Category: R

Mala Mahadevan looks at using R to describe data sets with two explanatory variables:

From the plot we can see that type 3 trees have the smallest circumference while type 4 have the largest, with type 2 close to type 4. We can also see that type 1 trees have the thinnest dispersion of circumference while type 4 has the highest, closely followed by type 2.  We can also see that there are no significant outliers in this data.

Understanding whether variables are categorical or continuous is vital to understanding what you can and should do with them.

Troy Walters uses the Tabulizer package to extract tables from a PDF and turn them into an R matrices or data frames:

Next we will use the extract_tables() function from tabulizer. First, I specify the url of the pdf file from which I want to extract a table. This pdf link includes the most recent data, covering the period from July 1, 2016 to November 25, 2016. I am using the default parameters for extract_tables. These are guess and method. I’ll leave guess set to TRUE, which tells tabulizer that we want it to figure out the locations of the tables on its own. We could set this to FALSE if we want to have more granular control, but for this application we don’t need to. We leave the method argument set to “matrix”, which will return a list of matrices (one for each pdf page). This could also be set to return data frames instead.

This is nice.  I have to imagine it only works for text-based PDFs and not ones which are generated from a series of images.

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.