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**T**his is presumably obvious to most if not all R programmers, but I became aware today of a hugely (?) delaying tactic in my R codes. I was working with Jean-Michel and Natesh [who are visiting at the moment] and when coding an MCMC run I was telling them that I usually preferred to code Nsim=10000 as Nsim=10^3 for readability reasons. Suddenly, I became worried that this representation involved a computation, as opposed to Nsim=1e3 and ran a little experiment:

> system.time(for (t in 1:10^8) x=10^3) utilisateur système écoulé 30.704 0.032 30.717 > system.time(for (t in 1:1e8) x=10^3) utilisateur système écoulé 30.338 0.040 30.359 > system.time(for (t in 1:10^8) x=1000) utilisateur système écoulé 6.548 0.084 6.631 > system.time(for (t in 1:1e8) x=1000) utilisateur système écoulé 6.088 0.032 6.115 > system.time(for (t in 1:10^8) x=1e3) utilisateur système écoulé 6.134 0.029 6.157 > system.time(for (t in 1:1e8) x=1e3) utilisateur système écoulé 6.627 0.032 6.654 > system.time(for (t in 1:10^8) x=exp(3*log(10))) utilisateur système écoulé 60.571 0.000 57.103

So using the usual scientific notation with powers is taking its toll! While the calculator notation with e is cost free… Weird!

I understand that the R notation 10^6 is an abbreviation for a power function that can be equally applied to pi^pi, say, but still feel aggrieved that a nice scientific notation like 10⁶ ends up as a computing trap! I thus asked the question to the Stack Overflow forum, getting the (predictable) answer that the R code 10^6 meant calling the R power function, while 1e6 was a constant. Since 10⁶ does not differ from π^{π}, there is no reason 10⁶ should be recognised by R as a million. Except that it makes my coding more coherent.

> system.time( for (t in 1:10^8) x=pi^pi) utilisateur système écoulé 44.518 0.000 43.179 > system.time( for (t in 1:10^8) x=10^6) utilisateur système écoulé 38.336 0.000 37.860

Another thing I discovered from this answer to my question is that negative integers are also requesting call to a function:

> system.time( for (t in 1:10^8) x=1) utilisateur système écoulé 10.561 0.801 11.062 > system.time( for (t in 1:10^8) x=-1) utilisateur système écoulé 22.711 0.860 23.098

This sounds even weirder.

Filed under: Books, Kids, R, Statistics, University life Tagged: exponent notation, exponentiation, functions in R, mantissa, power, R, scientific notation, system.time

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