Comprehensive Guide to evanverse"

In evanverse: Utility Functions for Data Analysis and Visualization

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 5,
  fig.align = "center",
  warning = FALSE,
  message = FALSE
)

📖 Comprehensive Guide to evanverse

Welcome to the comprehensive guide for evanverse - a feature-rich R utility package providing 55+ functions for data analysis, visualization, and bioinformatics workflows.

🚀 Package Installation and Setup

# Install from CRAN
install.packages("evanverse")

# Or install development version from GitHub
evanverse::inst_pkg("evanbio/evanverse")

library(evanverse)

📦 Package Management

The evanverse package provides robust package management utilities:

# Check if packages are installed
required_packages <- c("dplyr", "ggplot2", "tidyr")
check_pkg(required_packages)

# Get package version (skip on CRAN due to network dependency)
if (!identical(Sys.getenv("NOT_CRAN"), "false")) {
  try(pkg_version("evanverse"), silent = TRUE)
}

🎨 Color Palette System

Available Palettes

# List all available palettes
palettes_info <- list_palettes()
print(palettes_info)

Using Color Palettes

# Get specific palettes
vivid_colors <- get_palette("qual_vivid", type = "qualitative")
blues_gradient <- get_palette("seq_blues", type = "sequential")

cat("Vivid qualitative palette:\n")
print(vivid_colors)

cat("\nBlues sequential palette:\n")
print(blues_gradient)

Creating Custom Palettes

# Create a custom palette (demonstration only - not executed to avoid file creation)
custom_colors <- c("#FF6B6B", "#4ECDC4", "#45B7D1", "#96CEB4")

# Example of how to create a custom palette (using temp directory):
# create_palette(
#   name = "custom_demo",
#   colors = custom_colors,
#   type = "qualitative",
#   color_dir = tempdir()  # Use temporary directory to avoid cluttering package
# )

# Preview the custom colors
print("Custom palette colors:")
print(custom_colors)
cat("This would create a palette named 'custom_demo' with", length(custom_colors), "colors\n")

📊 Visualization Functions

Venn Diagrams

# Create sample data for Venn diagram
set1 <- c("A", "B", "C", "D", "E")
set2 <- c("C", "D", "E", "F", "G")
set3 <- c("E", "F", "G", "H", "I")

# Create Venn diagram
venn_plot <- plot_venn(
  set1 = set1,
  set2 = set2,
  set3 = set3,
  category.names = c("Set1", "Set2", "Set3"),
  title = "Three-way Venn Diagram Example"
)
print(venn_plot)

Bar Plots

# Sample data
sample_data <- data.frame(
  Category = c("Type A", "Type B", "Type C"),
  Count = c(25, 18, 12),
  Group = c("High", "High", "Medium")
)

# Create bar plot with custom colors
vivid_colors <- get_palette("qual_vivid", type = "qualitative")
bar_plot <- plot_bar(data = sample_data,
                     x = "Category",
                     y = "Count",
                     fill = "Group") +
  ggplot2::scale_fill_manual(values = vivid_colors) +
  ggplot2::labs(title = "Sample Distribution by Category",
                x = "Sample Type",
                y = "Count")
print(bar_plot)

🧬 Bioinformatics Utilities

Gene ID Conversion

# Convert gene symbols to Ensembl IDs
gene_symbols <- c("TP53", "BRCA1", "EGFR")
ensembl_ids <- convert_gene_id(
  ids = gene_symbols,
  from = "SYMBOL",
  to = "ENSEMBL",
  species = "human"
)
print(ensembl_ids)

GMT File Processing

# Convert GMT file to data frame
gmt_df <- gmt2df("path/to/geneset.gmt")
head(gmt_df)

# Convert GMT file to list
gmt_list <- gmt2list("path/to/geneset.gmt")
length(gmt_list)

🔄 Data Processing and Void Handling

Working with Void Values

# Create sample vector with void values
messy_vector <- c("A", "", "C", NA, "E")

print("Original vector:")
print(messy_vector)

# Check for void values
cat("\nAny void values:", any_void(messy_vector), "\n")

# Replace void values
clean_vector <- replace_void(messy_vector, value = "MISSING")
print("After replacing voids:")
print(clean_vector)

Data Transformation

# Convert data frame to grouped list by cylinder count
grouped_data <- df2list(
  data = mtcars[1:10, ],
  key_col = "cyl",
  value_col = "mpg"
)

print("Cars grouped by cylinder, showing MPG values:")
str(grouped_data)

⚡ Custom Operators

# Demonstrate custom operators
x <- c(1, 2, 3, 4, 5)
y <- c(3, 4, 5, 6, 7)

# Check what's NOT in another vector
print(x %nin% y)

# Paste operator
result <- "Hello" %p% " " %p% "World"
print(result)

# Check identity
print(5 %is% 5)

💾 File Operations

Flexible File Reading

# Read various file formats flexibly
data1 <- read_table_flex("data.csv")
data2 <- read_excel_flex("data.xlsx", sheet = 1)

# Get file information
file_info("data.csv")

# Display directory tree
file_tree(".")

🛠️ Development Tools

Timing and Execution

# Time execution of code
result <- with_timer(function() {
  Sys.sleep(0.01)  # Quick simulation
  sum(1:1000)
}, name = "Sum calculation")

print(result)

Safe Execution

# Execute code safely
safe_result <- safe_execute({
  x <- 1:10
  mean(x)
})

print(safe_result)

📈 Summary

The evanverse package provides a comprehensive toolkit for:

• Package Management: Multi-source installation and management
• Data Visualization: Publication-ready plots with sensible defaults
• Color Management: Professional palette system for consistent styling
• File Operations: Robust I/O with enhanced error handling
• Bioinformatics: Specialized tools for genomic data processing
• Data Processing: Advanced transformation and void value handling
• Custom Operators: Expressive syntax extensions for R
• Development Tools: Productivity enhancing utilities

With 55+ functions across 8 major categories, evanverse streamlines your data analysis workflow while maintaining flexibility and reliability.

🔗 Next Steps

• Explore the Color Palettes guide for advanced palette management
• Check out Bioinformatics Workflows for domain-specific applications
• Visit the Function Reference for detailed documentation

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For more information, visit the evanverse website or the GitHub repository.

evanverse documentation built on March 10, 2026, 5:07 p.m.