In this post, we will look at how to visualize multivariate clustered data. We will use the “Hitters” dataset from the “ISLR” package. We will use the features of the various baseball players as the dimensions for the clustering. Below is the initial code

library(ISLR);library(cluster)
data("Hitters")
str(Hitters)

## 'data.frame':    322 obs. of  20 variables:
##  $ AtBat    : int  293 315 479 496 321 594 185 298 323 401 ...
##  $ Hits     : int  66 81 130 141 87 169 37 73 81 92 ...
##  $ HmRun    : int  1 7 18 20 10 4 1 0 6 17 ...
##  $ Runs     : int  30 24 66 65 39 74 23 24 26 49 ...
##  $ RBI      : int  29 38 72 78 42 51 8 24 32 66 ...
##  $ Walks    : int  14 39 76 37 30 35 21 7 8 65 ...
##  $ Years    : int  1 14 3 11 2 11 2 3 2 13 ...
##  $ CAtBat   : int  293 3449 1624 5628 396 4408 214 509 341 5206 ...
##  $ CHits    : int  66 835 457 1575 101 1133 42 108 86 1332 ...
##  $ CHmRun   : int  1 69 63 225 12 19 1 0 6 253 ...
##  $ CRuns    : int  30 321 224 828 48 501 30 41 32 784 ...
##  $ CRBI     : int  29 414 266 838 46 336 9 37 34 890 ...
##  $ CWalks   : int  14 375 263 354 33 194 24 12 8 866 ...
##  $ League   : Factor w/ 2 levels "A","N": 1 2 1 2 2 1 2 1 2 1 ...
##  $ Division : Factor w/ 2 levels "E","W": 1 2 2 1 1 2 1 2 2 1 ...
##  $ PutOuts  : int  446 632 880 200 805 282 76 121 143 0 ...
##  $ Assists  : int  33 43 82 11 40 421 127 283 290 0 ...
##  $ Errors   : int  20 10 14 3 4 25 7 9 19 0 ...
##  $ Salary   : num  NA 475 480 500 91.5 750 70 100 75 1100 ...
##  $ NewLeague: Factor w/ 2 levels "A","N": 1 2 1 2 2 1 1 1 2 1 ...

Data Preparation

We need to remove all of the factor variables as the kmeans algorithm cannot support factor variables. In addition, we need to remove the “Salary” variable because it is missing data. Lastly, we need to scale the data because the scaling affects the results of the clustering. The code for all of this is below.

hittersScaled<-scale(Hitters[,c(-14,-15,-19,-20)])

Data Analysis

We will set the k for the kmeans to 3. This can be set to any number and it often requires domain knowledge to determine what is most appropriate. Below is the code

kHitters<-kmeans(hittersScaled,3)

We now look at some descriptive stats. First, we will see how many examples are in each cluster.

table(kHitters$cluster)

## 
##   1   2   3 
## 116 144  62

The groups are mostly balanced. Next, we will look at the mean of each feature by cluster. This will be done with the “aggregate” function. We will use the original data and make a list by the three clusters.

round(aggregate(Hitters[,c(-14,-15,-19,-20)],FUN=mean,by=list(kHitters$cluster)),1)

##   Group.1 AtBat  Hits HmRun Runs  RBI Walks Years CAtBat  CHits CHmRun
## 1       1 522.4 143.4  15.1 73.8 66.0  51.7   5.7 2179.1  597.2   51.3
## 2       2 256.6  64.5   5.5 30.9 28.6  24.3   5.6 1377.1  355.6   24.7
## 3       3 404.9 106.7  14.8 54.6 59.4  48.1  15.1 6480.7 1783.4  207.5
##   CRuns  CRBI CWalks PutOuts Assists Errors
## 1 299.2 256.1  199.7   380.2   181.8   11.7
## 2 170.1 143.6  122.2   209.0    62.4    5.8
## 3 908.5 901.8  694.0   303.7    70.3    6.4

Now we can see some difference. It seems group 3 are young (5.6 years of experience) starters based on the number of at-bats they get. Group 1 is young players who may not get to start due to the lower at-bats the receive. Group 2 is old (15.1 years) players who receive significant playing time and have but together impressive career statistics.

Now we will create our visual of the three clusters. For this, we use the “clusplot” function from the “cluster” package.

clusplot(hittersScaled,kHitters$cluster,color = T,shade = T,labels = 4)

In general, there is little overlap between the clusters. The overlap between groups 1 and 3 may be due to how they both have a similar amount of experience.

Conclusion

Visualizing the clusters can help with developing insights into the groups found during the analysis. This post provided one example of this.