K-means on a soccer field (part 2)

In the previous exercise, you successfully used the k-means algorithm to cluster the two teams from the lineup data frame. This time, let's explore what happens when you use a k of 3.

You will see that the algorithm will still run, but does it actually make sense in this context…

This exercise is part of the course

Cluster Analysis in R

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Exercise instructions

Build a k-means model called model_km3 for the lineup data using the kmeans() function with centers = 3.
Extract the vector of cluster assignments from the model model_km3$cluster and store this in the variable clust_km3.
Append the cluster assignments as a column cluster to the lineup data frame and save the results to a new data frame called lineup_km3.
Use ggplot to plot the positions of each player on the field and color them by their cluster.

Hands-on interactive exercise

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

# Build a kmeans model
model_km3 <- ___

# Extract the cluster assignment vector from the kmeans model
clust_km3 <- ___

# Create a new data frame appending the cluster assignment
lineup_km3 <- ___

# Plot the positions of the players and color them using their cluster
ggplot(___, aes(x = ___, y = ___, color = factor(___))) +
  geom_point()

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