Interlude: Breaking it down

We will now consider the code in the editor line by line to figure out what it does. Next we'll have a small exercise to make sure you understood it.

In the first line we initialize a vector. In this case, we created a vector of 5000 NAs called sample means50. This vector will store values generated within the for loop. NA means not available, and in this case they're used as placeholders until we fill in the values with actual sample means. NA is also often used for missing data in R.

The second line calls the for loop itself. The syntax can be loosely read as, "for every element i from 1 to 5000, run the following lines of code". You can think of i as the counter that keeps track of which loop you're on. Therefore, more precisely, the loop will run once when i=1, then once when i=2, and so on up to i=5000.

The body of the for loop is the part inside the curly braces, and this set of code is run for each value of i. Here, on every loop, we take a random sample of size 50 from area, take its mean, and store it as the ith element of sample_means50. In order to display that this is really happening, we asked R to print i at each iteration. This line of code is optional and is only used for displaying what's going on while the for loop is running.

The for loop allows us to not just run the code 5000 times, but to neatly package the results, element by element, into the empty vector that we initialized at the outset.

This exercise is part of the course

Data Analysis and Statistical Inference

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Exercise instructions

Carefully read the comments in the editor to understand what each line of the code does.
Run the code.

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

# The vector sample_means50 is initialized with NA values
sample_means50 <- rep(NA, 5000)

# The for loop runs 5000 times, with i taking values 1 up to 5000
for (i in 1:5000) {
  # Take a random sample of size 50
  samp <- sample(area, 50)
  # Store the mean of the sample in the sample_means50 vector on the ith place
  sample_means50[i] <- mean(samp)
  # Print the counter i
  print(i)
}

# Print the first few random means
head(sample_means50)

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