Category: R

Andrew Collier works with some wide data:

The concept of “wide data” is relative. In some domains 100 columns is considered “wide”, while in others that’s perfectly normal and you’d need to have thousands (or tens of thousands!) of columns for it to be considered even remotely “wide”. The data that we work with at Fathom Data generally lies in the first domain, but from time to time we do work on data that is considerably wider.

This post touches on a couple of approaches for dealing with that sort of data. We’ll be using some HCRIS (Healthcare Cost Report Information System) data, which are available for download here. Specifically, we’ll be working with an extract from the hcris2552_10_2017.csv file, which contains “select variables in flat shape”.

Click through for one example which has 1700 columns. H/T R-Bloggers.

Holger von Jouanne-Diedrich solves a classic puzzle:

The following puzzle is a well-known meme in social networks. It is said to have been invented by young Einstein and back in the days I was ambitious enough to solve it by hand (you should try too!).

Yet, even simpler is to use Constraint Programming (CP). An excellent choice for doing that is MiniZinc, a free and open-source constraint modelling language. And the best thing is that you can control it by R! If you want to see how, read on!

I’d solved it once by hand as well, but here we get to see a much easier route. Constraint-based programming is one of those things which doesn’t show up very often in the business world, but I think part of the reason is that most programming languages lack the capacity to implement constraints really well. It could also be that people are usually pretty mushy about laying out proper constraints.

The folks at Jumping Rivers explain what the Parquet file format is and how you can use it in R:

Apache Parquet is a popular column storage file format used by Hadoop systems, such as Pig, Spark, and Hive. The file format is language independent and has a binary representation. Parquet is used to efficiently store large data sets and has the extension .parquet. This blog post aims to understand how parquet works and the tricks it uses to efficiently store data.

Read on for that explanation and plenty of sample code.

Tomaz Kastrun reminds me of xclock:

It is all about measuring time using useless clocks. Script takes a system time and displays any given clock in a rather “static” way. You can choose between analog, small digital and big digital clock. And when playing with the time, you can also learn something new.

Click through to see how to make an analog clock plot in R, and then try it again with a digital clock.

Tomaz Kastrun is already counting down to next year:

Progress bar showing the number of days until end of the year. Yes, only based on the days and not hour. And needless to say, the progress is soothing using ASCII charts.

So the function is fair to say, simple and useless.

In fairness, the function isn’t that useful but the technique is, particularly if you’re installing something or running a long process.

Gregory Janesch shows off the various pipes in the magrittr R package:

The magrittr package is a part of the extended tidyverse – i.e., not one of the ones normally loaded. It is the one that supplies the pipe operator (%>%), but it turns out that the package actually contains four pipe operators in total. All are intended to streamline and improve the readability of code, though the three non-basic ones are a bit more situational, and I’ve rarely seen them used, so I thought I would go into them a bit.

The CRAN page for magrittr is here; much of this post is based off of the package’s vignettes and documentation.

Click through for demonstrations of each. I’ve only seen the basic pipe in use as well, but the others look quite interesting and I can see use cases where knowing about them would be helpful. Also, note in the comments about the secret 5th pipe. H/T R-Bloggers