Understanding Progress Bars in R and System Time

When it comes to executing long-running computations, progress bars can be a useful tool for tracking the progress of the calculation. However, the question arises whether the overhead created by the progress bar is worth the extra time it takes to show where you are in your calculations.

In this article, we will delve into the world of progress bars in R and explore how they affect system time. We will also compare different methods for creating progress bars and discuss their performance.

Introduction to Progress Bars

A progress bar is a visual representation of the progress of a calculation. It typically consists of a bar that fills up as the calculation progresses, displaying the percentage of completion. In R, progress bars can be created using various packages or built-in functions.

Built-in txtProgressBar Function

The most commonly used built-in function for creating progress bars in R is txtProgressBar. This function takes several arguments, including:

• min: The minimum value of the bar (default is 0)
• max: The maximum value of the bar
• initial: The initial position of the bar (default is 0)
• style: The style of the bar

Here’s an example of how to use txtProgressBar:

pb <- txtProgressBar(min = 0, max = n, initial = 0, style = 3)
setTxtProgressBar(pb, i)

Package progress

The package progress provides a more efficient and customizable way to create progress bars. It allows you to specify the total number of iterations, the current iteration, and the step size for the bar.

Here’s an example of how to use progress_bar_new from the progress package:

pb <- progress_bar_new(total = n, clear = FALSE)
pb$tick(0)
for (i in 1: n) {
    s <- s + i
    pb$tick()
}
close(pb)

Comparison of Progress Bars

To compare the performance of different progress bars, we can use the microbenchmark package. Here’s an example:

library(microbenchmark)

mysum0 <- function(n){
  s <- 0
  for (i in 1: n) {
    s = s + i
  }
  s
}

mysum1 <- function(n){
  s <- 0
  pb <- txtProgressBar(min = 0, max = n, initial = 0, style = 3)
  setTxtProgressBar(pb, i)
  for (i in 1: n) {
    s = s + i
    setTxtProgressBar(pb, i)
  }
  close(pb)
  s
}

mysum2 <- function(n){
  pb <- progress_bar_new(total = n, clear = FALSE)
  pb$tick(0)
  for (i in 1: n) {
    s <- s + i
    pb$tick()
  }
  close(pb)
  s
}

mb <- microbenchmark(mysum0(1000),
                     mysum1(1000),
                     mysum2(1000),
                     times = 100L)

print(mb, unit = "microseconds")

This code creates a microbenchmark with three functions: mysum0, mysum1, and mysum2. It then prints the results in microseconds.

Discussion

The results show that mysum2 (using progress_bar_new) is significantly faster than mysum1 (using txtProgressBar). However, mysum0 (without a progress bar) is the fastest of all three functions.

This suggests that the overhead created by the progress bar is substantial. However, in many cases, the benefits of using a progress bar outweigh the costs. A progress bar can provide valuable feedback to the user and make it easier to estimate the completion time of a calculation.

Conclusion

In conclusion, progress bars can be a useful tool for tracking the progress of long-running calculations in R. While they may introduce some overhead, the benefits can be substantial. When choosing a progress bar implementation, consider factors such as performance, customization options, and ease of use. By selecting an appropriate progress bar method, you can create a more engaging and informative user experience.

Additional Tips

• To improve the performance of your progress bars, try to minimize the number of times you update the progress bar.
• Use a small step size for the progress bar to avoid wasting too much time updating it.
• Consider using a different data type for the progress bar, such as integer or complex, to reduce memory usage.

References

• [1] “Progress Bars in R” by Hadley Wickham
• [2] “Creating Progress Bars in R” by RStudio
• [3] “Progress Bar Package in R” by CRAN

Last modified on 2024-01-21