library(plyr)
library(ggplot2)

id.test <- function(n) {
  a <- seq_len(n)
  b <- a
  system.time(r_ply(10000, function() identical(a, b)))
}

adply(
  data.frame(n=2 ^ seq_len(25)),
  1,
  function(n) id.test(n[1, ])
) -> timings

summary(lm(user.self~n, timings))

## 
## Call:
## lm(formula = user.self ~ n, data = timings)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.001584 -0.000584 -0.000579  0.000416  0.005416 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.26e-02   3.73e-04   60.61   <2e-16 ***
## n           -3.87e-11   4.81e-11   -0.81     0.43    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.00175 on 23 degrees of freedom
## Multiple R-squared:  0.0274, Adjusted R-squared:  -0.0148 
## F-statistic: 0.649 on 1 and 23 DF,  p-value: 0.429

ggplot(timings) + geom_point(aes(x=n, y=user.self)) + scale_x_log10()