Exploring Social Network Visualization with R: Successes and Challenges

In the realm of data visualization, exploring social networks can yield fascinating insights into relationships and connectivity. Recently, I delved into this area using R, leveraging packages like GGally, network, sna, and ggplot2. Here’s a recount of my journey, highlighting both successes and challenges encountered along the way.

Package Installation and Setup: 

The initial step was straightforward—installing and loading the necessary packages. Using install.packages() and library() commands, I quickly integrated GGally, network, sna, and ggplot2 into my R environment.

Generating Random Network Data:

I utilized the rgraph() function from the network package to create a random graph consisting of 10 nodes. Setting mode = "graph" and tprob = 0.5 ensured a symmetric and undirected graph.

Visualizing the Network:

With the network data prepared, I used ggnet2() from GGally to generate a visualization of the social network. This function creates an aesthetically pleasing graph representation. 

CODE : 

install.packages("GGally")

install.packages("network")

install.packages("sna")

install.packages("ggplot2")

library(GGally)

library(network)

library(sna)

library(ggplot2)

# Generate a random graph with 10 nodes and undirected edges

net <- rgraph(10, mode = "graph", tprob = 0.5)

# Create a network object from the random graph

net <- network(net, directed = FALSE)

# Assign vertex (node) names to the network

network.vertex.names(net) <- letters[1:10]

# Visualize the network using ggnet2

ggnet2(net)

ggnet2(net, node.size = 6, node.color = "black", edge.size = 1, edge.color = "grey")



Luckily I didn't run into any major failures, at first I tried to run the code but didn't have package "sna" installed but that was a quick fix. 

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