Data Wrangling

Getting Start

Installing and loading the required libraries

pacman::p_load(igraph, tidygraph, ggraph, 
               visNetwork, lubridate, clock,
               tidyverse, graphlayouts, 
               concaveman, ggforce, jsonlite, dplyr)

Importing data

t_data <- fromJSON("data/MC1_graph.json",
                   simplifyDataFrame = TRUE)

Data processing

Extracting Edges and Nodes

nodes_tbl <- as_tibble(t_data$nodes)
edges_tbl <- as_tibble(t_data$links) 

Creating Knowledge Graph

Mapping from node id to row index

id_map <- tibble(id = nodes_tbl$id,
                 index = seq_len(
                   nrow(nodes_tbl)))

Map source and target IDs to row indices

edges_tbl <- edges_tbl %>%
  left_join(id_map, by = c("source" = "id")) %>%
  rename(from = index) %>%
  left_join(id_map, by = c("target" = "id")) %>%
  rename(to = index)

Filter out any unmatched (invalid) edges

edges_tbl <- edges_tbl %>%
  filter(!is.na(from), !is.na(to))

Creating tidygraph

graph <- tbl_graph(nodes = nodes_tbl, 
                   edges = edges_tbl, 
                   directed = t_data$directed)

class(graph)

[1] "tbl_graph" "igraph"   

Visualising the knowledge graph

The tactic we used to conduct our visual analytics is ploting the whole knowledge for this dataset and then plotting sub-graphs to gain meaningful visual discovery since the whole graph will be very messy and we can hardy discover any useful patterns.

set.seed(1234)

Visualising the whole graph

ggraph(graph, layout = "fr") +
  geom_edge_link(alpha = 0.3, 
                 colour = "gray") +
  geom_node_point(aes(color = `Node Type`), 
                  size = 4) +
  geom_node_text(aes(label = name), 
                 repel = TRUE, 
                 size = 2.5) +
  theme_void()

Warning: ggrepel: 17411 unlabeled data points (too many overlaps). Consider
increasing max.overlaps