booma <- scan(nlines = 1)
150 151 125 126 124 116  92 119 145 140  99  76 119  88 137  74 151  66 118 151

boomb <- scan(nlines = 2)
133 152 184 129 144 111 136 140 143 115 119 161  88 159 125  99 114 112 123 
118 151 150 123

diff <- mean(booma) - mean(boomb)
diff

boom <- c(booma, boomb)

n_perm <- 1000
# n_perm should probably be set to at least 5000 or 10000

permdiff = NULL

# this sets up an "empty variable".  All this does is let R know that "permdiff"
# exists, but it tells R to keep it empty (otherwise R would give us errors 
# below when we refer to "permdiff"

for (i in 1 : n_perm) {
  
  # This sets up the loop.  "for" says" do everything after the "{", 
  # letting "i" equal 1 to n_perm. For example, with n_perm = 1000,
  # this will run through the loop 1000 times.
  
  newboom <- sample(boom, length(boom))
  
  # Basically what this does is re-arrange the combined sample (step 3).
  # We tell "sample" to sample from "boom", and to take a sample
  # that's the same length as "boom".  All this does is re-arrange
  # our sample.
  
  aboom <- newboom[1 : length(booma)]
  bboom <- newboom[(length(booma) + 1) : length(boom)]
  
  # Now we take this re-arranged sample (newboom) and divide it into two 
  # samples, each of which is the same length as the original samples.
  # 
  # Note that the first sample is obviously of length "booma", and the
  # second sample is then everything else (from "booma + 1" to "boom")
  #
  
  permdiff[i] <- mean(aboom) - mean(bboom)
  
  # Calculates the difference in the means of our "new" samples.
  # We will have n_perm of these differences (e.g, if n_perm = 1000,
  # then we will have 1000 differences)
  
}

ndiffs <- sum(abs(permdiff) >= abs(diff))

# This is a logical expression.  It is true if |permdiff| >= |diff|, and 
# then has the value 1.  So for every difference that we get from our loop
# that is bigger than our observed difference, we count "1".
# We can look at it by doing:

ndiffs

p <- ndiffs/n_perm

# We just figure out the proportion of our sampled differences
# that are greater than our observed difference.

p