## Key points:

- R kmean clustering fill color region give us a better visualizing of the results
- K-means clustering is a more famous unsupervised machine learning technique that partitions data points into k groups based on similarity.
- A common way to visualize k-means clustering is to plot the data points in a scatter plot and color them according to their cluster membership, but this can be misleading or confusing if the clusters overlap or are not well separated.
- A better way to visualize in kmean clustering fill color region fill the regions of the plot with different colors corresponding to the clusters using the geom_mark_ellipse() function from the ggforce package.
- This function adds ellipses around groups of points based on their mean and covariance and allows us to map the cluster variable to the fill aesthetic using the aes() function.
- We can also add labels to the ellipses using the geom_text_repel() function from the ggrepel package, which repels overlapping text labels away from each other and from the data points.

I am Zubair Goraya, a Certified data analyst and RStudio enthusiast. This article will show you how to fill color regions in R k-means clustering using simple steps and tricks.

## K-means clustering

K-means clustering is a popular unsupervised machine learning technique that partitions data points into k groups based on similarity. It helps explore the structure and patterns of your data and reduces the dimensionality and noise.

**One of the challenges of k-means clustering is how to visualize the
results, especially when you have more than two dimensions or variables in your
data. **

A common way to do this is to plot the data points in a scatter plot and color them according to their cluster membership. However, this can be misleading or confusing if the clusters overlap or are not well separated.

## R kmean clustering fill color region.

A better way to visualize k-means clustering is to fill the regions of the plot with different colors corresponding to the clusters. This way, you can see the boundaries and shapes of the clusters, as well as their relative sizes and positions.

Filling color regions in R k-means clustering is relatively easy, but it requires some extra steps and packages that I will explain in this article.

### Step 1: Load the required packages

To fill color regions in R k-means clustering, we will need the following packages:

- ggplot2: A powerful and elegant package for creating beautiful graphics in R.
- ggrepel: A package that extends ggplot2 by adding functions to repel overlapping text labels.
- ggforce: We will use a package with valuable extensions and geoms for ggplot2, such as geom_mark_ellipse(), to draw ellipses around the clusters.
- tidyverse: A collection of packages that make data manipulation and analysis more accessible and consistent in R.

You can install these packages from CRAN using the install.packages() function, or load them from your library if you already have them installed.

# Install packages install.packages(c("ggplot2", "ggrepel", "ggforce", "tidyverse")) # Load packages library(ggplot2) library(ggrepel) library(ggforce) library(tidyverse)

### Step 2: Generate some sample data

This example will use synthetic data generated using the rnorm() function. This function creates random numbers from a normal distribution by giving the standard deviation and mean.

We will create 2 variables, y, and x, with 100 observations each, and assign them to a data frame called df.

# Set seed for reproducibility set.seed(123) # Generate sample data x <- create="" data.frame="" data="" df="" frame="" mean="0," pre="" rnorm="" sd="1)" x="" y="">

### Step 3: Perform k-means clustering

The next step is to cluster k-means on our sample data using the kmeans() function from the stats package. To find the optimal number of cluster, we can use elbow method. The elbow method is a technique that helps you find the optimal value of k for k-means clustering This function takes as arguments:

- x: A data frame or numeric matrix containing the data to be clustered.
- Centers: The number of clusters (k) or a set of initial cluster centers.
- Iter.max: The maximum number of iterations allowed.
- nstart: The number of random starts to try.

For this example, we will use 4 clusters and 10 random starts. The function returns an object of class "kmeans" that contains information about the clustering results, such as the cluster assignments, the cluster centers, the within-cluster sums of squares, etc.

# Perform k-means clustering kmeans_result <- centers="4," df="" iter.max="10," kmeans="" kmeans_result="" nstart="10)" pre="" print="" result="">

The output shows that we have 4 clusters with different sizes and centers. The within-cluster sum of squares is 72.8, which measures how compact the clusters are. The lower this value is, the better the clustering quality.

### Step 4: Add cluster labels to the data frame

We must add a new column to our data frame containing each observation's cluster labels to plot the clustering results. We can do this by extracting the cluster vector from the kmeans result in the object using the $ operator and assigning it to a new cluster column.

# Add cluster labels to a data frame df$cluster <- 10="" cluster="" data="" df="" first="" frame="" head="" kmeans_result="" of="" pre="" rows="" the="" view="">

The output shows that we have a new column called cluster that indicates which cluster each observation belongs to.

### Step 5: Plot the data points with different colors

We can plot the data points with different colors according to their cluster membership. We will use the ggplot() function from the ggplot2 package to create a scatter plot of x and y and map the cluster variable to the color aesthetic using the aes() function. We will also add some theme elements to make the plot look nicer.

# Plot data points with different colors ggplot(df, aes(x, y, color = factor(cluster))) + geom_point(size = 3) + theme_bw() + theme(legend.position = "bottom")

The output shows a scatter plot of x and y with different colors for each cluster. The clusters could be more well separated, and some of them overlap with each other. This makes it hard to see the boundaries and shapes of the clusters.

### Step 6: R kmean clustering fill color region

To fill color regions in R k-means clustering, we will use the geom_mark_ellipse() function from the ggforce package. This function adds ellipses around groups of points based on their mean and covariance. We will map the cluster variable to the fill aesthetic using the aes() function and set the alpha argument to 0.2 to make the ellipses semi-transparent. We will also add some labels to the ellipses using the geom_text_repel() function from the ggrepel package. This function repels overlapping text labels away from each other and from the data points.

# Fill color regions in R k-means clustering ggplot(df, aes(x, y, color = factor(cluster))) + geom_point(size = 3) + geom_mark_ellipse(aes(fill = factor(cluster)), alpha = 0.2) + geom_text_repel(aes(label = paste("Cluster", cluster)), size = 4) + theme_bw() + theme(legend.position = "none")

The output shows a scatter plot of x and y with filled color regions for each cluster. We can see the boundaries and shapes of the clusters, as well as their relative sizes and positions. The labels also help us identify which cluster is which.

## Conclusion

In this article, you learned how to use R k-means clustering to group data points into clusters based on similarity. You also learned how to fill color regions in the cluster plot to make it more visually appealing and informative. You saw how to use the ggplot2 package and the geom_polygon() function to create the color regions and how to adjust the parameters and options to customize the plot. You also learned how to interpret the cluster plot and the cluster centers to understand the characteristics of each cluster.

This article helped you better understand R k-means clustering and how to fill color regions in the cluster plot. This technique is valuable for exploratory data analysis, visualization, and mining. You can apply it to various data types and scenarios, such as customer segmentation, market research, image compression, etc.

Thank you for reading this article, and I hope you enjoyed it. If you have any questions or feedback, please comment below.Happy coding! 😊

## FAQs

### What is k-means clustering?

K-means clustering is a popular unsupervised machine learning technique that partitions data points into k groups based on similarity. It helps explore the structure and patterns of your data and reduces the dimensionality and noise.

### How do I perform k-means clustering in R?

You can use the kmeans() function from the stats package, which takes as arguments or data frame or a numeric matrix containing the data to be clustered, the number of clusters (k) or a set of initial cluster centers, the maximum number of iterations allowed, and the number of random starts to try. The function returns an object of class "kmeans" that contains information about the clustering results, such as the cluster assignments, the cluster centers, the within-cluster sums of squares, etc.

### How do I plot k-means clustering results in R?

A common way to plot k-means clustering results in R is to use the ggplot() function from the ggplot2 library to create a scatter plot of your variables and map the cluster variable to the color aesthetic using the aes() function. This will color the data points according to their cluster membership. You can also add some theme elements to make the plot look nicer.

### How do I fill color regions in R k-means clustering?

A better way to visualize k-means clustering results in R is to fill color regions in R k-means clustering using simple steps and tricks. You can use the geom_mark_ellipse() function from the ggforce package to add ellipses around groups of points based on their mean and covariance. You can map the cluster variable to the fill aesthetic using the aes() function and set the alpha argument to a low value to make the ellipses semi-transparent. You can also add labels to the ellipses using the geom_text_repel() function from the ggrepel package, which repels overlapping text labels away from each other and from the data points.

### What are some benefits of filling color regions in R k-means clustering?

Filling color regions in R k-means clustering has some benefits over just coloring the data points, such as:

- You can see the boundaries and shapes of the clusters, as well as their relative sizes and positions.
- You can avoid confusion or misinterpretation if the clusters overlap or are not well separated.
- You can make your plot more attractive and informative.

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