Introduction to Probability and Statistics

R Tutorial: Run Length Encoding

This is a short tutorial to expand on the R reading questions. It will help you with one of the problems in problem set 2.

Color Coding

# Comments are in maroon 
Code is in black 
Results are in this shade of green    

rle(x)

# rle(x) stands for ‘run length encoding’. It will be easiest to explain what this means through examples. It will help with pset 2 in the question that asks you to estimate the probability of runs in a sequence of Bernoulli (coin flips) trials.  A run means a streak of repeats of the same number.   

# First let’s make a small sequence where we can see the runs 

x = c(1,1,1,2,3,3,3,1,1) 

# We can describe this sequence as: three 1’s, then one 2, then three 3’s and two 1’s. 

# This is exactly what rle(x) shows us 

y = rle(x) 
print(y) 
Run Length Encoding  
  lengths: int [1:4] 3 1 3 2  
  values : num [1:4] 1 2 3 1 

# The values vector shows the values in the order they appeared. In this case the values of x are: 1, 2, 3, 1. 

# The lengths vector shows the lenghts of the runs of each value. In this case, three 1’s, one 2, three 3’s and two 1’s. 

# To pick out just the lengths vector you use the syntax y$lengths 

print(y$lengths) 
[1] 3 1 3 2 

# Let’s look for streaks in a sequence of Bernoulli trials 
# We simulate 20 Bernoulli(0.5) trials using rbinon(20, 1, 0.5). 

set.seed(1) 
y = rbinom(50, 1, 0.5) 

# y is a vector of 0’s and 1’s of length 20. 
# We can use rle() to find the length of the longest run in y 

m = max(rle(y)$lengths) 
print(m)
[1] 6 

# We can count the number of runs of more than 3. 

s = sum(rle(y)$lengths > 3) 
print(s) 
[1] 3 

# We can count the number of runs of exactly length 3. 

s = sum(rle(y)$lengths == 3) 
print(s) 
[1] 2