I am not sure apeescape’s ggplot2 area plot with intensity colouring is really the best way of presenting the information, but it had me intrigued enough to replicate it using base R graphics.

The key technique is to draw a gradient line which R does not support natively so we have to roll our own code for that. Unfortunately, lines(..., type="l") does not recycle the colour col= argument, so we end up with rather more loops than I thought would be necessary.

We also get a nice opportunity to use the under-appreciated read.fwf function.

We have a mild obsession with employee productivity and how that declines as companies get bigger. We have previously found that when you treble the number of workers, you halve their individual productivity which is scary.

We now re-do the analysis four years later and, just because we can, we are using the leading companies of the London stock exchange instead of the largest American companies.

The results still hold. We called it the 3/2 rule: treble the number of workers and you halve their individual productivity. Large companies with ten times the number of employees are ¼ as productive as their smaller competitors.

Employee productivity is a big issue. If all the FTSE-100 companies achieved their average profits per employee, then the index would generate almost £1 trn of additional net profits for the economy.

We have a mild obsession with employee productivity and how that declines as companies get bigger. We have previously found that when you treble the number of workers, you halve their individual productivity which is mildly scary.

We revisit the analysis for the FTSE-100 constituent companies and find that the relation still holds four years later and across a continent.

Following on from my previous post about improving performance of R by linking with optimized linear algebra libraries, I thought it would be useful to try out the five benchmarks Revolutions Analytics have on their Revolutionary Performance pages.

Can we make our analysis using the R statistical computing and analysis platform run faster? Usually the answer is yes, and the best way is to improve your algorithm and variable selection.

But recently David Smith was suggesting that a big benefit of their (commercial) version of R was that it was linked to a to a better linear algebra library. So I decided to investigate.

The quick summary is that it only really makes a difference for fairly artificial benchmark tests. For “normal” work you are unlikely to see a difference most of the time.