Category: R

Frank Portman shows how to figure out how many taxicabs—or tanks—there are:

For the uninitiated, the Taxicab / Germany Tank problem is as follows:

Viewing a city from the train, you see a taxi numbered x. Assuming taxicabs are consecutively numbered, how many taxicabs are in the city?

This was also applied to counting German tanks in World War II to know when/if to attack. Statstical methods ended up being accurate within a few tanks (on a scale of 200-300) while “intelligence” (unintelligence) operations overestimated numbers about 6-7x. Read the full details on Wikipedia here (and donate while you’re over there).

Click through for the solution and how to implement it in R.

Peter Ellis defends pie charts under very specific circumstances:

The usual response from statisticians and data professionals to pie charts ranges from lofty disdain to outright snobbery. But sometimes I think they’re the right tool for communication with a particular audience. Like others I was struck by this image from New Zealand news site stuff.co.nz showing that nearly half the earthquake energy of the past six years came in one day (last Sunday night, and the shaking continues by the way). Pie charts work well when the main impression of relative proportions to the whole is obvious, and fine comparisons aren’t needed.

Here’s my own version of the graphic. I polished this up during a break while working at home due to the office being shut for earthquake-related reasons:

Consider me in the lofty disdain camp.  That said, this is probably the best case scenario for a pie chart:  when looking at relative percentage of one dominant element versus the remaining set.

I look at one use of R for DBAs:

I have a client data warehouse which holds daily rollups of revenue and cost for customers.  We’ve had some issues with the warehouse lately where data was not getting loaded due to system errors and timeouts, and our services team gave me a list of some customers who had gaps in their data due to persistent processing failures.  I figured out the root cause behind this (which will show up as tomorrow’s post), but I wanted to make sure that we filled in all of the gaps.

My obvious solution is to write a T-SQL query, getting some basic information by day for each customer.  I could scan through that result set, but the problem is that people aren’t great at reading tables of numbers; they do much better looking at pictures.  This is where R comes into play.

Click through for the code and a walkthrough of what each line is doing.

John Pertell runs into an issue reading a file on a network share using SQL Server R Services:

What I’m trying to do should be pretty easy. I want to use R code inside a stored procedure to read all the log files created by my weather program and store the results in a database. I also want to read the current monthly file on a regular basis, at least once a day. Once the data is in the database I’ll create some mobile reports with data and charts I can read on my phone. I’ll also be able to use my own data to play with local weather predicting. I thought it would make a pretty cool demo for my R sessions.

However when I run my stored procedure to read the logs I receive an error that there is no such file if I map the share as a drive, or I’m using an invalid parameter if I try to access the share directly.

Read on for code and the specific error message.

RStudio has officially hit 1.0:

While RStudio has been an enormously useful IDE for R since day 1, it’s officially been in “beta” status all of this time. But last week, RStudio released the first official production version, RStudio 1.0. Check out that link for the release history of RStudio and all that’s been added to it over the last 6 years, but this release also adds major new functionality, including:

R Tools for Visual Studio is certainly making strides, but RStudio is the gold standard for R IDEs.