Same expression, but takes much less time in a loop on average - r

Question

I've been trying to find the computational expense with Sys.time(), starting with some simple operations.
I started with something like this
a=c(10,6,8,3,2,7,9,11,13)
t_beginning=Sys.time()
cl2=NULL
indx=which(a==7)
t_ending=Sys.time()
print(t_ending-t_beginning)
and it gives me about 0.0023sec after running the code in Rstudio.
Then the code is put into a for loop to find the average expense of the two lines.
sum=0
a=c(10,6,8,3,2,7,9,11,13)
for (i in 1:5) {
print(i)
t_beginning=Sys.time()
cl2=NULL
indx=which(a==7)
t_ending=Sys.time()
sum=t_ending-t_beginning+sum
print(t_ending-t_beginning)
}
sum/5
It turns out that, for every iteration in the for loop, the time consumption is just several milliseconds, much less than what it took as out of the for loop.
[1] 1
Time difference of 7.152557e-06 secs
[1] 2
Time difference of 5.00679e-06 secs
[1] 3
Time difference of 4.053116e-06 secs
[1] 4
Time difference of 4.053116e-06 secs
[1] 5
Time difference of 5.00679e-06 secs
I expect that the average time cost of the for loop to be about the same as that without a loop, but they are so different. Not sure why this is happening. Can anyone reproduce the same? Thanks!

Answer 1

The difference comes from the way RStudio (or R) runs the code.
The original code is executed line by line, so the timing you get includes interface between RStudio and R.
a=c(10,6,8,3,2,7,9,11,13)
t_beginning=Sys.time()
cl2=NULL
indx=which(a==7)
t_ending=Sys.time()
print(t_ending-t_beginning)
# Time difference of 0.02099395 secs
If, however, you run all this code at once, by wrapping the code in curvy brackets, the timing you get improve drastically:
{
a=c(10,6,8,3,2,7,9,11,13)
t_beginning=Sys.time()
cl2=NULL
indx=which(a==7)
t_ending=Sys.time()
print(t_ending-t_beginning)
}
# Time difference of 0 secs