If you are un-familiar with this particular methodology, or if you wish to re-familiarize yourself, please consult the following article: K-Means Cluster (SPSS).

__Example:__For this demonstration, we will be utilizing the same data set which was previously utilized to demonstrate the analytical process within the SPSS platform. This data set can be found within this site’s GitHub Repository.

**# Load the data into the R platform #**

# Be sure to change the ‘filepathway’ so that it matches the file location on your #

# Be sure to change the ‘filepathway’ so that it matches the file location on your #

**# computer #**

KMeans <- read.table("C:\\filepathway\\kmeans.csv", fill = TRUE, header = TRUE, sep = "," )

# We’re going to assume that the variables: ‘ZCont_Var1’, ‘ZCont_Var2’, ‘ZCont_3’ #

KMeans <- read.table("C:\\filepathway\\kmeans.csv", fill = TRUE, header = TRUE, sep = "," )

# We’re going to assume that the variables: ‘ZCont_Var1’, ‘ZCont_Var2’, ‘ZCont_3’ #

**# are not**

**included within the initial data frame. #**

# Therefore, we must scale the variables: ‘Cont_Var1, ‘Cont_Var2’, ‘Cont_Var3’ prior to #

# Therefore, we must scale the variables: ‘Cont_Var1, ‘Cont_Var2’, ‘Cont_Var3’ prior to #

**# performing analysis. #**

ScaledKMeans <- scale(KMeans[4:6])

# In this example, we are going to create a two cluster model. Also, we will be utilizing #

ScaledKMeans <- scale(KMeans[4:6])

# In this example, we are going to create a two cluster model. Also, we will be utilizing #

**# an**

**‘n’ value of 10. This figure represents the number of iterations which will be #**

**# attempted**

**while**

**the underlying mechanism of the model decides on an #**

**# appropriate configuration. #**

ScaledKMeansCluster <- kmeans(ScaledKMeans, 2, nstart = 10)

# Once the model has been created, we will assign the cluster values to a data frame #

ScaledKMeansCluster <- kmeans(ScaledKMeans, 2, nstart = 10)

# Once the model has been created, we will assign the cluster values to a data frame #

**# in**

**order**

**to discern which cluster categorizations pertain to each observational value. #**

KMeans$ClusterID <- as.factor(ScaledKMeansCluster$cluster)

KMeans$ClusterID <- as.factor(ScaledKMeansCluster$cluster)

# Finally, we will graph the results of the analysis. The variables which will represent #

# Finally, we will graph the results of the analysis. The variables which will represent #

**# the**

**scales of the graph’s axis are: Zcont_Var1 and Zcont_Var2. #**

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