyr
  • devtools
  • ggplot2
  • cluster
  • foreign

It’s notable that the list of the most popular packages is heavily weighted towards the manipulation and display of data. This is the bulk of the work done by data scientists.

The highest ranking analytical package is for performing cluster analysis. No surprise, really, as looking for groups in data is a very common requirement.

2016 Retail Executive Survey highlights big data capability gap

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FTI Consulting has published the results of its 2016 Retail Executive Survey.

100 C-suite executives were asked to rank 15 strategic priorities that they deemed to be “essential” or “high priority”. While big data was 14th on this list, it’s important to remember that all were considered to be at least “high priority”.

The story looks different when the executives were asked about their capability to execute these initiatives. The one that they are currently least able to execute is “big data”.

According to the survey, investment is aligned with strategic priority—even when there’s a capability surplus. It makes little sense to keep spending more on areas where you are over-qualified at the expense of key initiatives where capability is lacking.

If retailers don’t start investing more aggressively to address this shortfall Amazon will continue to eat their lunch.

Statistics books to read for pleasure

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asleep with book

I’ve read quite a few (really) dry, technical books in my time. But even I was shocked to see an article entitled “Statistics Books to Read for Pleasure”.

Isn’t that just a step too far?

However, it reminded me of one excellent book that should be required reading after this month’s polling meltdown. “Everydata: The Misinformation Hidden in the Little Data You Consume Every Day” is an excellent, easy read.

And, yes, I admit it—I read it entirely for pleasure. That can be our guilty little secret.

Public data sources

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ethernet cables

Data science requires data. Yep. Insightful.

Unless you work at a data-rich organization, data can be hard to obtain. You may want to try out a new technique or tool. Alternatively, you may need additional data to fuse with your own limited in-house data. In either case, Nathan Yau’s updated list of public data sources might help.

He lists sources for the following types of data

  • demographic
  • health
  • geographic
  • news
  • sports
  • general purpose