Finding clusters

Lab 4H

Directions: Follow along with the slides, completing the questions in blue on your computer, and answering the questions in red in your journal.

Space, Click, Right Arrow or swipe left to move to the next slide.

Clustering data

We’ve seen previously that data scientists have methods to predict values of specific variables.
- We used regression to predict numerical values and classification to predict categories.
Clustering is similar to classification in that we want to group people into categories. But there’s one important difference:
- In clustering, we don’t know how many groups to use because we’re not predicting the value of a known variable!
In this lab, we’ll learn how to use the k-means clustering algorithm to group our data into clusters.

The k-means algorithm

The k-means algorithm works by splitting our data into k different clusters.
- The number of clusters, the value of k, is chosen by the data scientist.
The algorithm works only for numerical variables and only when we have no missing data.
To start, use the data function to load the futbol dataset.
- This data contains 24 players from the US Men’s National Soccer team (USMNT) and 33 quarterbacks from the National Football League (NFL).
(1) Write and run code creating a scatterplot of the players’ ht_inches and wt_lbs and color each dot based on the league they play for.

Running k-means

After plotting the players’ heights and weights, we can see that there are two clusters, or different types, of players:
- Players in the NFL tend to be taller and weigh more than the shorter and lighter USMNT players.
(2) Fill in the blanks below to use k-means to cluster the same height and weight data into two groups:

kclusters(____~____, data = futbol, k = ____)

(3) Use this code and the mutate function to add the values from kclusters to the futbol data. Name the variable clusters.

k-means vs. ground-truth

In comparing our football and soccer players, we know for certain which league each player plays in. We call this knowledge ground-truth.
Knowing the ground-truth for this example is helpful to illustrate how k-means works, but in reality, data-scientists would run k-means not knowing the ground-truth.
(4) Compare the clusters chosen by k-means to the ground-truth. How successful was k-means at recovering the league information?

On your own

Load your class’s timeuse data (remember to run timeuse_format so each row represents the mean time each student spent participating in the various activities).
(5) Write and run code creating a scatterplot of homework and videogames variables.
(6) Based on this graph, identify and remove any outliers by using the filter function.
(7) Write and run code using kclusters with k=2 for homework and videogames.
(8) Describe how the groups differ from each other in terms of how long each group spends playing videogames and doing homework.