# 加载必要的包library(ggplot2)library(vegan)library(cowplot)library(dplyr)
# --------------------------# (1) 模拟数据生成（控制变异性）# --------------------------
# 1.1 折线图数据（减小随机波动范围）set.seed(123)n_replicates <- 3  # 每个组合的重复数df_line <- expand.grid(  Day = c(0, 5, 10, 15, 25, 30),  Treatment = LETTERS[1:5],  Replicate = 1:n_replicates)
# 为每个处理-时间点生成更集中的数据（减小标准差）for (i in 1:nrow(df_line)) {  day <- df_line$Day[i]  treatment <- df_line$Treatment[i]  base_value <- 15 + day * 0.2  # 基础值随天数轻微增加  df_line$Part.C[i] <- rnorm(1, mean = base_value, sd = 1.5)  # 标准差从3减小到1.5}
# 计算均值和标准差df_summary <- df_line %>%  group_by(Day, Treatment) %>%  summarise(    Mean = mean(Part.C),    SD = sd(Part.C),    .groups = "drop"  )
# 1.2 PCoA 数据（保持不变）set.seed(456)n_samples <- 30n_otus <- 10otu_table <- matrix(rpois(n_samples * n_otus, lambda = 10), nrow = n_samples, ncol = n_otus)rownames(otu_table) <- paste0("Sample", 1:n_samples)metadata <- data.frame(  Sample = rownames(otu_table),  Treatment = rep(LETTERS[1:5], each = 6),  Day = rep(c(0, 5, 10, 15, 25, 30), times = 5))
# --------------------------# (2) 绘制折线图（误差棒缩小）# --------------------------p_line <- ggplot(df_summary, aes(x = Day, y = Mean, color = Treatment, group = Treatment)) +  geom_line(linewidth = 1) +  geom_point(size = 4,shape=18) +  geom_errorbar(    aes(ymin = Mean - SD, ymax = Mean + SD),    width = 0,    linewidth = 0.8  ) +  scale_color_manual(values = c("#FFC95E", "#FC7E86", "#507564", "#73C5DC", "#000000")) +  labs(    x = "Day",    y = "Concentration (Part.C)",    title = ""  ) +  theme_bw()+theme(panel.grid.major=element_blank(),panel.grid.minor=element_blank())+  theme(axis.text=element_text(colour='black',size=9))+  theme(    legend.position = "NA",    plot.title = element_text(face = "bold", hjust = 0)  )
# --------------------------# (3) PCoA 分析及绘图（图B，保持不变）# --------------------------dist_matrix <- vegdist(otu_table, method = "bray")pcoa_result <- cmdscale(dist_matrix, k = 2, eig = TRUE)pcoa_scores <- as.data.frame(pcoa_result$points)colnames(pcoa_scores) <- c("PCo1", "PCo2")pcoa_scores$Treatment <- metadata$Treatmentpcoa_scores$Day <- metadata$Dayvariance <- round(pcoa_result$eig / sum(pcoa_result$eig) * 100, 1)
p_pcoa <- ggplot(pcoa_scores, aes(x = PCo1, y = PCo2, color = Treatment, shape = factor(Day))) +  geom_point(size = 4, shape=24, alpha = 1) +  scale_color_manual(values = c("#FFC95E", "#FC7E86", "#507564", "#73C5DC", "#000000")) +  scale_shape_manual(values = c(16, 17, 15, 18, 8, 3)) +  labs(    x = paste0("PCo1 (", variance[1], "%)"),    y = paste0("PCo2 (", variance[2], "%)"),    title = "",    shape = "Day"  ) +  theme_bw()+theme(panel.grid.major=element_blank(),panel.grid.minor=element_blank())+  theme(axis.text=element_text(colour='black',size=9))+  theme(    legend.position = "right",    plot.title = element_text(face = "bold", hjust = 0)  )
# --------------------------# (4) 组合图形# --------------------------library(cowplot)cowplot::plot_grid(p_line, p_pcoa ,ncol= 2, rel_widths = c(1, 1.4))