How to Plot in 3D clusters using plotly package

R-Language is widely used in Data Science, Data Visualization Data Analysis many more, etc. Plotly package is highly rich in plotting various graphs, These graphs/charts are highly interactive and user-friendly.

The 3D cluster visualizes the similarity between variables as 3-D spatial relationships. Each point in the graph represents an individual property.  the points which are closer together were more frequently sorted into the same category or class.

Using Example:
Plotting the following dataset in 3-D plot using plotly library in R Programming Language.

x	y	z
1	10	2
2	20	4
3	30	8
4	40	16
5	50	32

Syntax:

plot_ly(data_object, x, y, z)

where:

• data_object: Represents the dataset or dataframe object.
• x : Represent x-data vector.
• y : Represent y-data vector.
• z : Represent z-data vector.

Example 1: In this example, we will create an exemplary dataset and then plot a 3D Cluster graph using that.

R

#Importing plotly library
library(plotly)

#Creating Dataframe
x = c(1, 2, 3, 4, 5)
y = c(10, 20, 30, 40, 50)
z = c(2, 4, 8, 16, 32)
df = data.frame(x, y, z)
df

#Plotting 3-D Scatter plot.
#Pass dataframe and axes
plt <- plot_ly(df, x = ~x, y = ~y, z = ~z)

#Add markers to the chart
plt <- plt %>% add_markers()

#Labeling the axes.
plt <- plt %>% layout(scene = list(xaxis = list(title = 'x-axis'),
                                   yaxis = list(title = 'y-axis'),
                                   zaxis = list(title = 'z-axis')))
plt

Output:

Example 2: In this example, we will use the iris dataset and then plot it with the original labels using any three independent features.

R

#Importing Library
library(plotly)

#Using iris dataset
#Removing Categorical Values
data = iris[, 1:4]

#Finding Clusters
data$cluster = factor(kmeans(data, 3)$cluster)

#Plotting the Data
clust<- plot_ly(data, x=~Sepal.Length,
                y=~Sepal.Width,
                z=~Petal.Width,
                color=~cluster) %>%
add_markers(size=1.5)

#Printing 3-D Clusters
clust

Output: