Here is the complete R script that we have used to demonstrate product network analysis:

library(igraph, quietly = TRUE)

## Creating a simple graph
simple.graph <- graph_from_literal(A-B, B-C, C-D, E-F, A-E, E-C)
plot.igraph(simple.graph)

## Graph properties
E(simple.graph) # Edges
V(simple.graph) # Vertices

## Graph attributes
V(simple.graph)$name <- c('alice', 'bob','charlie','david', 'eli','francis')
simple.graph <- set_vertex_attr(simple.graph ,"age", value = c('11', '11','15','9', '8','11'))
plot.igraph(simple.graph)
V(simple.graph)$color <- ifelse(V(simple.graph)$age == '11', "blue","green")
plot.igraph(simple.graph)

# Structural properties
degree(simple.graph) # degree of nodes
E(simple.graph)$weight <- c(10,20,35,15,25,35)
strength(simple.graph) # strength of nodes
get.adjacency(simple.graph) # adjacency matrix


# Delete edges and nodes
simple.graph <- delete.edges(simple.graph, "alice|bob" )
simple.graph <- delete.vertices(simple.graph, 'francis')
plot(simple.graph)


# use case date
data <- read.csv('data.csv')
head(data)

## Prepare the data
library(arules)
transactions.obj <- read.transactions(file = 'data.csv', format = "single", 
                                      sep = ",",
                                      cols = c("order_id", "product_id"), 
                                      rm.duplicates = FALSE,
                                      quote = "", skip = 0,
                                      encoding = "unknown")
transactions.obj

# Interest Measures
support    <- 0.015

# Frequent item sets
parameters = list(
  support = support,
  minlen  = 2,  # Minimal number of items per item set
  maxlen  = 2, # Maximal number of items per item set
  target  = "frequent itemsets"
)

freq.items <- apriori(transactions.obj, parameter = parameters)

# Let us examine our freq item sites
freq.items.df <- data.frame(item_set = labels(freq.items)
                            , support = freq.items@quality)
freq.items.df$item_set <- as.character(freq.items.df$item_set)
head(freq.items.df)

# Clean up for item pairs
library(tidyr)
freq.items.df <- separate(data = freq.items.df, col = item_set, into = c("item.1", "item.2"), sep = ",")
freq.items.df[] <- lapply(freq.items.df, gsub, pattern='\{', replacement='')
freq.items.df[] <- lapply(freq.items.df, gsub, pattern='\}', replacement='')
head(freq.items.df)

# Prepare data for graph
network.data <- freq.items.df[,c('item.1','item.2','support.count')]
names(network.data) <- c("from","to","weight")
head(network.data)

## Build the graph
library(igraph, quietly = TRUE)
set.seed(29)
my.graph <- graph_from_data_frame(network.data)
plot.igraph(my.graph,
            layout=layout.fruchterman.reingold,
            vertex.label.cex=.5,
            edge.arrow.size=.1)


## Clustering
random.cluster <- walktrap.community(my.graph)
str(random.cluster)
random.cluster
groupings.df <- data.frame(products = random.cluster$names, group = random.cluster$membership)
head(groupings.df)
groupings.df[groupings.df$group == 2,]
groupings.df[groupings.df$group == 1,]

plot(random.cluster,my.graph,
            layout=layout.fruchterman.reingold,
            vertex.label.cex=.5,
            edge.arrow.size=.1)

get.adjacency(my.graph)
strength(my.graph)
degree(my.graph)

## Random walks
for(var in seq(1,5)){
  print (random_walk(my.graph, start = "Banana", steps = 2))
}

random_walk(my.graph, start = "Banana", steps = 3)
random_walk(my.graph, start = "Banana", steps = 4)
random_walk(my.graph, start = "Banana", steps = 5)

RShiny app code:

library(shiny)
library(arules)
library(igraph, quietly = TRUE)


server <- function(input, output) {
  
  
  trans.obj <- reactive({
    data <- input$datafile
    transactions.obj <- read.transactions(file = data$datapath, format = "single", 
                                          sep = ",",
                                          cols = c("order_id", "product_id"), 
                                          rm.duplicates = FALSE,
                                          quote = "", skip = 0,
                                          encoding = "unknown")
    transactions.obj
    
    
  })
  
  trans.df <- reactive({
    
    data <- input$datafile
    if(is.null(data)){return(NULL)}
    trans.df <- read.csv(data$datapath)
    return(trans.df)
  })
  
  network.data <- reactive({
    transactions.obj <- trans.obj()
    support    <- 0.015
    
    # Frequent item sets
    parameters = list(
      support = support,
      minlen  = 2,  # Minimal number of items per item set
      maxlen  = 2, # Maximal number of items per item set
      target  = "frequent itemsets"
    )
    
    freq.items <- apriori(transactions.obj, parameter = parameters)
    
    # Let us examine our freq item sites
    freq.items.df <- data.frame(item_set = labels(freq.items)
                                , support = freq.items@quality)
    freq.items.df$item_set <- as.character(freq.items.df$item_set)
    
    # Clean up for item pairs
    library(tidyr)
    freq.items.df <- separate(data = freq.items.df, col = item_set, into = c("item.1", "item.2"), sep = ",")
    freq.items.df[] <- lapply(freq.items.df, gsub, pattern='\{', replacement='')
    freq.items.df[] <- lapply(freq.items.df, gsub, pattern='\}', replacement='')
    
    # Prepare data for graph
    network.data <- freq.items.df[,c('item.1','item.2','support.count')]
    names(network.data) <- c("from","to","weight")
    return(network.data)
    
  })
  
  output$transactions <- renderDataTable({
    trans.df()
  })
  output$ppairs <- renderDataTable({
    
    network.data()
    
  })
  
  output$community <- renderPlot({
    network.data <- network.data()
    my.graph <- graph_from_data_frame(network.data)
    random.cluster <- walktrap.community(my.graph)
    plot(random.cluster,my.graph,
         layout=layout.fruchterman.reingold,
         vertex.label.cex=.5,
         edge.arrow.size=.1,height = 1200, width = 1200)
  })

  
}

ui <- fluidPage(
  navbarPage("Product Pairs",
             tabPanel("Transactions"
                      , fileInput("datafile", "select transactions csv file",
                                  accept = c(
                                    "text/csv",
                                    "text/comma-separated-values,text/plain",
                                    ".csv"
                                    )
                      )
                      , dataTableOutput("transactions")
             ),
             tabPanel("Product Pairs"
                      ,dataTableOutput("ppairs")),
             tabPanel("Community"
                      ,plotOutput("community"))
  )
)

shinyApp(ui = ui, server = server)