library(ggplot2)
library(dplyr)
library(reshape2)

# ===================== 数据 =====================
eggnog <- data.frame(
  Category = c("A","B","C","D","E","F","G","H","I","J","K","L","M","N","O","P","Q","R","S","T","U","V","W","Y","Z"),
  
  disease = c(1331.92,583.42,59792.40,12647.23,72533.57,17147.12,
              58640.01,31475.03,36986.05,37400.36,58142.47,67299.68,
              55828.29,9119.68,32528.88,45813.37,34376.89,0,
              146233.98,50492.20,17621.21,17423.12,279.45,28.24,766.37),
  
  healthy = c(1979.61,1166.96,61561.81,11042.69,72943.92,17882.83,
              57833.78,30585.17,38720.49,42165.03,52066.30,51727.48,
              53251.32,9383.54,33073.21,43215.94,31527.85,0,
              123054.47,42624.56,18888.27,14535.32,263.66,182.17,1731.78)
)

# ===================== Y轴标签：字母 + 功能名称 =====================
eggnog$Label <- c(
  "A: RNA processing and modification",
  "B: Chromatin structure and dynamics",
  "C: Energy production and conversion",
  "D: Cell cycle control, cell division",
  "E: Amino acid transport and metabolism",
  "F: Nucleotide transport and metabolism",
  "G: Carbohydrate transport and metabolism",
  "H: Coenzyme transport and metabolism",
  "I: Lipid transport and metabolism",
  "J: Translation, ribosomal structure",
  "K: Transcription",
  "L: Replication, recombination and repair",
  "M: Cell wall/membrane/envelope biogenesis",
  "N: Cell motility",
  "O: Posttranslational modification, protein turnover",
  "P: Inorganic ion transport and metabolism",
  "Q: Secondary metabolites biosynthesis",
  "R: General function prediction only",
  "S: Function unknown",
  "T: Signal transduction mechanisms",
  "U: Intracellular trafficking, secretion",
  "V: Defense mechanisms",
  "W: Extracellular structures",
  "Y: Nuclear structure",
  "Z: Cytoskeleton"
)

# ===================== 4大功能分类 =====================
eggnog$Class <- case_when(
  eggnog$Category %in% c("D","M","N","O","T","U","V","W","Y","Z") ~ "Cellular processes and signaling",
  eggnog$Category %in% c("A","B","J","K","L") ~ "Information storage and processing",
  eggnog$Category %in% c("C","E","F","G","H","I","P","Q") ~ "Metabolism",
  TRUE ~ "Poorly characterized"
)

# 长数据格式
df <- melt(eggnog, id.vars = c("Category","Label","Class"),
           variable.name = "Group", value.name = "Abundance")

# 固定分类顺序
df$Class <- factor(df$Class, levels = c(
  "Cellular processes and signaling",
  "Information storage and processing",
  "Metabolism",
  "Poorly characterized"
))

# ===================== 最终绘图（无报错版） =====================
ggplot(df, aes(x = Abundance, y = Label, fill = Group)) +
  geom_col(
    position = position_dodge(width = 0.7),
    width = 0.5,        # 全局统一柱子粗细
    color = NA     # 柱子黑边
  ) +
  
  scale_fill_manual(values = c("disease" = "#F2B880", "healthy" = "#BBDEFB")) +
  
  # 分面：每个分类一个独立框，高度自适应
  facet_wrap(~Class, ncol = 1, scales = "free_y") +
  
  labs(x = "Number of Genes", y = "COG Functional Category") +
  
  # 主题：干净边框 + 无网格
  theme_bw() +
  theme(
    panel.grid = element_blank(),
    panel.border = element_rect(linewidth = 1),
    strip.background = element_rect(fill = "gray90"),
    strip.text = element_text(face = "bold", size = 11),
    axis.text.y = element_text(size = 9),
    legend.position = "bottom",
    legend.title = element_blank()
  )