This article introduces you to similar clustering analysis on your data using **Tableau** and **R**. Data files and source used in this post can be downloaded using the link below.

Clustering is the assignment of a set of observations into subsets (called clusters) so that observations in the same cluster are similar in some sense. We will perform the analysis in both Tableau and R using the same data.

**Clustering Analysis using Tableau**

To start with, we connect Tableau to Iris data set. You can download this from UCI Machine Learning Repository

First connect the Tableau work book to this csv data source and launch a new sheet. Drag the measures petal length, petal width to columns and sepal length, width to rows.

Next, disable aggregation of measures using *Analysis->Aggregate Measures*

Alternately, to keep it simple, you can choose to analyze only 2 measures as shown below. But in this article, we go with all 4 measures as above

If you observe, these scatter-plots does not identify or differentiate any groups. However in our case, the data set already has a column specifying flower species of these measures. So let us view it by dragging ‘Species’ to color which shows the distinct species groups as below:

Well, imagine what if we didn’t had the ‘species’ data handy and we wanted to identify the clusters based on the measures. Lets see how it can be accomplished using Tableau Cluster Analysis.

Start with our initial plot, i.e.

Go to **Analytics** tab, and drag ‘Clusters’ as shown in the screen capture below. Tableau automatically identifies the number of clusters.

Leave the defaults

Note that we have got exactly same cluster grouping as we got using ‘Species’ dimensi.on data.

Tableau uses **k-means algorithm** for cluster analysis which partitions the data into k clusters with a center or mean value of all the points in each. Clustering is based on the distance each measure lies from the center.

**Cluster Analysis using R**

To start with, let us revisit Tableau plot for iris data between petal.length and petal.width with cluster analysis.

Let’s go to **RStudio**, and plot this using **ggplot** (note that iris is available as part of the datasets installed with R)

library(ggplot2)
ggplot(iris, aes(Petal.Width, Petal.Length, color = Species)) +
geom_point()

Note that we get identical grouping in R plot, but we used **species** column data to group (color) the data.

Let us look at how to perform the cluster analysis to identify clusters in R.

First take a copy of iris dataset to another variable

#cluster analysis - Biju Paulose
#-------------------------------
#copying iris to myiris variable
myiris<-iris
#printing data
myiris

For our analysis, we do not want to use species column/data. So lets remove that from the new dataset.

#Remove Species column
myiris$Species <- NULL
#printing data to verify
myiris

Lets use k-means function for generating 3 clusters and plot the data

kmeans.result <- kmeans(myiris,3)
# plot the clusters
plot(myiris[c("Petal.Width", "Petal.Length")],col=kmeans.result$cluster)

The result is given below. As you can compare with the analysis performed in Tableau above, we could generate the same clustering of data from R. We will examine these more closely in my next article.