Interlude: The for loop
Let's take a break from the statistics for a moment to let that last block of code sink in. You have just run your first for loop, a cornerstone of computer programming. The idea behind the for loop is iteration: it allows you to execute code as many times as you want without having to type out every iteration.
In the case above, we wanted to iterate the two lines of code inside the curly braces that take a random sample of size 50 from area then save the mean of that sample into the sample_means50 vector. Without the for loop, this would be painful:
sample_means50 <- rep(NA, 5000)
samp <- sample(area, 50)
sample_means50[1] <- mean(samp)
samp <- sample(area, 50)
sample_means50[2] <- mean(samp)
samp <- sample(area, 50)
sample_means50[3] <- mean(samp)
samp <- sample(area, 50)
sample_means50[4] <- mean(samp)
and so on...
With the for loop, these thousands of lines of code are compressed into a handful of lines.
This exercise is part of the course
Data Analysis and Statistical Inference
Exercise instructions
We've added one extra line to the code, which prints the object i during each iteration of the for loop. Run the code in the editor and inspect its output.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# The ames data frame and area and price objects are already loaded into the workspace
# Set up an empty vector of 5000 NAs to store sample means:
sample_means50 <- rep(NA, 5000)
# Take 5000 samples of size 50 of area and store all of them in sample_means50.
# Also print i (the index counter):
for (i in 1:5000) {
samp <- sample(area, 50)
sample_means50[i] <- mean(samp)
print(i)
}