#Getting started with igraph package
install.packages("igraph")
library(igraph)
#########################
# OBJECT OF CLASS GRAPH
#########################

#########################
### Creating from scratch using build-in fuctions

#### 1. Deterministic Graph Generators

#empty
g <- graph.empty(n=10, directed=TRUE) # similar to network.initialize
class(g)
      
g<-graph.full(n=10, directed = FALSE, loops = FALSE)

#Stars
g<-graph.star(n=10, mode="out")
g<-graph.star(n=10, mode="in")
g<-graph.star(n=10, mode="mutual")
g<-graph.star(n=10, mode="undirected")

#Full Citation graph (i --> j only if j<i)
g<-make_full_citation_graph(10, directed = TRUE)
plot(g)

#Make full bipartite graph (2-mode net)
g<-make_full_bipartite_graph(n1=10, n2=5)
plot(g)


#Make trees
g<-make_tree(n=10, children=2)
plot(g)


??make
#########################
### Graphs from adjacency matrices

nodeInfo <- read.csv("vertexAttributes.csv", header=TRUE,stringsAsFactors=FALSE, sep=",")
nodeInfo

relations <- read.csv("relationalData.csv", header=FALSE,stringsAsFactors=FALSE, sep=",")
relations <- as.matrix(relations)
rownames(relations) <- nodeInfo$name
colnames(relations) <- nodeInfo$name

?graph_from_adjacency_matrix
relationsg = graph_from_adjacency_matrix(relations, mode="undirected")

vertex_attr(relationsg, "NumProject") <- nodeInfo$NumProject
vertex_attr(relationsg, "Type") <- nodeInfo$Type
vertex_attr(relationsg, "Prov") <- nodeInfo$Prov

vertex_attr(relationsg)

#########################
### Graphs from data frames

actors <- data.frame(
  name = c("Alice", "Bob", "Cecil", "David", "Esmeralda"),
  age = c(48, 33, 45, 34, 21),
  gender = c("F", "M", "F", "M", "F")
)

links <- data.frame(
  from = c("Bob", "Cecil", "Cecil", "David", "David", "Esmeralda"),
  to = c("Alice", "Bob", "Alice", "Alice", "Bob", "Alice"),
  friendship = c(4, 5, 5, 2, 1, 1),
  advice = c(4, 5, 5, 4, 2, 3)
)

links
?graph_from_data_frame
g <- graph_from_data_frame(links, directed = TRUE, vertices = actors)

V(g)$age
E(g)


#########################
### Basic operations for an object of class graph 

#Retrieve the adjacency matrix of a graph

get.adjacency(g, sparse=F)

get.adjacency(g, sparse=T) #Sparse version

# nodes and node count
V(g)
vcount(g)

# edges and edge count
E(g)
ecount(g)

# add node attribute
V(g)$height = c(152, 155, 178, 167, 198)
g

# add edge attribute (edge values or weights)
E(g)$weight = c(1, 2, 2, 5, 5, 4)
g


#delete attributes

g <- delete_vertex_attr(g, "height")
g <- delete_edge_attr(g, "advice")


# add/remove nodes/edges
g = add_vertices(g, 1, attr = list(name = "NewOne", age = 20, gender = "M"))
V(g)

g = add_edges(g, c("NewOne", "Alice"), attr = list(friendship = 5))
E(g)

g = delete_edges(g, "NewOne|Alice")
E(g)

g = delete_vertices(g, "NewOne")
V(g)

# Indexing nodes

V(g)[1] # by their indices

V(g)["Alice"] # by their labels/names

V(g)[age <= 25] # by their attributes

V(g)[gender == "M"] 

# Indexing edges

E(g)[1] # by their indices
E(g)[1:3]

E(g)["Bob|Alice"] # by their starting and ending points


#Indexing the network matrix directly

g[,1]


###################################
##### COERCE GRAPHS INTO  OTHER CLASSES

# write the graph to data frame
as_data_frame(g, what="vertices")

as_data_frame(g, what="both")


# write graph to a sparse adjacency matrix
as_adjacency_matrix(g)


# weighted matrix according to the edges
as_adjacency_matrix(g)

as_adjacency_matrix(g, attr = "advice")

##############################
## some basic graph theory igraph

all_simple_paths(g, from="Bob", to = V(g), mode = "all")

shortest_paths(g, from="Bob", to = V(g), mode = "all", output = "both")
?shortest_paths


?distances
distances(g, mode = "out")

plot(g)