K-means on a soccer field

In the previous chapter, you used the lineup dataset to learn about hierarchical clustering, in this chapter you will use the same data to learn about k-means clustering. As a reminder, the lineup data frame contains the positions of 12 players at the start of a 6v6 soccer match.

Just like before, you know that this match has two teams on the field so you can perform a k-means analysis using k = 2 in order to determine which player belongs to which team.

Note that in the kmeans() function k is specified using the centers parameter.

Deze oefening maakt deel uit van de cursus

Cluster Analysis in R

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Oefeninstructies

Build a k-means model called model_km2 for the lineup data using the kmeans() function with centers = 2.
Extract the vector of cluster assignments from the model model_km2$cluster and store this in the variable clust_km2.
Append the cluster assignments as a column cluster to the lineup data frame and save the results to a new data frame called lineup_km2.
Use ggplot to plot the positions of each player on the field and color them by their cluster.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Build a kmeans model
model_km2 <- kmeans(___, centers = ___)

# Extract the cluster assignment vector from the kmeans model
clust_km2 <- ___

# Create a new data frame appending the cluster assignment
lineup_km2 <- mutate(___, cluster = ___)

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

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