LASER Scholars Scavenger Hunt

Author

LASER Institute

Published

July 17, 2025

Sociogram

Code

# Load required libraries
library(tidyverse)  # For data manipulation and visualization
library(tidygraph)  # For network analysis and manipulation
library(ggraph)     # For network visualization

# Load the data
scholar_nodes <- read_csv("module-0/data/scholar-nodes-2024.csv")  # Load node data from CSV
scholar_edges <- read_csv("module-0/data/scholar-edges-2024.csv")  # Load edge data from CSV

# Create a tidygraph object
scholar_network <- tbl_graph(nodes = scholar_nodes, 
                             edges = scholar_edges,
                             directed = TRUE)  # Create a directed network graph from the nodes and edges

# Plot the network
ggraph(scholar_network, layout = "stress") + 
  geom_edge_link(arrow = arrow(length = unit(1, 'mm')), 
                 end_cap = circle(3, 'mm'),
                 start_cap = circle(3, 'mm'),
                 alpha = .08) +  # Draw edges with arrows, adjusting transparency and end/start caps
  geom_node_point(aes(size = local_size(),
                      color = attribute)) +  # Draw nodes with size based on local size and color based on attribute
  geom_node_text(aes(label = name,
                     size = local_size()),
                 repel = TRUE) +  # Add node labels, with text size based on local size, using repulsion to avoid overlap
  labs(title = "LASER Scholars Scavenger Hunt Network",
       color = "Selected most for:") +  # Add a title to the plot and set the legend title for color
  scale_size_continuous(range = c(3, 5)) +  # Scale the node sizes to be within a specified range
  guides(size = "none") +  # Remove the size legend
  theme_graph() +  # Apply a theme suitable for graphs
  theme(plot.title = element_text(hjust = 0.5),  # Center the plot title
        legend.text = element_text(size = 12))  # Increase the legend text size

--- title: "LASER Scholars Scavenger Hunt" author: "LASER Institute" date: today format: html: toc: true toc-depth: 4 toc-location: right code-fold: show messages: false code-tools: source: true toggle: true caption: none theme: light: simplex dark: cyborg editor: visual bibliography: lit/references.bib --- ### Sociogram ```{r} #| echo: false #| warning: false #| messages: false # Load required libraries library(tidyverse) # For data manipulation and visualization library(tidygraph) # For network analysis and manipulation library(ggraph) # For network visualization # Load the data scholar_nodes <- read_csv("module-0/data/scholar-nodes-2025.csv") # Load node data from CSV scholar_edges <- read_csv("module-0/data/scholar-edges-2025.csv") # Load edge data from CSV # Create a tidygraph object scholar_network <- tbl_graph(nodes = scholar_nodes, edges = scholar_edges, directed = TRUE) # Create a directed network graph from the nodes and edges # Plot the network ggraph(scholar_network, layout = "stress") + geom_edge_link(arrow = arrow(length = unit(1, 'mm')), end_cap = circle(3, 'mm'), start_cap = circle(3, 'mm'), alpha = .08) + # Draw edges with arrows, adjusting transparency and end/start caps geom_node_point(aes(size = local_size(), color = attribute)) + # Draw nodes with size based on local size and color based on attribute geom_node_text(aes(label = name, size = local_size()), repel = TRUE) + # Add node labels, with text size based on local size, using repulsion to avoid overlap labs(title = "LASER Scholars Scavenger Hunt Network", color = "Selected most for:") + # Add a title to the plot and set the legend title for color scale_size_continuous(range = c(2, 3.5)) + # Scale the node sizes to be within a specified range guides(size = "none") + # Remove the size legend theme_graph() + # Apply a theme suitable for graphs theme(plot.title = element_text(hjust = 0.5), # Center the plot title legend.text = element_text(size = 12)) # Increase the legend text size ``` ### Code ```{r} #| messages: false #| warning: false #| output: false # Load required libraries library(tidyverse) # For data manipulation and visualization library(tidygraph) # For network analysis and manipulation library(ggraph) # For network visualization # Load the data scholar_nodes <- read_csv("module-0/data/scholar-nodes-2024.csv") # Load node data from CSV scholar_edges <- read_csv("module-0/data/scholar-edges-2024.csv") # Load edge data from CSV # Create a tidygraph object scholar_network <- tbl_graph(nodes = scholar_nodes, edges = scholar_edges, directed = TRUE) # Create a directed network graph from the nodes and edges # Plot the network ggraph(scholar_network, layout = "stress") + geom_edge_link(arrow = arrow(length = unit(1, 'mm')), end_cap = circle(3, 'mm'), start_cap = circle(3, 'mm'), alpha = .08) + # Draw edges with arrows, adjusting transparency and end/start caps geom_node_point(aes(size = local_size(), color = attribute)) + # Draw nodes with size based on local size and color based on attribute geom_node_text(aes(label = name, size = local_size()), repel = TRUE) + # Add node labels, with text size based on local size, using repulsion to avoid overlap labs(title = "LASER Scholars Scavenger Hunt Network", color = "Selected most for:") + # Add a title to the plot and set the legend title for color scale_size_continuous(range = c(3, 5)) + # Scale the node sizes to be within a specified range guides(size = "none") + # Remove the size legend theme_graph() + # Apply a theme suitable for graphs theme(plot.title = element_text(hjust = 0.5), # Center the plot title legend.text = element_text(size = 12)) # Increase the legend text size ```