Notebook 08: Visualizations

Feedback should be send to goran.milovanovic@datakolektiv.com. These notebooks accompany the MilanoR talk 2019/06/25.

1. Setup

Note. The following chunks load packages, define the project directory tree and some constants.

### --- libraries
library(plotly)
library(data.table)
library(tidyverse)
library(visNetwork)
library(stringr)
library(tm)
library(BBmisc)
library(text2vec)
library(parallelDist)

### --- directories
dataDir <- 'data/'
analyticsDir <- 'analytics/'
funDir <- 'functions/'

2. Load the topical distributions: document_topic_matrix and word_topic_matrix

word_topic_matrix <- read.csv(
  paste0(analyticsDir, "analysis_word_topic_matrix.csv"), 
  header = T, 
  check.names = F, 
  row.names = 1,
  stringsAsFactors = F)

2. Concepts Distance Matrix

concepts <- rownames(word_topic_matrix)
# - Hellinger distances
conceptDist <- parDist(as.matrix(word_topic_matrix),
                       method = "hellinger",
                       diag = T,
                       upper = T,
                       threads = 7)
rm(word_topic_matrix); gc()
            used   (Mb) gc trigger   (Mb)  max used   (Mb)
Ncells   2130230  113.8    3968292  212.0   3019832  161.3
Vcells 153158176 1168.6  213581747 1629.5 171223122 1306.4
conceptDistMat <- as.matrix(conceptDist)
rm(conceptDist); gc()
            used  (Mb) gc trigger   (Mb)  max used   (Mb)
Ncells   2139946 114.3    3968292  212.0   3019832  161.3
Vcells 103343176 788.5  408129829 3113.8 502043035 3830.3
rownames(conceptDistMat) <- concepts
colnames(conceptDistMat) <- concepts
saveRDS(conceptDistMat, 
        paste0(analyticsDir, "conceptDistMat.Rds")
        )
rm(concepts)

3 Visualize!

3.1 Visualize local concept neighbourhoods: Twitter

# - locate "twitter"
term <- "twitter"
# - size of the neighbourhood
n_size <- 20
wTerm <- which(grepl(term, rownames(conceptDistMat)))
# - fetch neighbourhood
ng1 <- vector(mode = "list", length = length(wTerm))
for (i in 1:length(wTerm)) {
  ng1[[i]] <- names(
    sort(conceptDistMat[wTerm[i], ], decreasing = F)[1:n_size+1]
  )
}
names(ng1) <- rownames(conceptDistMat)[wTerm]
for (i in 1:length(ng1)) {
  ng1[[i]] <- setdiff(ng1[[i]], names(ng1)[i])
}
ng2 <- vector(mode = "list", length = sum(sapply(ng1, length)))
c <- 0
for (i in 1:length(ng1)) {
  for (j in 1:length(ng1[[i]])) {
    c <- c + 1
    ng2[[c]] <- names(
    sort(
      conceptDistMat[which(rownames(conceptDistMat) == ng1[[i]][j]), ], 
      decreasing = F)[1:n_size+1]
    )
  }
}
names(ng2) <- unname(unlist(ng1))
for (i in 1:length(ng2)) {
  ng2[[i]] <- setdiff(ng2[[i]], names(ng2)[i])
}
graphData <- rbind(stack(ng1), stack(ng2))
graphData$ind <- as.character(graphData$ind)
graphData <- graphData[, c(2, 1)]
colnames(graphData) <- c('outgoing', 'incoming')
graphData$incoming[grepl("^wd_", graphData$incoming)] <- 
  toupper(graphData$incoming[grepl("^wd_", graphData$incoming)])
graphData$outgoing[grepl("^wd_", graphData$outgoing)] <- 
  toupper(graphData$outgoing[grepl("^wd_", graphData$outgoing)])

# - visualize w. {visNetwork}
nodes <- unique(c(graphData$outgoing, graphData$incoming))
nodes <- data.frame(id = 1:length(nodes),
                    label = nodes,
                    stringsAsFactors = F)
edges <- graphData
colnames(edges) <- c("from", "to")
edges$from <- sapply(edges$from, function(x) {
  nodes$id[which(nodes$label == x)]
})
edges$to <- sapply(edges$to, function(x) {
  nodes$id[which(nodes$label == x)]
})

# - visualize
visNetwork(nodes, edges, width = "100%") %>%
  visIgraphLayout() %>%
  visNodes(
    shape = "dot",
    color = list(
      background = "#0085AF",
      border = "#013848",
      highlight = "#FF8000"
    ),
    shadow = list(enabled = TRUE, size = 10)
  ) %>%
  visEdges(
    shadow = FALSE,
    color = list(color = "#0085AF", highlight = "#C62F4B")
  ) %>%
  visOptions(highlightNearest = TRUE, selectedBy = "label") %>%
  visLayout(randomSeed = 11)

3.2 Visualize local concept neighbourhoods: Google

# - locate "google"
term <- "google"
# - size of the neighbourhood
n_size <- 5
wTerm <- which(grepl(term, rownames(conceptDistMat)))
# - fetch neighbourhood
ng1 <- vector(mode = "list", length = length(wTerm))
for (i in 1:length(wTerm)) {
  ng1[[i]] <- names(
    sort(conceptDistMat[wTerm[i], ], decreasing = F)[1:n_size+1]
  )
}
names(ng1) <- rownames(conceptDistMat)[wTerm]
for (i in 1:length(ng1)) {
  ng1[[i]] <- setdiff(ng1[[i]], names(ng1)[i])
}
ng2 <- vector(mode = "list", length = sum(sapply(ng1, length)))
c <- 0
for (i in 1:length(ng1)) {
  for (j in 1:length(ng1[[i]])) {
    c <- c + 1
    ng2[[c]] <- names(
    sort(
      conceptDistMat[which(rownames(conceptDistMat) == ng1[[i]][j]), ], 
      decreasing = F)[1:n_size+1]
    )
  }
}
names(ng2) <- unname(unlist(ng1))
for (i in 1:length(ng2)) {
  ng2[[i]] <- setdiff(ng2[[i]], names(ng2)[i])
}
graphData <- rbind(stack(ng1), stack(ng2))
graphData$ind <- as.character(graphData$ind)
graphData <- graphData[, c(2, 1)]
colnames(graphData) <- c('outgoing', 'incoming')
graphData$incoming[grepl("^wd_", graphData$incoming)] <- 
  toupper(graphData$incoming[grepl("^wd_", graphData$incoming)])
graphData$outgoing[grepl("^wd_", graphData$outgoing)] <- 
  toupper(graphData$outgoing[grepl("^wd_", graphData$outgoing)])

# - visualize w. {visNetwork}
nodes <- unique(c(graphData$outgoing, graphData$incoming))
nodes <- data.frame(id = 1:length(nodes),
                    label = nodes,
                    stringsAsFactors = F)
edges <- graphData
colnames(edges) <- c("from", "to")
edges$from <- sapply(edges$from, function(x) {
  nodes$id[which(nodes$label == x)]
})
edges$to <- sapply(edges$to, function(x) {
  nodes$id[which(nodes$label == x)]
})

# - visualize
visNetwork(nodes, edges, width = "100%") %>%
  visIgraphLayout() %>%
  visNodes(
    shape = "dot",
    color = list(
      background = "#0085AF",
      border = "#013848",
      highlight = "#FF8000"
    ),
    shadow = list(enabled = TRUE, size = 10)
  ) %>%
  visEdges(
    shadow = FALSE,
    color = list(color = "#0085AF", highlight = "#C62F4B")
  ) %>%
  visOptions(highlightNearest = TRUE, selectedBy = "label") %>%
  visLayout(randomSeed = 11)

3.3 Visualize local concept neighbourhoods: Apple

# - locate "Apple"
term <- "appl"
# - size of the neighbourhood
n_size <- 5
wTerm <- which(grepl(term, rownames(conceptDistMat)))
# - fetch neighbourhood
ng1 <- vector(mode = "list", length = length(wTerm))
for (i in 1:length(wTerm)) {
  ng1[[i]] <- names(
    sort(conceptDistMat[wTerm[i], ], decreasing = F)[1:n_size+1]
  )
}
names(ng1) <- rownames(conceptDistMat)[wTerm]
for (i in 1:length(ng1)) {
  ng1[[i]] <- setdiff(ng1[[i]], names(ng1)[i])
}
ng2 <- vector(mode = "list", length = sum(sapply(ng1, length)))
c <- 0
for (i in 1:length(ng1)) {
  for (j in 1:length(ng1[[i]])) {
    c <- c + 1
    ng2[[c]] <- names(
    sort(
      conceptDistMat[which(rownames(conceptDistMat) == ng1[[i]][j]), ], 
      decreasing = F)[1:n_size+1]
    )
  }
}
names(ng2) <- unname(unlist(ng1))
for (i in 1:length(ng2)) {
  ng2[[i]] <- setdiff(ng2[[i]], names(ng2)[i])
}
graphData <- rbind(stack(ng1), stack(ng2))
graphData$ind <- as.character(graphData$ind)
graphData <- graphData[, c(2, 1)]
colnames(graphData) <- c('outgoing', 'incoming')
graphData$incoming[grepl("^wd_", graphData$incoming)] <- 
  toupper(graphData$incoming[grepl("^wd_", graphData$incoming)])
graphData$outgoing[grepl("^wd_", graphData$outgoing)] <- 
  toupper(graphData$outgoing[grepl("^wd_", graphData$outgoing)])

# - visualize w. {visNetwork}
nodes <- unique(c(graphData$outgoing, graphData$incoming))
nodes <- data.frame(id = 1:length(nodes),
                    label = nodes,
                    stringsAsFactors = F)
edges <- graphData
colnames(edges) <- c("from", "to")
edges$from <- sapply(edges$from, function(x) {
  nodes$id[which(nodes$label == x)]
})
edges$to <- sapply(edges$to, function(x) {
  nodes$id[which(nodes$label == x)]
})

# - visualize
visNetwork(nodes, edges, width = "100%") %>%
  visIgraphLayout() %>%
  visNodes(
    shape = "dot",
    color = list(
      background = "#0085AF",
      border = "#013848",
      highlight = "#FF8000"
    ),
    shadow = list(enabled = TRUE, size = 10)
  ) %>%
  visEdges(
    shadow = FALSE,
    color = list(color = "#0085AF", highlight = "#C62F4B")
  ) %>%
  visOptions(highlightNearest = TRUE, selectedBy = "label") %>%
  visLayout(randomSeed = 11)

3.4 Visualize local concept neighbourhoods: Amazon

# - locate "Amazon"
term <- "amazon"
# - size of the neighbourhood
n_size <- 5
wTerm <- which(grepl(term, rownames(conceptDistMat)))
# - fetch neighbourhood
ng1 <- vector(mode = "list", length = length(wTerm))
for (i in 1:length(wTerm)) {
  ng1[[i]] <- names(
    sort(conceptDistMat[wTerm[i], ], decreasing = F)[1:n_size+1]
  )
}
names(ng1) <- rownames(conceptDistMat)[wTerm]
for (i in 1:length(ng1)) {
  ng1[[i]] <- setdiff(ng1[[i]], names(ng1)[i])
}
ng2 <- vector(mode = "list", length = sum(sapply(ng1, length)))
c <- 0
for (i in 1:length(ng1)) {
  for (j in 1:length(ng1[[i]])) {
    c <- c + 1
    ng2[[c]] <- names(
    sort(
      conceptDistMat[which(rownames(conceptDistMat) == ng1[[i]][j]), ], 
      decreasing = F)[1:n_size+1]
    )
  }
}
names(ng2) <- unname(unlist(ng1))
for (i in 1:length(ng2)) {
  ng2[[i]] <- setdiff(ng2[[i]], names(ng2)[i])
}
graphData <- rbind(stack(ng1), stack(ng2))
graphData$ind <- as.character(graphData$ind)
graphData <- graphData[, c(2, 1)]
colnames(graphData) <- c('outgoing', 'incoming')
graphData$incoming[grepl("^wd_", graphData$incoming)] <- 
  toupper(graphData$incoming[grepl("^wd_", graphData$incoming)])
graphData$outgoing[grepl("^wd_", graphData$outgoing)] <- 
  toupper(graphData$outgoing[grepl("^wd_", graphData$outgoing)])

# - visualize w. {visNetwork}
nodes <- unique(c(graphData$outgoing, graphData$incoming))
nodes <- data.frame(id = 1:length(nodes),
                    label = nodes,
                    stringsAsFactors = F)
edges <- graphData
colnames(edges) <- c("from", "to")
edges$from <- sapply(edges$from, function(x) {
  nodes$id[which(nodes$label == x)]
})
edges$to <- sapply(edges$to, function(x) {
  nodes$id[which(nodes$label == x)]
})

# - visualize
visNetwork(nodes, edges, width = "100%") %>%
  visIgraphLayout() %>%
  visNodes(
    shape = "dot",
    color = list(
      background = "#0085AF",
      border = "#013848",
      highlight = "#FF8000"
    ),
    shadow = list(enabled = TRUE, size = 10)
  ) %>%
  visEdges(
    shadow = FALSE,
    color = list(color = "#0085AF", highlight = "#C62F4B")
  ) %>%
  visOptions(highlightNearest = TRUE, selectedBy = "label") %>%
  visLayout(randomSeed = 11)

3.5 Visualize local concept neighbourhoods: Microsoft

# - locate "Microsoft"
term <- "microsoft"
# - size of the neighbourhood
n_size <- 10
wTerm <- which(grepl(term, rownames(conceptDistMat)))
# - fetch neighbourhood
ng1 <- vector(mode = "list", length = length(wTerm))
for (i in 1:length(wTerm)) {
  ng1[[i]] <- names(
    sort(conceptDistMat[wTerm[i], ], decreasing = F)[1:n_size+1]
  )
}
names(ng1) <- rownames(conceptDistMat)[wTerm]
for (i in 1:length(ng1)) {
  ng1[[i]] <- setdiff(ng1[[i]], names(ng1)[i])
}
ng2 <- vector(mode = "list", length = sum(sapply(ng1, length)))
c <- 0
for (i in 1:length(ng1)) {
  for (j in 1:length(ng1[[i]])) {
    c <- c + 1
    ng2[[c]] <- names(
    sort(
      conceptDistMat[which(rownames(conceptDistMat) == ng1[[i]][j]), ], 
      decreasing = F)[1:n_size+1]
    )
  }
}
names(ng2) <- unname(unlist(ng1))
for (i in 1:length(ng2)) {
  ng2[[i]] <- setdiff(ng2[[i]], names(ng2)[i])
}
graphData <- rbind(stack(ng1), stack(ng2))
graphData$ind <- as.character(graphData$ind)
graphData <- graphData[, c(2, 1)]
colnames(graphData) <- c('outgoing', 'incoming')
graphData$incoming[grepl("^wd_", graphData$incoming)] <- 
  toupper(graphData$incoming[grepl("^wd_", graphData$incoming)])
graphData$outgoing[grepl("^wd_", graphData$outgoing)] <- 
  toupper(graphData$outgoing[grepl("^wd_", graphData$outgoing)])

# - visualize w. {visNetwork}
nodes <- unique(c(graphData$outgoing, graphData$incoming))
nodes <- data.frame(id = 1:length(nodes),
                    label = nodes,
                    stringsAsFactors = F)
edges <- graphData
colnames(edges) <- c("from", "to")
edges$from <- sapply(edges$from, function(x) {
  nodes$id[which(nodes$label == x)]
})
edges$to <- sapply(edges$to, function(x) {
  nodes$id[which(nodes$label == x)]
})

# - visualize
visNetwork(nodes, edges, width = "100%") %>%
  visIgraphLayout() %>%
  visNodes(
    shape = "dot",
    color = list(
      background = "#0085AF",
      border = "#013848",
      highlight = "#FF8000"
    ),
    shadow = list(enabled = TRUE, size = 10)
  ) %>%
  visEdges(
    shadow = FALSE,
    color = list(color = "#0085AF", highlight = "#C62F4B")
  ) %>%
  visOptions(highlightNearest = TRUE, selectedBy = "label") %>%
  visLayout(randomSeed = 11)

3.6 Visualize local concept neighbourhoods: Facebook

# - locate "Facebook"
term <- "facebook"
# - size of the neighbourhood
n_size <- 3
wTerm <- which(grepl(term, rownames(conceptDistMat)))
# - fetch neighbourhood
ng1 <- vector(mode = "list", length = length(wTerm))
for (i in 1:length(wTerm)) {
  ng1[[i]] <- names(
    sort(conceptDistMat[wTerm[i], ], decreasing = F)[1:n_size+1]
  )
}
names(ng1) <- rownames(conceptDistMat)[wTerm]
for (i in 1:length(ng1)) {
  ng1[[i]] <- setdiff(ng1[[i]], names(ng1)[i])
}
ng2 <- vector(mode = "list", length = sum(sapply(ng1, length)))
c <- 0
for (i in 1:length(ng1)) {
  for (j in 1:length(ng1[[i]])) {
    c <- c + 1
    ng2[[c]] <- names(
    sort(
      conceptDistMat[which(rownames(conceptDistMat) == ng1[[i]][j]), ], 
      decreasing = F)[1:n_size+1]
    )
  }
}
names(ng2) <- unname(unlist(ng1))
for (i in 1:length(ng2)) {
  ng2[[i]] <- setdiff(ng2[[i]], names(ng2)[i])
}
graphData <- rbind(stack(ng1), stack(ng2))
graphData$ind <- as.character(graphData$ind)
graphData <- graphData[, c(2, 1)]
colnames(graphData) <- c('outgoing', 'incoming')
graphData$incoming[grepl("^wd_", graphData$incoming)] <- 
  toupper(graphData$incoming[grepl("^wd_", graphData$incoming)])
graphData$outgoing[grepl("^wd_", graphData$outgoing)] <- 
  toupper(graphData$outgoing[grepl("^wd_", graphData$outgoing)])

# - visualize w. {visNetwork}
nodes <- unique(c(graphData$outgoing, graphData$incoming))
nodes <- data.frame(id = 1:length(nodes),
                    label = nodes,
                    stringsAsFactors = F)
edges <- graphData
colnames(edges) <- c("from", "to")
edges$from <- sapply(edges$from, function(x) {
  nodes$id[which(nodes$label == x)]
})
edges$to <- sapply(edges$to, function(x) {
  nodes$id[which(nodes$label == x)]
})

# - visualize
visNetwork(nodes, edges, width = "100%") %>%
  visIgraphLayout() %>%
  visNodes(
    shape = "dot",
    color = list(
      background = "#0085AF",
      border = "#013848",
      highlight = "#FF8000"
    ),
    shadow = list(enabled = TRUE, size = 10)
  ) %>%
  visEdges(
    shadow = FALSE,
    color = list(color = "#0085AF", highlight = "#C62F4B")
  ) %>%
  visOptions(highlightNearest = TRUE, selectedBy = "label") %>%
  visLayout(randomSeed = 11)

Goran S. Milovanović & Mike Page

DataKolektiv, 2019.

contact:

License: GPLv3 This Notebook is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This Notebook is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this Notebook. If not, see http://www.gnu.org/licenses/.

---
title: Semantic Web Technologies and Wikidata from R
author:
- name: Goran S. Milovanović
  affiliation: Wikimedia Deutschland, Data Scientist, DataKolektiv, Owner
- name: Mike Page
  affiliation: DataKolektiv, Junior Data Scientist
date: "`r format(Sys.time(), '%d %B %Y')`"
abstract: 
output:
  html_notebook:
    code_folding: show
    theme: spacelab
    toc: yes
    toc_float: yes
    toc_depth: 5
  html_document:
    toc: yes
    toc_depth: 5
---

![](img/DK_Logo_100.png)

***
### Notebook 08: Visualizations
**Feedback** should be send to `goran.milovanovic@datakolektiv.com`. 
These notebooks accompany the MilanoR talk 2019/06/25.

***

### 1. Setup

**Note.** The following chunks load packages, define the project directory tree and some constants.

```{r echo = T, eval = T, message = F}
### --- libraries
library(plotly)
library(data.table)
library(tidyverse)
library(visNetwork)
library(stringr)
library(tm)
library(BBmisc)
library(text2vec)
library(parallelDist)

### --- directories
dataDir <- 'data/'
analyticsDir <- 'analytics/'
funDir <- 'functions/'
```

### 2. Load the topical distributions: `document_topic_matrix` and `word_topic_matrix`

```{r echo = T, eval = T}
word_topic_matrix <- read.csv(
  paste0(analyticsDir, "analysis_word_topic_matrix.csv"), 
  header = T, 
  check.names = F, 
  row.names = 1,
  stringsAsFactors = F)
```

### 2. Concepts Distance Matrix

```{r echo = T, eval = T}
concepts <- rownames(word_topic_matrix)
# - Hellinger distances
conceptDist <- parDist(as.matrix(word_topic_matrix),
                       method = "hellinger",
                       diag = T,
                       upper = T,
                       threads = 7)
rm(word_topic_matrix); gc()
conceptDistMat <- as.matrix(conceptDist)
rm(conceptDist); gc()
rownames(conceptDistMat) <- concepts
colnames(conceptDistMat) <- concepts
saveRDS(conceptDistMat, 
        paste0(analyticsDir, "conceptDistMat.Rds")
        )
rm(concepts)
```

### 3 Visualize!

#### 3.1 Visualize local concept neighbourhoods: `Twitter`

```{r echo = T, eval = T}
# - locate "twitter"
term <- "twitter"
# - size of the neighbourhood
n_size <- 20
wTerm <- which(grepl(term, rownames(conceptDistMat)))
# - fetch neighbourhood
ng1 <- vector(mode = "list", length = length(wTerm))
for (i in 1:length(wTerm)) {
  ng1[[i]] <- names(
    sort(conceptDistMat[wTerm[i], ], decreasing = F)[1:n_size+1]
  )
}
names(ng1) <- rownames(conceptDistMat)[wTerm]
for (i in 1:length(ng1)) {
  ng1[[i]] <- setdiff(ng1[[i]], names(ng1)[i])
}
ng2 <- vector(mode = "list", length = sum(sapply(ng1, length)))
c <- 0
for (i in 1:length(ng1)) {
  for (j in 1:length(ng1[[i]])) {
    c <- c + 1
    ng2[[c]] <- names(
    sort(
      conceptDistMat[which(rownames(conceptDistMat) == ng1[[i]][j]), ], 
      decreasing = F)[1:n_size+1]
    )
  }
}
names(ng2) <- unname(unlist(ng1))
for (i in 1:length(ng2)) {
  ng2[[i]] <- setdiff(ng2[[i]], names(ng2)[i])
}
graphData <- rbind(stack(ng1), stack(ng2))
graphData$ind <- as.character(graphData$ind)
graphData <- graphData[, c(2, 1)]
colnames(graphData) <- c('outgoing', 'incoming')
graphData$incoming[grepl("^wd_", graphData$incoming)] <- 
  toupper(graphData$incoming[grepl("^wd_", graphData$incoming)])
graphData$outgoing[grepl("^wd_", graphData$outgoing)] <- 
  toupper(graphData$outgoing[grepl("^wd_", graphData$outgoing)])

# - visualize w. {visNetwork}
nodes <- unique(c(graphData$outgoing, graphData$incoming))
nodes <- data.frame(id = 1:length(nodes),
                    label = nodes,
                    stringsAsFactors = F)
edges <- graphData
colnames(edges) <- c("from", "to")
edges$from <- sapply(edges$from, function(x) {
  nodes$id[which(nodes$label == x)]
})
edges$to <- sapply(edges$to, function(x) {
  nodes$id[which(nodes$label == x)]
})

# - visualize
visNetwork(nodes, edges, width = "100%") %>%
  visIgraphLayout() %>%
  visNodes(
    shape = "dot",
    color = list(
      background = "#0085AF",
      border = "#013848",
      highlight = "#FF8000"
    ),
    shadow = list(enabled = TRUE, size = 10)
  ) %>%
  visEdges(
    shadow = FALSE,
    color = list(color = "#0085AF", highlight = "#C62F4B")
  ) %>%
  visOptions(highlightNearest = TRUE, selectedBy = "label") %>%
  visLayout(randomSeed = 11)
```

#### 3.2 Visualize local concept neighbourhoods: `Google`

```{r echo = T, eval = T}
# - locate "google"
term <- "google"
# - size of the neighbourhood
n_size <- 5
wTerm <- which(grepl(term, rownames(conceptDistMat)))
# - fetch neighbourhood
ng1 <- vector(mode = "list", length = length(wTerm))
for (i in 1:length(wTerm)) {
  ng1[[i]] <- names(
    sort(conceptDistMat[wTerm[i], ], decreasing = F)[1:n_size+1]
  )
}
names(ng1) <- rownames(conceptDistMat)[wTerm]
for (i in 1:length(ng1)) {
  ng1[[i]] <- setdiff(ng1[[i]], names(ng1)[i])
}
ng2 <- vector(mode = "list", length = sum(sapply(ng1, length)))
c <- 0
for (i in 1:length(ng1)) {
  for (j in 1:length(ng1[[i]])) {
    c <- c + 1
    ng2[[c]] <- names(
    sort(
      conceptDistMat[which(rownames(conceptDistMat) == ng1[[i]][j]), ], 
      decreasing = F)[1:n_size+1]
    )
  }
}
names(ng2) <- unname(unlist(ng1))
for (i in 1:length(ng2)) {
  ng2[[i]] <- setdiff(ng2[[i]], names(ng2)[i])
}
graphData <- rbind(stack(ng1), stack(ng2))
graphData$ind <- as.character(graphData$ind)
graphData <- graphData[, c(2, 1)]
colnames(graphData) <- c('outgoing', 'incoming')
graphData$incoming[grepl("^wd_", graphData$incoming)] <- 
  toupper(graphData$incoming[grepl("^wd_", graphData$incoming)])
graphData$outgoing[grepl("^wd_", graphData$outgoing)] <- 
  toupper(graphData$outgoing[grepl("^wd_", graphData$outgoing)])

# - visualize w. {visNetwork}
nodes <- unique(c(graphData$outgoing, graphData$incoming))
nodes <- data.frame(id = 1:length(nodes),
                    label = nodes,
                    stringsAsFactors = F)
edges <- graphData
colnames(edges) <- c("from", "to")
edges$from <- sapply(edges$from, function(x) {
  nodes$id[which(nodes$label == x)]
})
edges$to <- sapply(edges$to, function(x) {
  nodes$id[which(nodes$label == x)]
})

# - visualize
visNetwork(nodes, edges, width = "100%") %>%
  visIgraphLayout() %>%
  visNodes(
    shape = "dot",
    color = list(
      background = "#0085AF",
      border = "#013848",
      highlight = "#FF8000"
    ),
    shadow = list(enabled = TRUE, size = 10)
  ) %>%
  visEdges(
    shadow = FALSE,
    color = list(color = "#0085AF", highlight = "#C62F4B")
  ) %>%
  visOptions(highlightNearest = TRUE, selectedBy = "label") %>%
  visLayout(randomSeed = 11)
```


#### 3.3 Visualize local concept neighbourhoods: `Apple`

```{r echo = T, eval = T}
# - locate "Apple"
term <- "appl"
# - size of the neighbourhood
n_size <- 5
wTerm <- which(grepl(term, rownames(conceptDistMat)))
# - fetch neighbourhood
ng1 <- vector(mode = "list", length = length(wTerm))
for (i in 1:length(wTerm)) {
  ng1[[i]] <- names(
    sort(conceptDistMat[wTerm[i], ], decreasing = F)[1:n_size+1]
  )
}
names(ng1) <- rownames(conceptDistMat)[wTerm]
for (i in 1:length(ng1)) {
  ng1[[i]] <- setdiff(ng1[[i]], names(ng1)[i])
}
ng2 <- vector(mode = "list", length = sum(sapply(ng1, length)))
c <- 0
for (i in 1:length(ng1)) {
  for (j in 1:length(ng1[[i]])) {
    c <- c + 1
    ng2[[c]] <- names(
    sort(
      conceptDistMat[which(rownames(conceptDistMat) == ng1[[i]][j]), ], 
      decreasing = F)[1:n_size+1]
    )
  }
}
names(ng2) <- unname(unlist(ng1))
for (i in 1:length(ng2)) {
  ng2[[i]] <- setdiff(ng2[[i]], names(ng2)[i])
}
graphData <- rbind(stack(ng1), stack(ng2))
graphData$ind <- as.character(graphData$ind)
graphData <- graphData[, c(2, 1)]
colnames(graphData) <- c('outgoing', 'incoming')
graphData$incoming[grepl("^wd_", graphData$incoming)] <- 
  toupper(graphData$incoming[grepl("^wd_", graphData$incoming)])
graphData$outgoing[grepl("^wd_", graphData$outgoing)] <- 
  toupper(graphData$outgoing[grepl("^wd_", graphData$outgoing)])

# - visualize w. {visNetwork}
nodes <- unique(c(graphData$outgoing, graphData$incoming))
nodes <- data.frame(id = 1:length(nodes),
                    label = nodes,
                    stringsAsFactors = F)
edges <- graphData
colnames(edges) <- c("from", "to")
edges$from <- sapply(edges$from, function(x) {
  nodes$id[which(nodes$label == x)]
})
edges$to <- sapply(edges$to, function(x) {
  nodes$id[which(nodes$label == x)]
})

# - visualize
visNetwork(nodes, edges, width = "100%") %>%
  visIgraphLayout() %>%
  visNodes(
    shape = "dot",
    color = list(
      background = "#0085AF",
      border = "#013848",
      highlight = "#FF8000"
    ),
    shadow = list(enabled = TRUE, size = 10)
  ) %>%
  visEdges(
    shadow = FALSE,
    color = list(color = "#0085AF", highlight = "#C62F4B")
  ) %>%
  visOptions(highlightNearest = TRUE, selectedBy = "label") %>%
  visLayout(randomSeed = 11)
```

#### 3.4 Visualize local concept neighbourhoods: `Amazon`

```{r echo = T, eval = T}
# - locate "Amazon"
term <- "amazon"
# - size of the neighbourhood
n_size <- 5
wTerm <- which(grepl(term, rownames(conceptDistMat)))
# - fetch neighbourhood
ng1 <- vector(mode = "list", length = length(wTerm))
for (i in 1:length(wTerm)) {
  ng1[[i]] <- names(
    sort(conceptDistMat[wTerm[i], ], decreasing = F)[1:n_size+1]
  )
}
names(ng1) <- rownames(conceptDistMat)[wTerm]
for (i in 1:length(ng1)) {
  ng1[[i]] <- setdiff(ng1[[i]], names(ng1)[i])
}
ng2 <- vector(mode = "list", length = sum(sapply(ng1, length)))
c <- 0
for (i in 1:length(ng1)) {
  for (j in 1:length(ng1[[i]])) {
    c <- c + 1
    ng2[[c]] <- names(
    sort(
      conceptDistMat[which(rownames(conceptDistMat) == ng1[[i]][j]), ], 
      decreasing = F)[1:n_size+1]
    )
  }
}
names(ng2) <- unname(unlist(ng1))
for (i in 1:length(ng2)) {
  ng2[[i]] <- setdiff(ng2[[i]], names(ng2)[i])
}
graphData <- rbind(stack(ng1), stack(ng2))
graphData$ind <- as.character(graphData$ind)
graphData <- graphData[, c(2, 1)]
colnames(graphData) <- c('outgoing', 'incoming')
graphData$incoming[grepl("^wd_", graphData$incoming)] <- 
  toupper(graphData$incoming[grepl("^wd_", graphData$incoming)])
graphData$outgoing[grepl("^wd_", graphData$outgoing)] <- 
  toupper(graphData$outgoing[grepl("^wd_", graphData$outgoing)])

# - visualize w. {visNetwork}
nodes <- unique(c(graphData$outgoing, graphData$incoming))
nodes <- data.frame(id = 1:length(nodes),
                    label = nodes,
                    stringsAsFactors = F)
edges <- graphData
colnames(edges) <- c("from", "to")
edges$from <- sapply(edges$from, function(x) {
  nodes$id[which(nodes$label == x)]
})
edges$to <- sapply(edges$to, function(x) {
  nodes$id[which(nodes$label == x)]
})

# - visualize
visNetwork(nodes, edges, width = "100%") %>%
  visIgraphLayout() %>%
  visNodes(
    shape = "dot",
    color = list(
      background = "#0085AF",
      border = "#013848",
      highlight = "#FF8000"
    ),
    shadow = list(enabled = TRUE, size = 10)
  ) %>%
  visEdges(
    shadow = FALSE,
    color = list(color = "#0085AF", highlight = "#C62F4B")
  ) %>%
  visOptions(highlightNearest = TRUE, selectedBy = "label") %>%
  visLayout(randomSeed = 11)
```

#### 3.5 Visualize local concept neighbourhoods: `Microsoft`

```{r echo = T, eval = T}
# - locate "Microsoft"
term <- "microsoft"
# - size of the neighbourhood
n_size <- 10
wTerm <- which(grepl(term, rownames(conceptDistMat)))
# - fetch neighbourhood
ng1 <- vector(mode = "list", length = length(wTerm))
for (i in 1:length(wTerm)) {
  ng1[[i]] <- names(
    sort(conceptDistMat[wTerm[i], ], decreasing = F)[1:n_size+1]
  )
}
names(ng1) <- rownames(conceptDistMat)[wTerm]
for (i in 1:length(ng1)) {
  ng1[[i]] <- setdiff(ng1[[i]], names(ng1)[i])
}
ng2 <- vector(mode = "list", length = sum(sapply(ng1, length)))
c <- 0
for (i in 1:length(ng1)) {
  for (j in 1:length(ng1[[i]])) {
    c <- c + 1
    ng2[[c]] <- names(
    sort(
      conceptDistMat[which(rownames(conceptDistMat) == ng1[[i]][j]), ], 
      decreasing = F)[1:n_size+1]
    )
  }
}
names(ng2) <- unname(unlist(ng1))
for (i in 1:length(ng2)) {
  ng2[[i]] <- setdiff(ng2[[i]], names(ng2)[i])
}
graphData <- rbind(stack(ng1), stack(ng2))
graphData$ind <- as.character(graphData$ind)
graphData <- graphData[, c(2, 1)]
colnames(graphData) <- c('outgoing', 'incoming')
graphData$incoming[grepl("^wd_", graphData$incoming)] <- 
  toupper(graphData$incoming[grepl("^wd_", graphData$incoming)])
graphData$outgoing[grepl("^wd_", graphData$outgoing)] <- 
  toupper(graphData$outgoing[grepl("^wd_", graphData$outgoing)])

# - visualize w. {visNetwork}
nodes <- unique(c(graphData$outgoing, graphData$incoming))
nodes <- data.frame(id = 1:length(nodes),
                    label = nodes,
                    stringsAsFactors = F)
edges <- graphData
colnames(edges) <- c("from", "to")
edges$from <- sapply(edges$from, function(x) {
  nodes$id[which(nodes$label == x)]
})
edges$to <- sapply(edges$to, function(x) {
  nodes$id[which(nodes$label == x)]
})

# - visualize
visNetwork(nodes, edges, width = "100%") %>%
  visIgraphLayout() %>%
  visNodes(
    shape = "dot",
    color = list(
      background = "#0085AF",
      border = "#013848",
      highlight = "#FF8000"
    ),
    shadow = list(enabled = TRUE, size = 10)
  ) %>%
  visEdges(
    shadow = FALSE,
    color = list(color = "#0085AF", highlight = "#C62F4B")
  ) %>%
  visOptions(highlightNearest = TRUE, selectedBy = "label") %>%
  visLayout(randomSeed = 11)
```

#### 3.6 Visualize local concept neighbourhoods: `Facebook`

```{r echo = T, eval = T}
# - locate "Facebook"
term <- "facebook"
# - size of the neighbourhood
n_size <- 3
wTerm <- which(grepl(term, rownames(conceptDistMat)))
# - fetch neighbourhood
ng1 <- vector(mode = "list", length = length(wTerm))
for (i in 1:length(wTerm)) {
  ng1[[i]] <- names(
    sort(conceptDistMat[wTerm[i], ], decreasing = F)[1:n_size+1]
  )
}
names(ng1) <- rownames(conceptDistMat)[wTerm]
for (i in 1:length(ng1)) {
  ng1[[i]] <- setdiff(ng1[[i]], names(ng1)[i])
}
ng2 <- vector(mode = "list", length = sum(sapply(ng1, length)))
c <- 0
for (i in 1:length(ng1)) {
  for (j in 1:length(ng1[[i]])) {
    c <- c + 1
    ng2[[c]] <- names(
    sort(
      conceptDistMat[which(rownames(conceptDistMat) == ng1[[i]][j]), ], 
      decreasing = F)[1:n_size+1]
    )
  }
}
names(ng2) <- unname(unlist(ng1))
for (i in 1:length(ng2)) {
  ng2[[i]] <- setdiff(ng2[[i]], names(ng2)[i])
}
graphData <- rbind(stack(ng1), stack(ng2))
graphData$ind <- as.character(graphData$ind)
graphData <- graphData[, c(2, 1)]
colnames(graphData) <- c('outgoing', 'incoming')
graphData$incoming[grepl("^wd_", graphData$incoming)] <- 
  toupper(graphData$incoming[grepl("^wd_", graphData$incoming)])
graphData$outgoing[grepl("^wd_", graphData$outgoing)] <- 
  toupper(graphData$outgoing[grepl("^wd_", graphData$outgoing)])

# - visualize w. {visNetwork}
nodes <- unique(c(graphData$outgoing, graphData$incoming))
nodes <- data.frame(id = 1:length(nodes),
                    label = nodes,
                    stringsAsFactors = F)
edges <- graphData
colnames(edges) <- c("from", "to")
edges$from <- sapply(edges$from, function(x) {
  nodes$id[which(nodes$label == x)]
})
edges$to <- sapply(edges$to, function(x) {
  nodes$id[which(nodes$label == x)]
})

# - visualize
visNetwork(nodes, edges, width = "100%") %>%
  visIgraphLayout() %>%
  visNodes(
    shape = "dot",
    color = list(
      background = "#0085AF",
      border = "#013848",
      highlight = "#FF8000"
    ),
    shadow = list(enabled = TRUE, size = 10)
  ) %>%
  visEdges(
    shadow = FALSE,
    color = list(color = "#0085AF", highlight = "#C62F4B")
  ) %>%
  visOptions(highlightNearest = TRUE, selectedBy = "label") %>%
  visLayout(randomSeed = 11)
```

***
Goran S. Milovanović & Mike Page

DataKolektiv, 2019.

contact: datakolektiv@datakolektiv.com

![](img/DK_Logo_100.png)

***
License: [GPLv3](http://www.gnu.org/licenses/gpl-3.0.txt)
This Notebook is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This Notebook is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this Notebook. If not, see <http://www.gnu.org/licenses/>.

***

