How to use kmsModule

Introduction

soroban’s kmsModule generates k-means cluster.

In this article, we’ll use USArrests dataset of datasets, which can be just use by data("USArrests")

This article is based on 0.0.1 Version of soroban

Declare module

soroban’s module assumes that used in the Shiny application.

and You can use snippet(type shinyapp) to build very basic shiny application.

library(shiny)

ui <- fluidPage(
 
)

server <- function(input, output, session) {
  
}

shinyApp(ui, server)

This application will show nothing.

So let’s add treeModule in ui.

ui <- fluidPage(
  mod_kmsModule_ui(
    id = 'module'
  )
)

also, treeModule in server.

server <- function(input, output, session) {
  mod_kmsModule_server(
    id = 'module', 
    inputData = reactive(datasets::USArrests)
  )
}

So final (which is very basic) code will like this. (Assume data from AER loaded.)

library(shiny)

ui <- fluidPage(
  mod_kmsModule_ui(
    id = 'module'
  )
)

server <- function(input, output, session) {
  mod_kmsModule_server(
    id = 'module', 
    inputData = reactive(datasets::USArrests)
  )
}

shinyApp(ui, server) # Run application

You should notice 2 things.

1. both id in ui and server should be same.
2. inputData in server should be format of reactive

Structure of kmsModule

treeModule is consisted with Control Area and Result Area

and below using flow.

1. Declare module (we did already)
2. select K
3. build Cluster

• If data has different scale (not normalized), check scale.

• If data has label (name), you can select if as Labels-Opt

Usage of kmsModule

Using USArrests, we’ll group city by their similarity.

Set K as 4

For any issue or suggestion, please make issue in soroban’s github.