### Install and Load ggNetView R Package

Source: https://github.com/bolinho9ue/ggnetview/blob/main/README.md

Installs and loads the ggNetView package in R. Requires the package file to be available locally. Assumes R version 4.0 or later.

```R
install.packages("path/to/ggNetView_unicarinate.zip")
library(ggNetView)
```

--------------------------------

### Multi-Orientation Environment-Species Heatmaps

Source: https://context7.com/bolinho9ue/ggnetview/llms.txt

Prepares environmental data for creating integrated visualizations linking species networks with environmental correlations. This example shows the creation of a sample environmental data frame.

```r
# Prepare environmental data
env_data <- data.frame(
  matrix(rnorm(100 * 20), nrow = 100, ncol = 20)
)
colnames(env_data) <- paste0("Env_", 1:20)
```

--------------------------------

### Create Multi-Orientation Heatmap with ggNetView (Correlation)

Source: https://context7.com/bolinho9ue/ggnetview/llms.txt

Generates multi-orientation heatmap visualizations using gglink_heatmaps. This example uses correlation as the relation_method, with Spearman correlation, and specifies various orientations and plotting parameters.

```r
heatmap_plots <- gglink_heatmaps(
  env = env_data,
  spec = species_data,
  env_select = env_groups,
  spec_select = spec_groups,
  spec_layout = "ringle",
  relation_method = "correlation",
  cor.method = "spearman",
  cor.use = "pairwise",
  drop_nonsig = FALSE,
  shape = 22,
  distance = 4,
  orientation = c("top_right", "bottom_right", "top_left", "bottom_left"),
  r = 6
)
```

--------------------------------

### Create Heatmap with ggNetView (Mantel Test)

Source: https://context7.com/bolinho9ue/ggnetview/llms.txt

Generates a heatmap visualization using gglink_heatmaps with the mantel test as the relation_method. This example specifies mantel test parameters and a subset of orientations.

```r
heatmap_mantel <- gglink_heatmaps(
  env = env_data,
  spec = species_data,
  env_select = env_groups,
  spec_select = spec_groups,
  relation_method = "mantel",
  mantel.method = "mantel",
  mantel.method2 = "spearman",
  mantel.alternative = "two.sided",
  orientation = c("top_right", "bottom_left"),
  r = 8
)
```

--------------------------------

### Prepare Species Data for ggNetView

Source: https://context7.com/bolinho9ue/ggnetview/llms.txt

Prepares species abundance data by selecting the first 8 columns and renaming them to 'Spec_1' through 'Spec_8'. This ensures the data is in the correct format for the ggNetView functions.

```r
species_data <- data.frame(abundance_mat[, 1:8])
colnames(species_data) <- paste0("Spec_", 1:8)
```

--------------------------------

### ggNetView: Gephi-style Layout with Module Organization

Source: https://context7.com/bolinho9ue/ggnetview/llms.txt

Creates a network plot with a Gephi-style layout, organizing nodes by modules. Allows for label display, outer borders, and control over point size and orientation.

```r
p4 <- ggNetView(
  graph_obj = graph_obj,
  layout = "gephi",
  layout.module = "adjacent",
  k_nn = 10,
  label = TRUE,
  labelsize = 5,
  add_outer = TRUE,
  shape = 22,
  pointsize = c(4, 14),
  orientation = "up",
  seed = 1115
)
```

--------------------------------

### Import Network Data in R

Source: https://github.com/bolinho9ue/ggnetview/blob/main/README.md

Imports network data from a CSV file into the ggNetView package. The function expects a valid file path to the CSV. Output is a network object ready for visualization.

```R
my_network <- read.csv("path/to/your_network_data.csv")
```

--------------------------------

### ggNetView: Petal Layout with Labels and Outer Borders

Source: https://context7.com/bolinho9ue/ggnetview/llms.txt

Visualizes a network using a petal layout with specified radius, label display, and outer border styling. It controls point appearance and seed for reproducibility.

```r
p2 <- ggNetView(
  graph_obj = graph_obj,
  layout = "petal",
  r = 1.5,
  label = TRUE,
  labelsize = 4,
  add_outer = TRUE,
  outerwidth = 1.5,
  outerlinetype = 2,
  outeralpha = 0.3,
  pointalpha = 0.8,
  pointsize = c(3, 10),
  seed = 1115
)
```

--------------------------------

### Build Network from WGCNA Results in R

Source: https://context7.com/bolinho9ue/ggnetview/llms.txt

Generates a network graph from WGCNA Topological Overlap Matrix (TOM) and module assignment data. It allows for optional node annotations and supports directed/undirected graphs. Dependencies include the 'ggNetView' package.

```r
# Simulate WGCNA output
wgcna_edges <- data.frame(
  from = rep(paste0("Gene_", 1:20), each = 5),
  to = paste0("Gene_", sample(1:20, 100, replace = TRUE)),
  weight = runif(100, 0.1, 0.9)
)

wgcna_modules <- data.frame(
  ID = paste0("Gene_", 1:20),
  Module = sample(c("blue", "red", "green", "yellow"), 20, replace = TRUE)
)

# Build network from WGCNA
graph_wgcna <- build_graph_from_wgcna(
  wgcna_tom = wgcna_edges,
  module = wgcna_modules,
  directed = FALSE,
  seed = 1115
)

# With additional annotations
gene_annotation <- data.frame(
  name = paste0("Gene_", 1:20),
  Pathway = sample(c("Metabolism", "Signaling", "Transport"), 20, replace = TRUE),
  Expression = rnorm(20, mean = 5, sd = 2)
)

graph_wgcna_full <- build_graph_from_wgcna(
  wgcna_tom = wgcna_edges,
  module = wgcna_modules,
  node_annotation = gene_annotation,
  directed = FALSE
)
```

--------------------------------

### ggNetView: Circular Rings Layout for Hierarchical Visualization

Source: https://context7.com/bolinho9ue/ggnetview/llms.txt

Visualizes hierarchical data using a circular rings layout. Configurable parameters include the number of rings, radius, point shape, size, node label size, and line alpha.

```r
p5 <- ggNetView(
  graph_obj = graph_obj,
  layout = "rings",
  ring_n = 5,
  r = 1.2,
  shape = 21,
  pointalpha = 0.85,
  pointsize = c(3, 12),
  nodelabsize = 3,
  linealpha = 0.25,
  orientation = "up"
)
```

--------------------------------

### Visualize Network in R using ggNetView

Source: https://github.com/bolinho9ue/ggnetview/blob/main/README.md

Generates a network visualization using the imported network data. Requires the network object to be previously loaded. The output is a plot object.

```R
plot_network(my_network)
```

--------------------------------

### Build Network from Correlation Matrix in R

Source: https://context7.com/bolinho9ue/ggnetview/llms.txt

Constructs a network graph from a numeric abundance matrix using WGCNA, SpiecEasi, or correlation-based methods. It supports various transformation, correlation, and module detection algorithms, and allows for node annotations. Dependencies include the 'ggNetView' package and potentially others like 'WGCNA' or 'SpiecEasi'.

```r
library(ggNetView)

# Create sample microbiome abundance matrix (samples x features)
set.seed(1115)
abundance_mat <- matrix(
  rpois(100 * 50, lambda = 10),
  nrow = 100,
  ncol = 50
)
colnames(abundance_mat) <- paste0("Species_", 1:50)

# Build correlation-based network with WGCNA method
graph_obj <- build_graph_from_mat(
  mat = abundance_mat,
  transfrom.method = "log2",
  r.threshold = 0.7,
  p.threshold = 0.05,
  method = "WGCNA",
  cor.method = "spearman",
  proc = "BH",
  module.method = "Fast_greedy",
  top_modules = 10,
  seed = 1115
)

# Build network using SpiecEasi for sparse compositional data
graph_obj_spiec <- build_graph_from_mat(
  mat = abundance_mat,
  method = "SpiecEasi",
  SpiecEasi.method = "mb",
  module.method = "Walktrap",
  top_modules = 15
)

# Add node annotations
node_annotation <- data.frame(
  name = paste0("Species_", 1:50),
  Phylum = sample(c("Firmicutes", "Bacteroidetes", "Proteobacteria"), 50, replace = TRUE),
  Function = sample(c("Degrader", "Producer", "Symbiont"), 50, replace = TRUE)
)

graph_obj_annotated <- build_graph_from_mat(
  mat = abundance_mat,
  method = "cor",
  cor.method = "pearson",
  r.threshold = 0.6,
  p.threshold = 0.01,
  node_annotation = node_annotation,
  top_modules = 12
)
```

--------------------------------

### Build Network from Data Frame in R

Source: https://context7.com/bolinho9ue/ggnetview/llms.txt

Constructs a network graph from an edge list data frame, which can include edge weights. It supports directed and undirected graphs and allows for node annotations. Various module detection algorithms can be applied. Dependencies include the 'ggNetView' package.

```r
# Create edge list data frame
edges <- data.frame(
  from = c("A", "A", "B", "B", "C", "D", "E", "F"),
  to = c("B", "C", "C", "D", "E", "E", "F", "A"),
  weight = c(0.8, 0.75, 0.9, 0.65, 0.7, 0.85, 0.6, 0.72)
)

# Build simple network
graph_simple <- build_graph_from_df(
  df = edges,
  directed = FALSE,
  module.method = "Fast_greedy",
  top_modules = 5,
  seed = 1115
)

# Build network with node annotations
node_info <- data.frame(
  name = LETTERS[1:6],
  type = c("TypeI", "TypeI", "TypeII", "TypeII", "TypeIII", "TypeIII"),
  importance = c(10, 8, 12, 9, 11, 7)
)

graph_annotated <- build_graph_from_df(
  df = edges,
  node_annotation = node_info,
  directed = FALSE,
  module.method = "Walktrap",
  top_modules = 3
)

# Build directed network with edge betweenness clustering
graph_directed <- build_graph_from_df(
  df = edges,
  directed = TRUE,
  module.method = "Edge_betweenness",
  top_modules = 10
)
```

--------------------------------

### Save Network Graph in R

Source: https://github.com/bolinho9ue/ggnetview/blob/main/README.md

Saves the generated network graph to a file. Supports various formats depending on the underlying plotting library. Requires a filename as input.

```R
ggsave("network_visualization.png")
```

--------------------------------

### ggNetView: Heart-Centered Layout with Custom Styling

Source: https://context7.com/bolinho9ue/ggnetview/llms.txt

Generates a network visualization using a heart-centered layout with custom point shapes, sizes, and line styling. Includes jittering for overlapping points and option to remove nodes.

```r
p3 <- ggNetView(
  graph_obj = graph_obj,
  layout = "heart_centered",
  shape = 21,
  pointalpha = 0.9,
  pointsize = c(2, 15),
  pointstroke = 0.5,
  mapping_line = TRUE,
  linealpha = 0.3,
  linecolor = "grey60",
  jitter = TRUE,
  jitter_sd = 0.05,
  remove = TRUE,
  seed = 1115
)
```

--------------------------------

### Visualize Network with Custom Layouts in R

Source: https://context7.com/bolinho9ue/ggnetview/llms.txt

Generates publication-quality network visualizations using ggplot2 and ggraph. It supports various graph objects created by ggNetView functions and offers extensive customization options for layouts, node shapes, sizes, and colors. Dependencies include 'ggNetView', 'ggplot2', and 'ggraph'.

```r
# Basic star layout visualization
p1 <- ggNetView(
  graph_obj = graph_obj,
  layout = "star",
  shape = 21,
  pointsize = c(2, 12),
  seed = 1115
)


```

--------------------------------

### Random Matrix Theory Threshold Selection

Source: https://context7.com/bolinho9ue/ggnetview/llms.txt

Automatically determines optimal correlation threshold for network construction using Random Matrix Theory. It analyzes eigenvalue statistics and generates plots to justify the chosen threshold.

```r
# Create correlation matrix
cor_matrix <- cor(t(abundance_mat), method = "spearman")

# Run RMT analysis with plot generation
rmt_result <- ggNetView_RMT(
  mat = cor_matrix,
  nr_thresholds = 51,
  unfold.method = "gaussian",
  discard.outliers = TRUE,
  discard.zeros = TRUE,
  min.mat.dim = 40,
  save_plots = TRUE,
  out_dir = "RMT_analysis",
  verbose = TRUE
)

# Access recommended threshold
optimal_threshold <- rmt_result$chosen_threshold
cat("Optimal threshold:", optimal_threshold, "\n")
cat("Selection reason:", rmt_result$chosen_reason, "\n")

# View threshold scan results
print(head(rmt_result$scores))
#   threshold eff_dim nr_zeros nr_spacings  kld_exp  kld_wig    ks_p sse_exp
# 1    0.3500     245   128475         244 0.045123 0.234567 0.12345 0.00234
# 2    0.4000     198   145632         197 0.032456 0.198234 0.23456 0.00198
# 3    0.4500     156   162789         155 0.028901 0.176543 0.34567 0.00167

# Build network using RMT-selected threshold
graph_rmt_optimal <- build_graph_from_mat(
  mat = abundance_mat,
  method = "cor",
  cor.method = "spearman",
  r.threshold = optimal_threshold,
  p.threshold = 0.05,
  module.method = "Fast_greedy"
)
```

--------------------------------

### Extract Module Subgraphs

Source: https://context7.com/bolinho9ue/ggnetview/llms.txt

Extracts individual module subgraphs from a modularized network using `get_subgraph`. Allows for visualization of individual modules and analysis of their specific topology and structural properties.

```r
# Extract all modules as separate subgraphs
subgraphs <- get_subgraph(graph_obj)

# Access individual modules
module_1 <- subgraphs[["1"]]
module_2 <- subgraphs[["2"]]

# Visualize individual modules
p_module1 <- ggNetView(
  graph_obj = module_1,
  layout = "circle",
  shape = 21,
  pointsize = c(3, 10),
  label = FALSE,
  seed = 1115
)

# Analyze module-specific topology
topology_module1 <- get_network_topology(module_1)

# Iterate through all modules for systematic analysis
module_stats <- lapply(names(subgraphs), function(mod_name) {
  subgraph <- subgraphs[[mod_name]]
  nodes <- tidygraph::as_tibble(tidygraph::activate(subgraph, nodes))
  edges <- tidygraph::as_tibble(tidygraph::activate(subgraph, edges))

  data.frame(
    module = mod_name,
    node_count = nrow(nodes),
    edge_count = nrow(edges),
    density = nrow(edges) / (nrow(nodes) * (nrow(nodes) - 1) / 2)
  )
})
module_summary <- do.call(rbind, module_stats)
```

--------------------------------

### Compute Network Topology Metrics

Source: https://context7.com/bolinho9ue/ggnetview/llms.txt

Calculates comprehensive network topology statistics including centrality measures and community structure using the `get_network_topology` function. The output can be used for filtering nodes based on importance.

```r
# Calculate network topology
topology <- get_network_topology(graph_obj)

# Access topology metrics
print(head(topology))
#   degree_vals avg_path   dia clust_global clust_local betweenness_vals
# 1          15   2.1543   5.0       0.4523      0.5234           123.45
# 2          12   2.1543   5.0       0.4523      0.4891            98.23
# 3          18   2.1543   5.0       0.4523      0.6012           156.78
#   closeness_vals eigen_vals fast_greedy_com_modularity
# 1        0.4652     0.2341                     0.5234
# 2        0.4523     0.1987                     0.5234
# 3        0.4789     0.2876                     0.5234

# Use topology for filtering important nodes
important_nodes <- topology[topology$betweenness_vals > quantile(topology$betweenness_vals, 0.75), ]
high_degree_nodes <- topology[topology$degree_vals > 10, ]
```

--------------------------------

### Access ggNetView Heatmap Plot Versions

Source: https://context7.com/bolinho9ue/ggnetview/llms.txt

Demonstrates how to access individual plot components from the list of heatmap plots generated by gglink_heatmaps. 'plot_straight_lines' and 'plot_curved_lines' are extracted for further manipulation or display.

```r
plot_straight_lines <- heatmap_plots[[1]]
plot_curved_lines <- heatmap_plots[[2]]
```

--------------------------------

### Define Environmental and Species Groups for ggNetView

Source: https://context7.com/bolinho9ue/ggnetview/llms.txt

Defines lists for environmental variable groups (Env01-Env04) and species groups (Core_Species) used for subsetting data in ggNetView visualizations. These lists specify the indices for selecting relevant variables and species.

```r
env_groups <- list(
  Env01 = 1:5,
  Env02 = 6:10,
  Env03 = 11:15,
  Env04 = 16:20
)

spec_groups <- list(
  Core_Species = 1:8
)
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.