# A tibble: 255 × 5
From To Type Notes Generation
<chr> <chr> <chr> <chr> <chr>
1 Targaryen Stark Married R+L=J Current
2 Baratheon Martell Engaged died Current
3 Baratheon Stark Engaged broken Current
4 Martell Essos Married <NA> Current
5 Martell Reach Affair <NA> Current
6 Martell Essos Affair <NA> Current
7 Martell Essos Affair <NA> Current
8 Martell Septa Affair <NA> Current
9 Martell Dorne Affair <NA> Current
10 Martell Targaryen Married <NA> Current
# … with 245 more rows
The data is about the marriages happened between families in GOT. There are 5 variables and 255 case. First 2 variables are ‘From’ and ‘To’ which are the most important part of a ‘Network Analysis’ data. We also have the ‘Type’ of relationship and ‘Generation’ which classify the case as ‘Past’ or ‘Current’.
Creating igraph from dataframe
We are cretaing an igraph from the GOT marriage dataframe so that we can do the network analysis.
GOT marriage network has 20 vertices as listed below. It has 255 edges but the number of unique type relationship is 3; ‘Married’, ‘Engagedf’, ‘Affair’. Unique classification of ‘generation’ attribute are ‘Current’ and ‘Past’.
Again, checking the vertex count and edge count of the network. The vertex count for grouped network data and the original network data are same as 20. But edge count differs. For original network data, there are 255 edges but for grouped data, edges are 88. This is because, in the grouped network, each edge is a type of relationship from a family to another family. In the original data, each edge represent a unique relationship between families and therefore it is 255.
Code
vcount(got_marriage.net)
[1] 20
Code
ecount(got_marriage.net)
[1] 255
Code
vcount(got_marriage_group.net)
[1] 20
Code
ecount(got_marriage_group.net)
[1] 88
The network is not bipartite which means it is a single mode network. It is un directed or other words symmetrical. It is also not weighted.
Code
is_bipartite(got_marriage.net)
[1] FALSE
Code
is_directed(got_marriage.net)
[1] FALSE
Code
is_weighted(got_marriage.net)
[1] FALSE
The vertex attributes and edge attributes are listed. Vertices has only one attribute named ‘name’. and edges have 3 attributes; ‘Type’, ‘Notes’, ‘Generation’.
---title: "Week 1 Challenge"author: "Jerin Jacob"description: "Loading Data and Creating a Network"date: "02/15/2023"format: html: toc: true code-fold: true code-copy: true code-tools: true# editor: visualcategories: - challenge_1---```{r}#| label: setup#| include: falselibrary(tidyverse)library(googlesheets4)library(igraph)library(statnet)knitr::opts_chunk$set(echo =TRUE)```## Reading in the DataGOT Marriage data is read in.```{r}got_marriage <-read_csv("_data/got/got_marriages.csv", show_col_types =FALSE) got_marriage```The data is about the marriages happened between families in GOT. There are 5 variables and 255 case. First 2 variables are 'From' and 'To' which are the most important part of a 'Network Analysis' data. We also have the 'Type' of relationship and 'Generation' which classify the case as 'Past' or 'Current'. ## Creating igraph from dataframeWe are cretaing an igraph from the GOT marriage dataframe so that we can do the network analysis.```{r}got_marriage.net<-graph_from_data_frame(got_marriage,directed=FALSE)#deg <- degree(got_marriage.net, mode="all")got_marriage.net``````{r}# got_marriage.net %>%# group_by(From, To, Type) %>%# summarise(total_count = n())```An igraph is created for the data that is groouped by 'From', 'To', and 'Type'```{r}got_marriage_group <- got_marriage %>%group_by(From, To, Type) %>%summarise(total_count =n())got_marriage_group.net<-graph_from_data_frame(got_marriage_group,directed=FALSE)got_marriage_group.net```GOT marriage network has 20 vertices as listed below. It has 255 edges but the number of unique type relationship is 3; 'Married', 'Engagedf', 'Affair'. Unique classification of 'generation' attribute are 'Current' and 'Past'.```{r}#table(got_marriage$Type)V(got_marriage.net)unique(E(got_marriage.net)$Type)unique(E(got_marriage.net)$Generation)```Again, checking the vertex count and edge count of the network. The vertex count for grouped network data and the original network data are same as 20. But edge count differs. For original network data, there are 255 edges but for grouped data, edges are 88. This is because, in the grouped network, each edge is a type of relationship from a family to another family. In the original data, each edge represent a unique relationship between families and therefore it is 255. ```{r}vcount(got_marriage.net)ecount(got_marriage.net)vcount(got_marriage_group.net)ecount(got_marriage_group.net)```The network is not bipartite which means it is a single mode network. It is un directed or other words symmetrical. It is also not weighted.```{r}is_bipartite(got_marriage.net)is_directed(got_marriage.net)is_weighted(got_marriage.net)```The vertex attributes and edge attributes are listed. Vertices has only one attribute named 'name'. and edges have 3 attributes; 'Type', 'Notes', 'Generation'.```{r}vertex_attr_names(got_marriage.net)edge_attr_names(got_marriage.net)```The vertex attribute name is accessed.```{r}V(got_marriage.net)$name``````{r}E(got_marriage.net)$Generation```Plotting the GOT marriage network```{r}plot(got_marriage.net, arrow.mode="-")```Plotting the grouped GOT marriage network```{r}plot(got_marriage_group.net, arrow_mode ="-")```
