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…

Diese Übung ist Teil des Kurses

<Kurs>Cluster Analysis in R</Kurs>

Kurs ansehen

Übungsanweisungen

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.

Interaktive praktische Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

# 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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