1. Installing and Launching R Packages

packages = c('igraph', 'tidygraph', 'ggraph', 'visNetwork', 'lubridate', 'tidyverse')

for(p in packages){library
  if(!require(p, character.only = T)){
    install.packages(p)
  }
  library(p, character.only = T)
}

2. Data Wrangling

GAStech_nodes <- read_csv("data/GAStech_email_node.csv")
GAStech_edges <- read_csv("data/GAStech_email_edge-v2.csv")

GAStech_edges$SentDate  = dmy(GAStech_edges$SentDate)
GAStech_edges$Weekday = wday(GAStech_edges$SentDate, label = TRUE, abbr = FALSE)

GAStech_edges_aggregated <- GAStech_edges %>%
  filter(MainSubject == "Work related") %>%
  group_by(source, target, Weekday) %>%
    summarise(Weight = n()) %>%
  filter(source!=target) %>%
  filter(Weight > 1) %>%
  ungroup()

GAStech_graph <- tbl_graph(nodes = GAStech_nodes, edges = GAStech_edges_aggregated, directed = TRUE)

GAStech_graph %>%
  activate(edges) %>%
  arrange(desc(Weight))

## # A tbl_graph: 54 nodes and 1456 edges
## #
## # A directed multigraph with 1 component
## #
## # Edge Data: 1,456 x 4 (active)
##    from    to Weekday Weight
##   <int> <int> <ord>    <int>
## 1    40    41 Tuesday     23
## 2    40    43 Tuesday     19
## 3    41    43 Tuesday     15
## 4    41    40 Tuesday     14
## 5    42    41 Tuesday     13
## 6    42    40 Tuesday     12
## # ... with 1,450 more rows
## #
## # Node Data: 54 x 4
##      id label           Department     Title           
##   <dbl> <chr>           <chr>          <chr>           
## 1     1 Mat.Bramar      Administration Assistant to CEO
## 2     2 Anda.Ribera     Administration Assistant to CFO
## 3     3 Rachel.Pantanal Administration Assistant to CIO
## # ... with 51 more rows

3. Task 1: Static Organisation Graph

With reference to the organisation network graph in Section 6.1 of Hands-on Exercise 10, you are required to complete the following tasks:

Improve the code chunk used to create the organisation network graph by using the latest functions provided in ggraph2.0.
Identify three aspects of the graph visualisation in Section 6.1 that can be improved.
Provide the sketch of your alternative design.
Using appropriate ggraph functions, plot the alternative design.

3.1 Initial plot from Section 6.1

g <- GAStech_graph %>%
  mutate(betweenness_centrality = centrality_betweenness()) %>%
  mutate(closeness_centrality = centrality_closeness()) %>%
  ggraph(layout = "nicely") + 
  geom_edge_link(aes()) +
  geom_node_point(aes(colour = closeness_centrality, size=betweenness_centrality))

g + theme_graph()

3.2 Sketch for Task 1’s Revised plot

Sketch

3.3 Revised plot from Section 6.1

Issue 1: Lack of Transparency in the edges makes it difficult for any analysis on the weight of the edges.
- Solution 1: Revised plot includes geom_edge_link() with weight of edges to control the width and alpha to be able to visualize any overlapping connections.
Issue 2: Unable to analyse any trend based on time even with the weekdays column given within the data table.
- Solution 2: Revised plot includes facet_edges() to reduce any overplotting and provide meaning to analyse the differences in the connection between nodes over the different days of the week.
Issue 3: Lack of Categorisation in the nodes for users to make up analysis based on connections between nodes.
- Solution 3: Revised plot includes a circle layout within ggraph(). Additionally, geom_node_voronoi() is added to visualise the departments of the nodes.

set_graph_style()

g <- GAStech_graph %>%
  mutate(betweenness_centrality = centrality_betweenness()) %>%
  mutate(closeness_centrality = centrality_closeness()) %>%
  ggraph(layout = "circle") + 
  geom_node_voronoi(aes(fill = Department), max.radius = 0.5, colour = 'white') +
  geom_edge_density(aes(fill = Weight)) +
  geom_edge_link(aes(width = Weight), alpha = 0.1) + 
  geom_node_point(aes(color = closeness_centrality, size = betweenness_centrality)) 

g + theme_graph() + facet_edges(~Weekday) + theme(legend.position = "bottom", legend.direction = "vertical")

## nerror = 4 
## Increasing madj from 23 to 28 and trying again.
## nerror = 4 
## Increasing madj from 28 to 34 and trying again.
## nerror = 4 
## Increasing madj from 23 to 28 and trying again.
## nerror = 4 
## Increasing madj from 28 to 34 and trying again.
## nerror = 4 
## Increasing madj from 23 to 28 and trying again.
## nerror = 4 
## Increasing madj from 28 to 34 and trying again.
## nerror = 4 
## Increasing madj from 23 to 28 and trying again.
## nerror = 4 
## Increasing madj from 28 to 34 and trying again.
## nerror = 4 
## Increasing madj from 23 to 28 and trying again.
## nerror = 4 
## Increasing madj from 28 to 34 and trying again.

4. Task 2: Interactive Organisation Graph

With reference to the organisation network graph in Section 7.4 of Hands-on Exercise 10, you are required to complete the following tasks:

Improve the design of the graph by incorporating the following interactivity:
- When a name is selected from the drop-down list, the corresponding node will not only be highlighted but also will be labelled. Furthermore, all the linked nodes of the selected node will also be labelled too.
- When a node of the interactive graph is selected, the node will not only be highlighted but also will be labelled. Furthermore, all the linked nodes of the selected node will be labelled as well.
Identify three aspects of the graph visualisation in Section 7.4 that can be improved.
Provide the sketch of your alternative design.
Using appropriate visNetwork functions, plot the alternative design.

4.1 Data preperation

GAStech_edges_aggregated <- GAStech_edges %>%
  left_join(GAStech_nodes, by = c("sourceLabel" = "label")) %>%
  rename(from = id) %>%
  left_join(GAStech_nodes, by = c("targetLabel" = "label")) %>%
  rename(to = id) %>%
  filter(MainSubject == "Work related") %>%
  group_by(from, to) %>%
    summarise(weight = n()) %>%
  filter(from!=to) %>%
  filter(weight > 1) %>%
  ungroup()

GAStech_nodes <- GAStech_nodes %>%
  rename(group = Department)

4.2 Initial plot from Section 7.4

visNetwork(GAStech_nodes, GAStech_edges_aggregated) %>%
  visIgraphLayout(layout = "layout_with_fr") %>%
  visOptions(highlightNearest = TRUE, nodesIdSelection = TRUE)

4.3 Sketch for Task 2’s Revised plot