kohonen | A basic implementation of a Kohonen map in JavaScript | Artificial Intelligence library

I'm trying to run some and k means analysis. But I can't solve it because there's an error code.

Error in storage.mode(x) <- "double" : 'list' object cannot be coerced to type 'double'

So, it's my first time trying to make my own data visualization, and what I want to do is something like a heat map or highlighted table like the original Kohonen (but using squares rather than hexagons) research about countries, but I don't know how to apply it to the map I've got from training the network.

I've read a few links about making a highlighted table and heat map on Tableau, but what I get is always just a table mainly because I'm not using a measure (i just want every country of one color), so, my question is: is it possible to use Tableau for this situation and if so, how could I do it? Thanks!

Original SOM visualization from Dr. Kohonen

Table from Tableau

I am using the following code which performs the SOM (Self Organizing Map, also called the Kohonen Network) machine learning algorithm to visualize some data. Then, I use a clustering algorithm (I select 8 clusters) on the visualization:

I am following the tutorial over here : https://www.rpubs.com/loveb/som . This tutorial shows how to use the Kohonen Network (also called SOM, a type of machine learning algorithm) on the iris data.

I ran this code from the tutorial:

I am using the R programming language. I am trying to arrange "plot1, plot2, plot3, plot4" on the same page:

In Databricks (SparkR), I run the batch algorithm of the self-organizing map in parallel from the kohonen package as it gives me considerable reductions in computation time as opposed to my local machine. However, after fitting the model I would like to download/export the trained model (a list) to my local machine to continue working with the results (create plots etc.) in a way that is not available in Databricks. I know how to save & download a SparkDataFrame to csv:

I am learning about the "kohonen" package in R for the purpose of making Self Organizing Maps (SOM, also called Kohonen Networks - a type of Machine Learning algorithm). I am following this R language tutorial over here: https://www.rpubs.com/loveb/som

I tried to create my own data (this time with both "factor" and "numeric" variables) and run the SOM algorithm (this time using the "supersom()" function instead):

I am trying to learn about the "kohonen" package in R. In particular, there is a function called "supersom()" (https://www.rdocumentation.org/packages/kohonen/versions/3.0.10/topics/supersom , corresponding to the SOM (Self Organizing Maps) algorithm used in unsupervised machine learning) that I am trying to apply on some data.

Below, (from a previous question: R error: "Error in check.data : Argument Should be Numeric") I learned how to apply the "supersom()" function on some artificially created data with both "factor" and "numeric" variables.

I am learning about the "kohonen" library for the R programming language. I created some artificial data to try some of the functions on. I tried using the "supersom()" function on only continuous (i.e type = as.numeric) data and everything works well. However, when I tried to run the "supersom()" function on both continuous and categorical (type = as.factor), I start to run into some errors ("Argument data should be numeric").

The "supersom()" function has an argument called "dist.fct" (distance function) which allows the user to specify which type of "distance" (e.g. "Euclidean" for continuous, "tanimoto" for categorical) should be used for different columns. I created a data set with 4 continuous variables and 3 categorical variables. Using the following link : https://www.rdocumentation.org/packages/kohonen/versions/2.0.5/topics/supersom , I tried to run the example: