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pacman::p_load(igraph, tidygraph, ggraph,
visNetwork, lubridate, clock,
tidyverse, graphlayouts,jsonlite)In-class_Ex05
Import the package and data
Import json type data
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MC1 <- fromJSON("data/MC1.json")Extract data frame from MC1 list as tibble table
Use select to pick columns and reorganize the sequence of data frame
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MC1_noeds <- as_tibble(MC1$nodes) %>%
select(id,type,country)Show the code
MC1_edges <- as_tibble(MC1$links) %>%
select(source,target,type,weight,key)Import other csv data
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GAStech_nodes <- read_csv("data/GAStech_email_node.csv")
GAStech_edges <- read_csv("data/GAStech_email_edge-v2.csv")Show the code
head(GAStech_edges)# A tibble: 6 × 8
source target SentDate SentTime Subject MainSubject sourceLabel targetLabel
<dbl> <dbl> <chr> <time> <chr> <chr> <chr> <chr>
1 43 41 6/1/2014 08:39 GT-Seismi… Work relat… Sven.Flecha Isak.Baza
2 43 40 6/1/2014 08:39 GT-Seismi… Work relat… Sven.Flecha Lucas.Alca…
3 44 51 6/1/2014 08:58 Inspectio… Work relat… Kanon.Herr… Felix.Resu…
4 44 52 6/1/2014 08:58 Inspectio… Work relat… Kanon.Herr… Hideki.Coc…
5 44 53 6/1/2014 08:58 Inspectio… Work relat… Kanon.Herr… Inga.Ferro
6 44 45 6/1/2014 08:58 Inspectio… Work relat… Kanon.Herr… Varja.LagosWrangle data type
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GAStech_edges <- GAStech_edges %>%
mutate(SendDate = dmy(SentDate)) %>%
mutate(Weekday = wday(SentDate,
label = TRUE,
abbr = FALSE))
GAStech_edges# A tibble: 9,063 × 10
source target SentDate SentTime Subject MainSubject sourceLabel targetLabel
<dbl> <dbl> <chr> <time> <chr> <chr> <chr> <chr>
1 43 41 6/1/2014 08:39 GT-Seism… Work relat… Sven.Flecha Isak.Baza
2 43 40 6/1/2014 08:39 GT-Seism… Work relat… Sven.Flecha Lucas.Alca…
3 44 51 6/1/2014 08:58 Inspecti… Work relat… Kanon.Herr… Felix.Resu…
4 44 52 6/1/2014 08:58 Inspecti… Work relat… Kanon.Herr… Hideki.Coc…
5 44 53 6/1/2014 08:58 Inspecti… Work relat… Kanon.Herr… Inga.Ferro
6 44 45 6/1/2014 08:58 Inspecti… Work relat… Kanon.Herr… Varja.Lagos
7 44 44 6/1/2014 08:58 Inspecti… Work relat… Kanon.Herr… Kanon.Herr…
8 44 46 6/1/2014 08:58 Inspecti… Work relat… Kanon.Herr… Stenig.Fus…
9 44 48 6/1/2014 08:58 Inspecti… Work relat… Kanon.Herr… Hennie.Osv…
10 44 49 6/1/2014 08:58 Inspecti… Work relat… Kanon.Herr… Isia.Vann
# ℹ 9,053 more rows
# ℹ 2 more variables: SendDate <date>, Weekday <ord>Sum up by weekday
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GAStech_edges_aggregated <- GAStech_edges %>%
filter(MainSubject == "Work related") %>%
group_by(source, target, Weekday) %>%
summarise(Weight = n(),.groups="drop") %>%
filter(source!=target) %>%
filter(Weight > 1)
GAStech_edges_aggregated# A tibble: 1,372 × 4
source target Weekday Weight
<dbl> <dbl> <ord> <int>
1 1 2 Sunday 5
2 1 2 Monday 2
3 1 2 Tuesday 3
4 1 2 Wednesday 4
5 1 2 Friday 6
6 1 3 Sunday 5
7 1 3 Monday 2
8 1 3 Tuesday 3
9 1 3 Wednesday 4
10 1 3 Friday 6
# ℹ 1,362 more rowsBuild tidygraph data
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GAStech_graph <- tbl_graph(nodes = GAStech_nodes,
edges = GAStech_edges_aggregated,
directed = TRUE)
GAStech_graph# A tbl_graph: 54 nodes and 1372 edges
#
# A directed multigraph with 1 component
#
# 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
4 4 Linda.Lagos Administration Assistant to COO
5 5 Ruscella.Mies.Haber Administration Assistant to Engineering Group Manag…
6 6 Carla.Forluniau Administration Assistant to IT Group Manager
# ℹ 48 more rows
#
# A tibble: 1,372 × 4
from to Weekday Weight
<int> <int> <ord> <int>
1 1 2 Sunday 5
2 1 2 Monday 2
3 1 2 Tuesday 3
# ℹ 1,369 more rows