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
```