# Comprehensive Guide to evanverse

# 📖 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)
    #> # A tibble: 3 × 4
    #>   package name    installed source
    #>   <chr>   <chr>   <lgl>     <chr>
    #> 1 dplyr   dplyr   TRUE      CRAN
    #> 2 ggplot2 ggplot2 TRUE      CRAN
    #> 3 tidyr   tidyr   TRUE      CRAN

    # Get package version (skip on CRAN due to network dependency)
    if (!identical(Sys.getenv("NOT_CRAN"), "false")) {
      try(pkg_version("evanverse"), silent = TRUE)
    }
    #>     package version latest source
    #> 1 evanverse   0.4.0  0.3.7   CRAN

## 🎨 Color Palette System

### Available Palettes

    # List all available palettes
    palettes_info <- list_palettes()
    print(palettes_info)
    #>                    name        type n_color
    #> 12         div_contrast   diverging       2
    #> 14          div_fireice   diverging       2
    #> 16            div_polar   diverging       2
    #> 18           div_sunset   diverging       2
    #> 15     div_pinkgreen_rb   diverging       3
    #> 13           div_earthy   diverging       5
    #> 17             div_sage   diverging       7
    #> 29          qual_earthy qualitative       3
    #> 70         qual_primary qualitative       3
    #> 77        qual_softtrio qualitative       3
    #> 90         qual_vintage qualitative       3
    #> 21        qual_balanced qualitative       4
    #> 30       qual_egypt_met qualitative       4
    #> 46   qual_kandinsky_met qualitative       4
    #> 34       qual_greek_met qualitative       5
    #> 40    qual_isfahan2_met qualitative       5
    #> 42        qual_java_met qualitative       5
    #> 44     qual_johnson_met qualitative       5
    #> 56      qual_navajo_met qualitative       5
    #> 58  qual_newkingdom_met qualitative       5
    #> 79        qual_tara_met qualitative       5
    #> 89         qual_vibrant qualitative       5
    #> 91          qual_violin qualitative       5
    #> 93     qual_wissing_met qualitative       5
    #> 33     qual_gauguin_met qualitative       6
    #> 35         qual_harmony qualitative       6
    #> 38      qual_homer2_met qualitative       6
    #> 45      qual_juarez_met qualitative       6
    #> 47       qual_klimt_met qualitative       6
    #> 48      qual_lakota_met qualitative       6
    #> 64          qual_pastel qualitative       6
    #> 66       qual_peru1_met qualitative       6
    #> 67       qual_peru2_met qualitative       6
    #> 68   qual_pillement_met qualitative       6
    #> 19 qual_archambault_met qualitative       7
    #> 20     qual_austria_met qualitative       7
    #> 26       qual_degas_met qualitative       7
    #> 28      qual_derain_met qualitative       7
    #> 36    qual_hokusai1_met qualitative       7
    #> 41          qual_jama_g qualitative       7
    #> 53      qual_moreau_met qualitative       7
    #> 55     qual_nattier_met qualitative       7
    #> 62    qual_okeeffe2_met qualitative       7
    #> 69     qual_pissaro_met qualitative       7
    #> 82          qual_tron_g qualitative       7
    #> 84   qual_tsimshian_met qualitative       7
    #> 85    qual_vangogh1_met qualitative       7
    #> 88    qual_veronese_met qualitative       7
    #> 22    qual_cassatt1_met qualitative       8
    #> 37      qual_homer1_met qualitative       8
    #> 39      qual_ingres_met qualitative       8
    #> 54 qual_morgenstern_met qualitative       8
    #> 57          qual_nejm_g qualitative       8
    #> 59      qual_nizami_met qualitative       8
    #> 74         qual_set2_rb qualitative       8
    #> 78         qual_tam_met qualitative       8
    #> 80      qual_thomas_met qualitative       8
    #> 81     qual_tiepolo_met qualitative       8
    #> 83        qual_troy_met qualitative       8
    #> 86    qual_vangogh2_met qualitative       8
    #> 87    qual_vangogh3_met qualitative       8
    #> 25       qual_cross_met qualitative       9
    #> 49        qual_lancet_g qualitative       9
    #> 52       qual_monet_met qualitative       9
    #> 73         qual_set1_rb qualitative       9
    #> 92           qual_vivid qualitative       9
    #> 23    qual_cassatt2_met qualitative      10
    #> 24        qual_cosmic_g qualitative      10
    #> 27      qual_demuth_met qualitative      10
    #> 31        qual_flatui_g qualitative      10
    #> 43           qual_jco_g qualitative      10
    #> 51        qual_mobility qualitative      10
    #> 60           qual_npg_g qualitative      10
    #> 50       qual_manet_met qualitative      11
    #> 61    qual_okeeffe1_met qualitative      11
    #> 63      qual_paquin_met qualitative      11
    #> 32      qual_futurama_g qualitative      12
    #> 71       qual_redon_met qualitative      12
    #> 72      qual_renoir_met qualitative      12
    #> 75         qual_set3_rb qualitative      12
    #> 76      qual_signac_met qualitative      14
    #> 65         qual_pbmc_sc qualitative      17
    #> 2             seq_blues  sequential       3
    #> 3             seq_blush  sequential       4
    #> 4            seq_forest  sequential       4
    #> 11            seq_muted  sequential       4
    #> 6          seq_hokusai2  sequential       6
    #> 7          seq_hokusai3  sequential       6
    #> 9         seq_locuszoom  sequential       7
    #> 8           seq_isfahan  sequential       8
    #> 10         seq_mobility  sequential       9
    #> 5         seq_hiroshige  sequential      10
    #> 1        seq_benedictus  sequential      13
    #>                                                                                                                                                     colors
    #> 12                                                                                                                                        #C64328, #56BBA5
    #> 14                                                                                                                                        #2AA6C6, #C64328
    #> 16                                                                                                                                        #8CB5D2, #E18E8F
    #> 18                                                                                                                                        #57A2FF, #FF8000
    #> 15                                                                                                                         #E64B35B2, #00A087B2, #3C5488B2
    #> 13                                                                                                             #283618, #606C38, #FEFAE0, #DDA15E, #BC6C25
    #> 17                                                                                           #EDEAE7, #B1CABA, #BBCDD7, #BBAAB6, #6D8092, #504B54, #0E0F0F
    #> 29                                                                                                                               #C64328, #56BBA5, #E3A727
    #> 70                                                                                                                               #C64328, #2AA6C6, #E3A727
    #> 77                                                                                                                         #E64B35B2, #00A087B2, #3C5488B2
    #> 90                                                                                                                               #96A0D9, #D9BDAD, #D9D5A0
    #> 21                                                                                                                      #5D83B4, #9FD0E8, #CDAE9D, #959683
    #> 30                                                                                                                      #dd5129, #0f7ba2, #43b284, #fab255
    #> 46                                                                                                                      #3b7c70, #ce9642, #898e9f, #3b3a3e
    #> 34                                                                                                             #3c0d03, #8d1c06, #e67424, #ed9b49, #f5c34d
    #> 40                                                                                                             #d7aca1, #ddc000, #79ad41, #34b6c6, #4063a3
    #> 42                                                                                                             #663171, #cf3a36, #ea7428, #e2998a, #0c7156
    #> 44                                                                                                             #a00e00, #d04e00, #f6c200, #0086a8, #132b69
    #> 56                                                                                                             #660d20, #e59a52, #edce79, #094568, #e1c59a
    #> 58                                                                                                             #e1846c, #9eb4e0, #e6bb9e, #9c6849, #735852
    #> 79                                                                                                             #eab1c6, #d35e17, #e18a1f, #e9b109, #829d44
    #> 89                                                                                                             #BF3F9D, #B3BCD7, #6DA6A0, #D98A29, #F2C894
    #> 91                                                                                                             #37848C, #F2935C, #F2A88D, #D95555, #A7CAE9
    #> 93                                                                                                             #4b1d0d, #7c291e, #ba7233, #3a4421, #2d5380
    #> 33                                                                                                    #b04948, #811e18, #9e4013, #c88a2c, #4c6216, #1a472a
    #> 35                                                                                                    #BF3641, #836AA6, #377BA6, #448C42, #D96236, #B79290
    #> 38                                                                                                    #bf3626, #e9851d, #f9c53b, #aeac4c, #788f33, #165d43
    #> 45                                                                                                    #a82203, #208cc0, #f1af3a, #cf5e4e, #637b31, #003967
    #> 47                                                                                                    #df9ed4, #c93f55, #eacc62, #469d76, #3c4b99, #924099
    #> 48                                                                                                    #04a3bd, #f0be3d, #931e18, #da7901, #247d3f, #20235b
    #> 64                                                                                                    #B2AA76, #8C91CF, #D7D79C, #DABFAC, #BCEDDB, #C380A0
    #> 66                                                                                                    #b5361c, #e35e28, #1c9d7c, #31c7ba, #369cc9, #3a507f
    #> 67                                                                                                    #65150b, #961f1f, #c0431f, #f19425, #c59349, #533d14
    #> 68                                                                                                    #a9845b, #697852, #738e8e, #44636f, #2b4655, #0f252f
    #> 19                                                                                           #88a0dc, #381a61, #7c4b73, #ed968c, #ab3329, #e78429, #f9d14a
    #> 20                                                                                           #a40000, #16317d, #007e2f, #ffcd12, #b86092, #721b3e, #00b7a7
    #> 26                                                                                           #591d06, #96410e, #e5a335, #556219, #418979, #2b614e, #053c29
    #> 28                                                                                           #efc86e, #97c684, #6f9969, #aab5d5, #808fe1, #5c66a8, #454a74
    #> 36                                                                                           #6d2f20, #b75347, #df7e66, #e09351, #edc775, #94b594, #224b5e
    #> 41                                                                                           #374E55, #DF8F44, #00A1D5, #B24745, #79AF97, #6A6599, #80796B
    #> 53                                                                                           #421600, #792504, #bc7524, #8dadca, #527baa, #104839, #082844
    #> 55                                                                                           #52271c, #944839, #c08e39, #7f793c, #565c33, #184948, #022a2a
    #> 62                                                                                           #fbe3c2, #f2c88f, #ecb27d, #e69c6b, #d37750, #b9563f, #92351e
    #> 69                                                                                           #134130, #4c825d, #8cae9e, #8dc7dc, #508ca7, #1a5270, #0e2a4d
    #> 82                                                                                           #FF410D, #6EE2FF, #F7C530, #95CC5E, #D0DFE6, #F79D1E, #748AA6
    #> 84                                                                                           #582310, #aa361d, #82c45f, #318f49, #0cb4bb, #2673a3, #473d7d
    #> 85                                                                                           #2c2d54, #434475, #6b6ca3, #969bc7, #87bcbd, #89ab7c, #6f9954
    #> 88                                                                                           #67322e, #99610a, #c38f16, #6e948c, #2c6b67, #175449, #122c43
    #> 22                                                                                  #b1615c, #d88782, #e3aba7, #edd7d9, #c9c9dd, #9d9dc7, #8282aa, #5a5a83
    #> 37                                                                                  #551f00, #a62f00, #df7700, #f5b642, #fff179, #c3f4f6, #6ad5e8, #32b2da
    #> 39                                                                                  #041d2c, #06314e, #18527e, #2e77ab, #d1b252, #a97f2f, #7e5522, #472c0b
    #> 54                                                                                  #98768e, #b08ba5, #c7a2b6, #dfbbc8, #ffc680, #ffb178, #db8872, #a56457
    #> 57                                                                                  #BC3C29, #0072B5, #E18727, #20854E, #7876B1, #6F99AD, #FFDC91, #EE4C97
    #> 59                                                                                  #dd7867, #b83326, #c8570d, #edb144, #8cc8bc, #7da7ea, #5773c0, #1d4497
    #> 74                                                                                  #66C2A5, #FC8D62, #8DA0CB, #E78AC3, #A6D854, #FFD92F, #E5C494, #B3B3B3
    #> 78                                                                                  #ffd353, #ffb242, #ef8737, #de4f33, #bb292c, #9f2d55, #62205f, #341648
    #> 80                                                                                  #b24422, #c44d76, #4457a5, #13315f, #b1a1cc, #59386c, #447861, #7caf5c
    #> 81                                                                                  #802417, #c06636, #ce9344, #e8b960, #646e3b, #2b5851, #508ea2, #17486f
    #> 83                                                                                  #421401, #6c1d0e, #8b3a2b, #c27668, #7ba0b4, #44728c, #235070, #0a2d46
    #> 86                                                                                  #bd3106, #d9700e, #e9a00e, #eebe04, #5b7314, #c3d6ce, #89a6bb, #454b87
    #> 87                                                                                  #e7e5cc, #c2d6a4, #9cc184, #669d62, #3c7c3d, #1f5b25, #1e3d14, #192813
    #> 25                                                                         #c969a1, #ce4441, #ee8577, #eb7926, #ffbb44, #859b6c, #62929a, #004f63, #122451
    #> 49                                                                         #00468B, #ED0000, #42B540, #0099B4, #925E9F, #FDAF91, #AD002A, #ADB6B6, #1B1919
    #> 52                                                                         #4e6d58, #749e89, #abccbe, #e3cacf, #c399a2, #9f6e71, #41507b, #7d87b2, #c2cae3
    #> 73                                                                         #E41A1C, #377EB8, #4DAF4A, #984EA3, #FF7F00, #FFFF33, #A65628, #F781BF, #999999
    #> 92                                                                         #E64B35, #4DBBD5, #00A087, #3C5488, #F39B7F, #8491B4, #91D1C2, #DC0000, #7E6148
    #> 23                                                                #2d223c, #574571, #90719f, #b695bc, #dec5da, #c1d1aa, #7fa074, #466c4b, #2c4b27, #0e2810
    #> 24                                                                #2E2A2B, #CF4E9C, #8C57A2, #358DB9, #82581F, #2F509E, #E5614C, #97A1A7, #3DA873, #DC9445
    #> 27                                                                #591c19, #9b332b, #b64f32, #d39a2d, #f7c267, #b9b9b8, #8b8b99, #5d6174, #41485f, #262d42
    #> 31                                                                #c0392b, #d35400, #f39c12, #27ae60, #16a085, #2980b9, #8e44ad, #2c3e50, #7f8c8d, #bdc3c7
    #> 43                                                                #0073C2, #EFC000, #868686, #CD534C, #7AA6DC, #003C67, #8F7700, #3B3B3B, #A73030, #4A6990
    #> 51                                                                #f7fbff, #deebf7, #c6dbef, #9ecae1, #6baed6, #4292c6, #2171b5, #08519c, #08306b, #fdbf6f
    #> 60                                                                #E64B35, #4DBBD5, #00A087, #3C5488, #F39B7F, #8491B4, #91D1C2, #DC0000, #7E6148, #B09C85
    #> 50                                                       #3b2319, #80521c, #d29c44, #ebc174, #ede2cc, #7ec5f4, #4585b7, #225e92, #183571, #43429b, #5e65be
    #> 61                                                       #6b200c, #973d21, #da6c42, #ee956a, #fbc2a9, #f6f2ee, #bad6f9, #7db0ea, #447fdd, #225bb2, #133e7e
    #> 63                                                       #831818, #c62320, #f05b43, #f78462, #feac81, #f7dea3, #ced1af, #98ab76, #748f46, #47632a, #275024
    #> 32                                              #FF6F00, #C71000, #008EA0, #8A4198, #5A9599, #FF6348, #84D7E1, #FF95A8, #3D3B25, #ADE2D0, #1A5354, #3F4041
    #> 71                                              #5b859e, #1e395f, #75884b, #1e5a46, #df8d71, #af4f2f, #d48f90, #732f30, #ab84a5, #59385c, #d8b847, #b38711
    #> 72                                              #17154f, #2f357c, #6c5d9e, #9d9cd5, #b0799a, #f6b3b0, #e48171, #bf3729, #e69b00, #f5bb50, #ada43b, #355828
    #> 75                                              #8DD3C7, #FFFFB3, #BEBADA, #FB8072, #80B1D3, #FDB462, #B3DE69, #FCCDE5, #D9D9D9, #BC80BD, #CCEBC5, #FFED6F
    #> 76                            #fbe183, #f4c40f, #fe9b00, #d8443c, #9b3441, #de597c, #e87b89, #e6a2a6, #aa7aa1, #9f5691, #633372, #1f6e9c, #2b9b81, #92c051
    #> 65 #a2d2e7, #67a8cd, #ffc17f, #cf9f88, #6fb3a8, #b3e19b, #50aa4b, #ff9d9f, #f36569, #3581b7, #cdb6da, #704ba3, #9a7fbd, #dba9a8, #e40300, #e99b78, #ff8831
    #> 2                                                                                                                                #deebf7, #9ecae1, #3182bd
    #> 3                                                                                                                       #FFCDB2, #FFB4A2, #E5989B, #B5828C
    #> 4                                                                                                                       #B2C9AD, #91AC8F, #66785F, #4B5945
    #> 11                                                                                                                      #E2E0C8, #A7B49E, #818C78, #5C7285
    #> 6                                                                                                     #abc9c8, #72aeb6, #4692b0, #2f70a1, #134b73, #0a3351
    #> 7                                                                                                     #d8d97a, #95c36e, #74c8c3, #5a97c1, #295384, #0a2e57
    #> 9                                                                                            #D43F3A, #EEA236, #5CB85C, #46B8DA, #357EBD, #9632B8, #B8B8B8
    #> 8                                                                                   #4e3910, #845d29, #ae8548, #e3c28b, #4fb6ca, #178f92, #175f5d, #054544
    #> 10                                                                         #f7fbff, #deebf7, #c6dbef, #9ecae1, #6baed6, #4292c6, #2171b5, #08519c, #08306b
    #> 5                                                                 #e76254, #ef8a47, #f7aa58, #ffd06f, #ffe6b7, #aadce0, #72bcd5, #528fad, #376795, #1e466e
    #> 1                                      #9a133d, #b93961, #d8527c, #f28aaa, #f9b4c9, #f9e0e8, #ffffff, #eaf3ff, #c5daf6, #a1c2ed, #6996e3, #4060c8, #1a318b

### 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")
    #> Vivid qualitative palette:
    print(vivid_colors)
    #> [1] "#E64B35" "#4DBBD5" "#00A087" "#3C5488" "#F39B7F" "#8491B4" "#91D1C2"
    #> [8] "#DC0000" "#7E6148"

    cat("\nBlues sequential palette:\n")
    #>
    #> Blues sequential palette:
    print(blues_gradient)
    #> [1] "#deebf7" "#9ecae1" "#3182bd"

### 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:")
    #> [1] "Custom palette colors:"
    print(custom_colors)
    #> [1] "#FF6B6B" "#4ECDC4" "#45B7D1" "#96CEB4"
    cat("This would create a palette named 'custom_demo' with", length(custom_colors), "colors\n")
    #> This would create a palette named 'custom_demo' with 4 colors

## 📊 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"
    )

Venn diagram example

    print(venn_plot)

Venn diagram example

### 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)

Professional 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:")
    #> [1] "Original vector:"
    print(messy_vector)
    #> [1] "A" ""  "C" NA  "E"

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

    # Replace void values
    clean_vector <- replace_void(messy_vector, value = "MISSING")
    print("After replacing voids:")
    #> [1] "After replacing voids:"
    print(clean_vector)
    #> [1] "A"       "MISSING" "C"       "MISSING" "E"

### 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:")
    #> [1] "Cars grouped by cylinder, showing MPG values:"
    str(grouped_data)
    #> List of 3
    #>  $ 4: num [1:3] 22.8 24.4 22.8
    #>  $ 6: num [1:5] 21 21 21.4 18.1 19.2
    #>  $ 8: num [1:2] 18.7 14.3

## ⚡ 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)
    #> [1]  TRUE  TRUE FALSE FALSE FALSE

    # Paste operator
    result <- "Hello" %p% " " %p% "World"
    print(result)
    #> [1] "Hello   World"

    # Check identity
    print(5 %is% 5)
    #> [1] TRUE

## 💾 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)
    #> function (...)
    #> {
    #>     cli::cli_alert_info("{name} started at {format(Sys.time(), '%Y-%m-%d %H:%M:%S')}")
    #>     tictoc::tic()
    #>     result <- fn(...)
    #>     timing <- tictoc::toc(quiet = TRUE)
    #>     elapsed <- as.numeric(timing$toc - timing$tic, units = "secs")
    #>     cli::cli_alert_success("{name} completed in {sprintf('%.3f', elapsed)} seconds")
    #>     invisible(result)
    #> }
    #> <bytecode: 0x0000020de075f008>
    #> <environment: 0x0000020de075fd98>

### Safe Execution

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

    print(safe_result)
    #> [1] 5.5

## 📈 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

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