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Counting nodes and computing connectance

In this exercise, you will count the number of each type of node using the customers dataframe. The churn column has two different values:

  • 0 for non-churners
  • 1 for churners

You will also compute the network's connectance using the formula \(p=\frac{2E}{N(N-1)}\) where \(N\) is the number of nodes and \(E\) the number of edges in the network.

This exercise is part of the course

Predictive Analytics using Networked Data in R

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Exercise instructions

  • Count the number of churn nodes by conditioning on customers$churn.
  • Count the number of non-churn nodes by conditioning on customers$churn.
  • Count the total number of nodes and name the variable nodes.
  • Compute the network's connectance using the formula for \(p\) shown above. You can use edges from the previous exercise.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Count the number of churn nodes
ChurnNodes <- sum(customers$___ == ___)
 
# Count the number of non-churn nodes
NonChurnNodes <- sum(___)
 
# Count the total number of nodes
___ <- ChurnNodes + NonChurnNodes
 
# Compute the network connectance
connectance <- 2 * ___ / ___ / (nodes - 1)

# Print the value
connectance
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