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…

Latihan ini adalah bagian dari kursus

Cluster Analysis in R

Lihat Kursus

Petunjuk latihan

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.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# 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()

Edit dan Jalankan Kode