nba_clusts

NBA positions dataset clustered using k-means with a range of values of <code>k</code>

datasets

Deciding what resolution to use can be a difficult question when
approaching a clustering analysis. One way to approach this problem is to
look at how samples move as the number of clusters increases. This package
allows you to produce clustering trees, a visualisation for interrogating
clusterings as resolution increases.

Luke Zappia

clustree

Visualise Clusterings at Different Resolutions

Alicia Oshlack

Andrea Rau

Paul Hoffman

nba_clusts function

<code>nba_clusts</code> is a data.frame containing the NBA positions dataset
with additional columns holding k-means clusterings at different values of
<code>k</code> and the first two principal components<ul>
<li>Position - Player position</li>
<li>TurnoverPct - Turnover percentage</li>
<li>ReboundPct - Rebound percentage</li>
<li>AssistPct - Assist percentage</li>
<li>FieldGoalPct - Field goal percentage</li>
<li>K1 - K5 - Results of k-means clustering</li>
<li>PC1 - First principal component</li>
<li>PC2 - Second principal component</li>
</ul>

Format

Clustered NBA positions dataset — nba_clusts

Clustered NBA positions dataset

nba_clusts: Clustered NBA positions dataset

Description

Usage

Arguments

Format