Colin Gillespie shows off an R package for benchmarking:

Of course, it’s more likely that you’ll want to compare more than two things. You can compare as many function calls as you want with mark(), as we’ll demonstrate in the following example. It’s probably more likely that you’ll want to compare these function calls against more than one value. For example, in the digest package there are eight different algorithms. Ranging from the standard md5 to the newer xxhash64 methods. To compare times, we’ll generate n = 20 random character strings of length N = 10,000. This can all be wrapped up in the single function press() function call from the bench package:

Click through for an example involving hashing algorithms.

David Smith discusses potential changes to MRAN:

As CRAN has grown and changes to packages have become more frequent, maintaining MRAN is an increasingly resource-intensive process. We’re contemplating changes, like changing the frequency of snapshots, or thinning the archive of snapshots that haven’t been used. But before we do that we’d  like to hear from the community first. Have you used MRAN snapshots? If so, how are you using them? How many different snapshots have you used, and how often do you change that up? Please leave your feedback at the survey link below by June 14, and we’ll use the feedback we gather in our decision-making process. Responses are anonymous, and we’ll summarize the responses in a future blog post. Thanks in advance!

Please take the survey as well. If you’ve used SQL Server Machine Learning Services (or SQL Server R Services), you’ve used MRAN.

John Mount shares thoughts about the concept of tidy data:

A question is: is such a data set “tidy”? The paper itself claims the above definitions are “Codd’s 3rd normal form.” So, no the above table is not “tidy” under that paper’s definition. The the winner’s date of birth is a fact about the winner alone, and not a fact about the joint row keys (the tournament plus year) as required by the rules of Codd’s 3rd normal form. The critique being: this data presentation does not express the intended data invariant that Al Fredrickson must have the same “Winner Date of Birth” in all rows.

My spin on it is that tidy data is Boyce-Codd Normal Form but may subsequently be denormalized. This may reintroduce violations of 3NF (as in Mount’s example) and sometimes 2NF, but does not change the shape of the variables themselves—that is, a variable still represents a single thing and exists per observation.