In-class Exercise 5

Part 1 - Explore JSON

1. load package

pacman::p_load(jsonlite,tidygraph, ggraph, visNetwork, tidyverse) 

2. read JSON file

MC1 <- jsonlite::fromJSON("D:/MITB/ISSS608/fruitpunchsamurai666/project/MC1/data/MC1.json")

3. read nodes and edge dataframe from JSON

#read the sub-dataframe from the json file, select and rearrange the columns needed
MC1_nodes <- as_tibble(MC1$nodes) %>% select(id, type, country)
MC1_edges <- as_tibble(MC1$links) %>% select(source, target, type, weight, key)

Part 2 - Network Visualization

1. load package

pacman::p_load(igraph, tidygraph, ggraph, 
               visNetwork, lubridate, clock,
               tidyverse, graphlayouts)

2. read data

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

glimpse(GAStech_edges)

Rows: 9,063
Columns: 8
$ source      <dbl> 43, 43, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 26, 26, 26…
$ target      <dbl> 41, 40, 51, 52, 53, 45, 44, 46, 48, 49, 47, 54, 27, 28, 29…
$ SentDate    <chr> "6/1/2014", "6/1/2014", "6/1/2014", "6/1/2014", "6/1/2014"…
$ SentTime    <time> 08:39:00, 08:39:00, 08:58:00, 08:58:00, 08:58:00, 08:58:0…
$ Subject     <chr> "GT-SeismicProcessorPro Bug Report", "GT-SeismicProcessorP…
$ MainSubject <chr> "Work related", "Work related", "Work related", "Work rela…
$ sourceLabel <chr> "Sven.Flecha", "Sven.Flecha", "Kanon.Herrero", "Kanon.Herr…
$ targetLabel <chr> "Isak.Baza", "Lucas.Alcazar", "Felix.Resumir", "Hideki.Coc…

3. Wrangling time

GAStech_edges <- GAStech_edges %>%
  mutate(SendDate = dmy(SentDate)) %>%
  mutate(Weekday = wday(SentDate,
                        label = TRUE,
                        abbr = FALSE))

4. Wrangling attributes by grouping by source-target-day and then removing emails that are cc to senders or only has been sent once.

GAStech_edges_aggregated <- GAStech_edges %>%
  filter(MainSubject == "Work related") %>%
  group_by(source, target, Weekday) %>%
    summarise(Weight = n()) %>%
  filter(source!=target) %>%
  filter(Weight > 1) %>%
  #ungroup() is a function in the dplyr package in R that removes grouping from a grouped data frame. When data is grouped, operations are applied to each group separately. ungroup() is used to reverse the grouping operation so that subsequent operations are applied to the entire data frame as a whole.
  ungroup()

5. build tidygraph data model and rearrange the rows by weight in descending order

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 1372 edges
#
# A directed multigraph with 1 component
#
# A tibble: 1,372 × 4
   from    to Weekday  Weight
  <int> <int> <ord>     <int>
1    40    41 Saturday     13
2    41    43 Monday       11
3    35    31 Tuesday      10
4    40    41 Monday       10
5    40    43 Monday       10
6    36    32 Sunday        9
# ℹ 1,366 more rows
#
# A tibble: 54 × 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
# ℹ 51 more rows

6. plot the graph without x and y axis using Fruchterman and Reingold layout

g <- ggraph(GAStech_graph,layout = "fr") + 
  geom_edge_link(aes(width=Weight), 
                 alpha=0.2) +
  geom_node_point(aes(colour = Department, 
                      size = 3))

g + theme_graph()

7. working with facet (edge as an example)

   set_graph_style()
   
   g <- ggraph(GAStech_graph, 
               layout = "nicely") + 
     geom_edge_link(aes(width=Weight), 
                    alpha=0.2) +
     scale_edge_width(range = c(0.1, 5)) +
     geom_node_point(aes(colour = Department), 
                     size = 2)
   
   g + facet_edges(~Weekday) +
     th_foreground(foreground = "grey80",  
                   border = TRUE) +
     theme(legend.position = 'bottom')

   

8. interactive network graph with visNetwork

   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()

#shades the nodes by assigning unique colour to each category in the group field.
GAStech_nodes <- GAStech_nodes %>%
  rename(group = Department) 

visNetwork(GAStech_nodes,
           GAStech_edges_aggregated) %>%
  visIgraphLayout(layout = "layout_with_fr") %>%
  visOptions(highlightNearest = TRUE,
             nodesIdSelection = TRUE) %>%
  visLegend() %>%
  visLayout(randomSeed = 123)