堆叠柱状图简介
堆叠柱状图，它通过堆叠的方式在单一柱子内展示多个类别的数据，以揭示每个类别在总体中的贡献或占比。堆叠柱状图将每个柱子进行分割，以显示相同类型下各个数据的大小情况。每一根柱子上的值分别代表不同的数据大小，而每一层又代表不同分类的数据，不同分类数据的总和代表整根柱子的高度。堆叠柱状图适用于比较每个分组的数据总量，以及每个分组内部各个小分类的数据大小和占比。它常用于销售数据的比较、市场份额分析、产品组合优化等场景。例如，可以使用堆叠柱状图来展示不同产品一年的销售额在每个月的对比，或者不同销售渠道的销售额对比。
标签：#微生物组数据分析 #MicrobiomeStatPlot #堆叠柱状图 #R语言可视化 #Stack bar plot
作者：First draft(初稿)：Defeng Bai(白德凤)；Proofreading(校对)：Ma Chuang(马闯) and Jiani Xun(荀佳妮)；Text tutorial(文字教程)：Defeng Bai(白德凤)
源代码及测试数据链接：
https://github.com/YongxinLiu/MicrobiomeStatPlot/项目中目录 3.Visualization_and_interpretation/StackBarPlot
或公众号后台回复“MicrobiomeStatPlot”领取
参考文献：https://www.nature.com/articles/s41591-023-02343-2
这篇文章是MRC毒理学研究所James Thaventhiran团队等人合作于2023年在Nature medicine在线发表题为“Accelerated waning of the humoral response to COVID-19 vaccines in obesity”的研究论文。
图 3 | 接种第三剂 COVID-19 疫苗后的免疫反应。
结果
为了进一步评估与重度肥胖相关的体液免疫，我们接下来使用已建立的高维光谱流式细胞仪面板对 SARS-CoV-2 RBD 结合型 B 细胞进行计数和表型（图3c-e和扩展数据图6a-c）。RBD结合（RBD+）细胞的无监督 t 分布随机邻接嵌入（tSNE）分析是在几个时间点对来自正常体重指数（BMI）个体和重度肥胖个体的所有 IgD- B 细胞组合进行的。簇 1 代表 IgM+ B 细胞；簇 2 显示了非典型或年龄相关 B 细胞的特征32；簇 3 分离了浆细胞；最小的簇（簇 4 和簇 5）是类开关记忆 B 细胞（图 3c-e）。
源代码及测试数据链接：
https://github.com/YongxinLiu/MicrobiomeStatPlot/
或公众号后台回复“MicrobiomeStatPlot”领取
软件包安装
# 基于CRAN安装R包，检测没有则安装 Installing R packages based on CRAN and installing them if they are not detectedp_list = c("ggplot2", "reshape2", "ggsci", "patchwork", "dplyr", "treeio","cowplot", "ggtree", "cols4all", "ggh4x", "ggprism", "tidyverse","tidyr", "plyr","scales","phytools","gridExtra","ggalluvial","readxl","alluvial")for(p in p_list){if (!requireNamespace(p)){install.packages(p)}library(p, character.only = TRUE, quietly = TRUE, warn.conflicts = FALSE)}# 加载R包 Loading R packagessuppressWarnings(suppressMessages(library(ggplot2)))suppressWarnings(suppressMessages(library(reshape2)))suppressWarnings(suppressMessages(library(ggsci)))suppressWarnings(suppressMessages(library(patchwork)))suppressWarnings(suppressMessages(library(dplyr)))suppressWarnings(suppressMessages(library(treeio)))suppressWarnings(suppressMessages(library(cowplot)))suppressWarnings(suppressMessages(library(ggtree)))suppressWarnings(suppressMessages(library(cols4all)))suppressWarnings(suppressMessages(library(ggh4x)))suppressWarnings(suppressMessages(library(ggprism)))suppressWarnings(suppressMessages(library(tidyverse)))suppressWarnings(suppressMessages(library(tidyr)))suppressWarnings(suppressMessages(library(plyr)))suppressWarnings(suppressMessages(library(phytools)))suppressWarnings(suppressMessages(library(scales)))suppressWarnings(suppressMessages(library(gridExtra)))suppressWarnings(suppressMessages(library(dplyr)))suppressWarnings(suppressMessages(library(readxl)))suppressWarnings(suppressMessages(library(ggalluvial)))suppressWarnings(suppressMessages(library(alluvial)))
实战1 带误差线堆叠柱状图
# 载入数据# Load datadf1 <- read.table(file = "data/data_practice1.txt", sep = "\t", header = TRUE, check.names = FALSE)# 误差线位置# Error bar positiondf1 <- df1 %>%group_by(group) %>%dplyr::mutate(xx=cumsum(value))# 保证误差线可以对应其正确位置# Ensure that the error bars correspond to their correct positionsdf1$variable <- factor(df1$variable,levels = c("value2","value1"))# 转换为因子，指定绘图顺序# Convert to factors and specify drawing orderdf1$group <- factor(df1$group, levels = unique(df1$group))# 配色方案# Set coloroptimized_colors_grouped <- c("#1b9e77", "#d95f02")optimized_colors_stacked <- c("#66c2a5", "#fc8d62")# 优化后的分组柱状图绘制# Plotp1_optimized <- ggplot(data = df1, aes(x = group, y = value, fill = variable)) +geom_bar(position = "dodge", stat = "identity", color = NA, width = 0.75) +scale_fill_manual(values = optimized_colors_grouped) +theme_minimal(base_size = 16) +theme(legend.position = "top",axis.text.x = element_text(angle = 45, hjust = 1),axis.title.y = element_text(size = 14),legend.title = element_blank()) +labs(y = "Value", x = "")#p1_optimized# 优化后的堆叠柱状图绘制# Plotp2_optimized <- ggplot(data = df1, aes(x = group, y = value, fill = variable)) +geom_bar(position = "stack", stat = "identity", color = NA, width = 0.75) +scale_fill_manual(values = optimized_colors_stacked) +#theme_minimal(base_size = 16) +theme_classic()+theme(legend.position = "top",axis.text.x = element_text(angle = 45, hjust = 1),axis.title.y = element_text(size = 14),legend.title = element_blank()) +labs(y = "Value", x = "")ggsave(filename = "results/Stacked_bar_plot.pdf", plot = p2_optimized, width = 10, height = 7)#p2_optimized# 为分组柱状图添加误差棒# Add error barp3_optimized <- ggplot(df1, aes(x = group, y = value, fill = variable)) +geom_bar(position = position_dodge(), stat = "identity", color = NA, width = 0.75) +scale_fill_manual(values = optimized_colors_grouped) +#theme_minimal(base_size = 16) +theme_classic()+theme(legend.position = "top",axis.text.x = element_text(angle = 45, hjust = 1),axis.title.y = element_text(size = 14),legend.title = element_blank()) +geom_errorbar(aes(ymin = value - sd, ymax = value + sd),position = position_dodge(width = 0.75), width = 0.3) +labs(y = "Value", x = "")#p3_optimized# 为堆叠柱状图添加误差棒# Add error bardf1 <- df1 %>%group_by(group) %>%dplyr::mutate(position = cumsum(value))p4_optimized <- ggplot(df1, aes(x = group, y = value, fill = variable)) +geom_bar(position = "stack", stat = "identity", color = NA, width = 0.75) +scale_fill_manual(values = optimized_colors_stacked) +#theme_minimal(base_size = 16) +theme_classic()+theme(legend.position = "top",axis.text.x = element_text(angle = 45, hjust = 1),axis.title.y = element_text(size = 14),legend.title = element_blank()) +geom_errorbar(aes(ymin = position - sd, ymax = position + sd),width = 0.3) +labs(y = "Value", x = "")ggsave(filename = "results/Stacked_bar_with_error.pdf", plot = p4_optimized, width = 10, height = 7)#p4_optimized# 组合图# Combinedlibrary(patchwork)p5_optimized <- p2_optimized + p4_optimized#p5_optimized
实战2 带聚类树的堆叠柱状图
参考：https://mp.weixin.qq.com/s/FK5ZOtsfx7clVOSKLR23FQ
# 导入OTU文件# Import OTU fileotu <- read.table('data/phylum_top10.txt', header = T)# 计算Bray-Curtis距离，均值聚类，生成树形图# Calculate Bray-Curtis distance, mean clustering, generate tree diagramstree <- hclust(vegan::vegdist(t(otu), method = 'bray'), method = 'average') %>%as.phylo()# 对树形图节点进行分组# Grouping of tree diagram nodestree <- groupClade(tree, .node = c(16))# 绘制树形聚类图# Plotting tree clustersp1 <- ggtree(tree, aes(color = group, linetype = group)) +geom_tiplab(aes(color = group), size = 3) +scale_color_manual(values = c("#E69F00", "#56B4E9")) +theme_minimal() +theme(legend.position = "none",axis.line = element_blank(),axis.text = element_blank(),axis.ticks = element_blank())# 计算每个分类的均值并按均值排序# Calculate the mean for each classification and sort by meanotu$mean <- apply(otu, 1, mean)otu <- otu[order(otu$mean), ]otu$phylum <- factor(rownames(otu), levels = rownames(otu))otu <- otu[, -c(ncol(otu)-1)]# 检查OTU数据中的分类数量# Check the number of classifications in the OTU datanum_phyla <- length(unique(otu$phylum))# 绘制物种组成柱状图# Histograms of species compositionp2 <- otu %>%melt(id.vars = 'phylum') %>%ggplot(aes(variable, value, fill = phylum)) +geom_bar(stat = 'identity', position = 'fill', color = 'black', size = 0.3) + # 添加边框scale_x_discrete(limits = c('t1', 't2', 't5', 't4', 't3', 't6', 'c1', 'c2', 'c6', 'c4', 'c3', 'c5')) +#scale_fill_igv()+scale_fill_manual(values = c("#d2da93","#5196d5","#00ceff","#ff630d","#35978b","#e5acd7","#77aecd","#ec8181","#dfc6a5","#e50719","#d27e43","#8a4984","#fe5094","#8d342e","#f94e54","#ffad00","#36999d","#00fc8d","#b64aa0","#9b82e1"))+scale_color_manual(values = c("#d2da93","#5196d5","#00ceff","#ff630d","#35978b","#e5acd7","#77aecd","#ec8181","#dfc6a5","#e50719","#d27e43","#8a4984","#fe5094","#8d342e","#f94e54","#ffad00","#36999d","#00fc8d","#b64aa0","#9b82e1"))+scale_y_continuous(expand = c(0, 0), labels = percent) +coord_flip() +theme_minimal() +theme(axis.ticks.y = element_blank(),axis.title.y = element_blank(),axis.text.y = element_blank(),axis.line = element_blank()) +labs(y = 'Percentage')# 拼接两张图，调整布局# Splicing two diagrams and adjusting the layoutp3 <- ggdraw() +draw_plot(p1, 0, 0.05, 0.45, 0.95) +draw_plot(p2, 0.45, 0, 0.55, 1)# 保存图表为PDF文件 Save as pdfggsave(filename = "results/Stacked_bar_with_tree.pdf", plot = p3, width = 12, height = 8)
实战3 堆积柱状图
参考：https://mp.weixin.qq.com/s/JYt0QW1oR8vvORPmkyFHtw
# 载入数据# Load datadf <- read.table(file = "data/data_practice3.txt", sep = "\t", header = TRUE, check.names = FALSE)# 基本堆叠柱状图# Basic stack bar plotp31 <- ggplot(df, aes(x = time, y = values, fill = genes)) +geom_col(width = 0.45, position = position_stack(vjust = 1)) +scale_fill_manual(values = c("#d36e70", "#64b7d4", "#f9a648", "#72c8b4", "#f05a2c", "#4f81bd")) +scale_y_continuous(expand = c(0, 0), limits = c(0, 100)) + # 调整y轴比例scale_x_discrete(expand = c(0.1, 0.1)) +geom_text(aes(label = values), position = position_stack(vjust = 0.5), colour = "white", size = 3) +labs(x = "", y = "Counts", title = "") +theme_prism(base_fontface = "plain", base_family = "serif", base_size = 16,base_line_size = 0.8, axis_text_angle = 0) +theme_classic() +theme(axis.title.y = element_text(face = "bold", size = 14, color = "black", family = "sans"))# 横向堆叠柱状图# Horizontal stack bar plotp32 <- ggplot(df, aes(x = time, y = values, fill = genes)) +geom_col(width = 0.45, position = position_stack(vjust = 1)) +scale_fill_manual(values = c("#e1abbc", "#edd064", "#0eb0c8", "#f2ccac", "#a1d5b9", "#6a73cf")) +scale_y_continuous(expand = c(0, 0), limits = c(0, 100)) +scale_x_discrete(expand = c(0.1, 0.1)) +coord_flip() +geom_text(aes(label = values), position = position_stack(vjust = 0.5), colour = "white", size = 3) +labs(x = "", y = "Counts", title = "") +theme_prism(base_fontface = "plain", base_family = "serif", base_size = 16, base_line_size = 0.8, axis_text_angle = 0) +theme_classic() +theme(axis.title.y = element_text(face = "bold", size = 14, color = "black", family = "sans"))# 不展示数量大小# Do not display the quantityp33 <- ggplot(df, aes(x = time, y = values, fill = genes)) +geom_col( width = 0.5,position = position_stack(vjust = 1)) +scale_fill_manual(values=c("#e1abbc","#edd064","#0eb0c8","#f2ccac","#a1d5b9","#6a73cf")) +scale_y_discrete(expand = c(0.0,0.0), limits = c(0, 25,50,75,94))+scale_x_discrete(expand = c(0.1,0.1), limits = c("D1","D10","D30","D40"))+labs(x = "", y = "counts", title = "") +theme_prism(base_fontface = "plain",base_family = "serif",base_size = 16,base_line_size = 0.8,axis_text_angle = 0) +theme_classic()+theme(axis.title.y = element_text(face="bold",size = 14, color = "black",family = "sans"))# 百分比形式展示# Display as percentagep34 <- ggplot(df, aes(x = time, y = values, fill = genes)) +geom_bar(width = 0.5,stat="identity",position = "fill") +scale_fill_manual(values=c("#e1abbc","#0eb0c8","#edd064","#f2ccac","#a1d5b9","#8a95ab")) +scale_y_discrete(expand = c(0.0,0.0),limits=c(0.00,0.25,0.50,0.75,1.00))+scale_x_discrete(expand = c(0.1,0.1), limits = c("D1","D10","D30","D40"))+labs(x = "", y = "Frequency", title = "") +theme_prism(base_fontface = "plain",base_family = "serif",base_size = 16,base_line_size = 0.8,axis_text_angle = 0) +theme_classic()+theme(axis.title.y = element_text(face="bold",size = 14, color = "black",family = "sans"))ggsave("results/stacked_bar_percentage.pdf", width = 8, height = 6)# 组合图# Combinedlibrary(cowplot)width = 89height = 59p0 = plot_grid(p31, p32, p33, p34, labels = c("A", "B", "C", "D"), ncol = 2)ggsave("results/simple_stack_bar_plot.pdf", p0, width = width * 2, height = height * 3, units = "mm")
实战4 堆积柱状图结合折线图
参考：https://mp.weixin.qq.com/s/ayHjWK7WwJGKn0B8WCivJg 参考：https://mp.weixin.qq.com/s/o9aveoC4LXb2Tu8kmGQ4rQ
# 载入数据# Load datamydata <- read.table(file = "data/data_practice4.txt", sep = "\t", header = TRUE, check.names = FALSE)# 两两比较绘图# Plotp41 <- ggplot(mydata, aes(x = type, y = values, fill = cell_groups)) +geom_col(width = 0.55, position = position_stack(reverse = TRUE), show.legend = FALSE) +geom_line(aes(colour = cell_groups)) +scale_x_discrete(limits = c("Normal BMI D0", "Severe obesity D0", 3,"Normal BMI D8", "Severe obesity D8", 6,"Normal BMI D28", "Severe obesity D28"),labels = c("Normal BMI D0", "Severe obesity D0", "","Normal BMI D8", "Severe obesity D8", "","Normal BMI D28", "Severe obesity D28")) +scale_y_continuous(expand = c(0, 0), breaks = seq(0, 100, 20)) +#scale_fill_manual(values = c("firebrick1", "dodgerblue3", "limegreen", "blueviolet", "orange1")) +scale_fill_manual(values = c("#5ebcc2", "#324b4d", "#94b0b2", "#aba9dd", "#7575a6")) +theme_classic(base_size = 14) +theme(plot.subtitle = element_text(face = "bold", size = 18, hjust = -0.2, vjust = -3),axis.line = element_line(size = 0.3),axis.ticks = element_line(size = 0.3),axis.ticks.length = unit(0.2, "cm"),panel.grid = element_blank(),panel.border = element_blank(),axis.text.x = element_text(colour = "black",angle = 45, hjust = 0.97, vjust = 0.95),legend.position = "top") +labs(subtitle = "e",colour = "",fill = "",x = "",y = expression("% of total RBD"^"+"*" cells"))# 三个比较绘图(方式1)# Load datadat1 <- read.table(file = "data/data_practice42.txt", sep = "\t", header = TRUE, check.names = FALSE)colnames(dat1)<-paste0("X",1:4)dat1 %>%mutate(X4=factor(X4,levels = c("ratio of down-relulated genes","ratio of up-relulated genes","ratio of None-differentially expressed genes")))p42 <- ggplot(dat1, aes(x = X2, y = X3, fill = X4)) +geom_col(width = 0.55, position = position_stack(reverse = TRUE), show.legend = FALSE) +geom_line(aes(colour = X4)) +#geom_bar(aes(colour = X4))+scale_x_discrete(limits = c("genes with DEL leaf", "genes with INS leaf", "genes without SV leaf", 4,"genes with DEL fruit", "genes with INS fruit", "genes without SV fruit", 8,"genes with DEL seed", "genes with INS seed", "genes without SV seed", 12,"genes with DEL embryo", "genes with INS embryo", "genes without SV embryo"),labels = c("genes with DEL leaf", "genes with INS leaf", "genes without SV leaf", "","genes with DEL fruit", "genes with INS fruit", "genes without SV fruit", "","genes with DEL seed", "genes with INS seed", "genes without SV seed", "","genes with DEL embryo", "genes with INS embryo", "genes without SV embryo")) +scale_y_continuous(expand = c(0, 0), breaks = seq(0, 1, 0.1)) +#scale_fill_manual(values = c("firebrick1", "dodgerblue3", "limegreen", "blueviolet", "orange1")) +scale_fill_manual(values = c("#5ebcc2", "#324b4d", "#94b0b2", "#aba9dd", "#7575a6")) +guides(fill=guide_legend(ncol = 1))+theme_classic(base_size = 14) +theme(plot.subtitle = element_text(face = "bold", size = 18, hjust = -0.2, vjust = -3),axis.line = element_line(size = 0.3),axis.ticks = element_line(size = 0.3),axis.ticks.length = unit(0.2, "cm"),panel.grid = element_blank(),panel.border = element_blank(),axis.text.x = element_text(colour = "black",angle = 45, hjust = 0.97, vjust = 0.95),legend.position = "top") +labs(subtitle = "e",colour = "",fill = "",x = "",y = "Percentage (%)")# 三个比较绘图(方式2)# 载入数据dat2 <- read.table(file = "data/data_practice43.txt", sep = "\t", header = TRUE, check.names = FALSE)colnames(dat2)<-paste0("X",1:5)dat2 %>%mutate(X5=factor(X5,levels = c("ratio of down-relulated genes","ratio of up-relulated genes","ratio of None-differentially expressed genes")))# 绘图p43 <- ggplot(data=dat2,aes(x=X2,y=X4))+geom_bar(aes(fill=X5),stat = "identity",position = "fill")+theme_bw(base_size = 15)+theme(panel.grid = element_blank(),panel.border = element_blank(),axis.line = element_line(),legend.position = "top",legend.title = element_blank())+guides(fill=guide_legend(ncol = 1))+scale_x_continuous(breaks = c(1,2,3,5,6,7,9,10,11,13,14,15),labels = rep(c("D","I","N"),4))+scale_y_continuous(expand = expansion(mult = c(0,0)),breaks = c(0,0.5,1),labels = c(0,50,100))+labs(x=NULL,y="Percentage (%)")+scale_fill_manual(values = c("#7575a6","#5ebcc2","#94b0b2"))# 分组标签p44 <- data.frame(x=c(2,6,10,14),y=1,label=c("Leaf","Fruit","Seed","Embryo")) %>%ggplot(aes(x=x,y=y))+geom_text(aes(label=label),hjust=0.5)+theme_void()+scale_x_continuous(limits = c(1,15))# 组合图1library(patchwork)p45 <- p43+p44+plot_layout(ncol = 1,heights = c(10,1))# 组合图2# Combinedlibrary(cowplot)width = 89height = 59p0 = plot_grid(p41, p42, p45, labels = c("A", "B", "C"), ncol = 2)ggsave("results/multigroup_stack_bar_plot.pdf", p0, width = width * 4, height = height * 5, units = "mm")
实战5 排序堆叠柱状图
按照丰度最高的门排序
# 导入OTU文件# Import OTU fileotu <- read.table('data/phylum_top10.txt', header = TRUE)# 计算每个门的平均丰度并排序# Calculate and rank the average abundance of each gateotu$mean <- apply(otu, 1, mean)otu <- otu[order(otu$mean), ]# 转置数据并排序# Transpose and sort the dataotu2 <- t(otu[, -13])otu2 <- otu2[order(otu2[, 11]), ]otu3 <- as.data.frame(t(otu2))otu3$phylum <- factor(rownames(otu), levels = rownames(otu))# 将数据框转换为长格式# Converting data frames to long formatotu4 <- melt(otu3, id.vars = 'phylum')# 分组信息处理# Packet information processingotu4$group <- gsub("t[1-6]", "group1", otu4$variable)otu4$group <- gsub("c[1-6]", "group2", otu4$group)# 绘制 group1 图形# Drawing group1 graphsotu5 <- otu4[otu4$group == "group1", ]p_g2 <- ggplot(otu5, aes(x = factor(variable, levels = unique(variable)),y = value, fill = factor(phylum, levels = unique(phylum)))) +geom_bar(stat = "identity", position = "fill", width = 0.9) +scale_y_continuous(labels = scales::percent, expand = c(0, 0)) +guides(fill = guide_legend(title = "Phylum")) +theme_bw() +theme(axis.ticks.x = element_blank(),axis.text.x = element_blank(),legend.key.size = unit(0.4, "cm"),axis.title.x = element_blank(),legend.position = "bottom") +ggtitle("group1") +scale_fill_manual(values = c("#d2da93","#5196d5","#00ceff","#ff630d","#35978b","#e5acd7","#77aecd","#ec8181","#dfc6a5","#e50719","#d27e43","#8a4984","#fe5094","#8d342e","#f94e54","#ffad00","#36999d","#00fc8d","#b64aa0","#9b82e1"))# 绘制 group2 图形# Drawing group2 graphsotu6 <- otu4[otu4$group == "group2", ]p_g3 <- ggplot(otu6, aes(x = factor(variable, levels = unique(variable)),y = value, fill = factor(phylum, levels = unique(phylum)))) +geom_bar(stat = "identity", position = "fill", width = 0.9) +scale_y_continuous(labels = scales::percent, expand = c(0, 0)) +guides(fill = guide_legend(title = "Phylum")) +theme_bw() +theme(axis.ticks.x = element_blank(),axis.text.x = element_blank(),axis.text.y = element_blank(),axis.ticks.y = element_blank(),axis.title.y = element_blank(),legend.key.size = unit(0.4, "cm"),axis.title.x = element_blank(),legend.position = "none") +ggtitle("group2") +scale_fill_manual(values = c("#d2da93","#5196d5","#00ceff","#ff630d","#35978b","#e5acd7","#77aecd","#ec8181","#dfc6a5","#e50719","#d27e43","#8a4984","#fe5094","#8d342e","#f94e54","#ffad00","#36999d","#00fc8d","#b64aa0","#9b82e1"))# 合并图形并保存为PDF# Merge graphics and save as PDFp_g4 <- p_g2 + p_g3ggsave("results/phylum_composition_group1_group2.pdf", p_g4, width = 12, height = 6)
实战6 堆叠柱状图连线
参考：Yunyun Gao, Danyi Li, Yong-Xin Liu, Microbiome research outlook: past, present, and future, Protein & Cell, 2023, pwad031, https://doi.org/10.1093/procel/pwad031.
# 载入数据# Load datamydata<- read.table("data/FigureS1bBarplotWithLine.txt", header = T, sep='\t')# 定义绘图顺序# Define the desired order of levels for the ID variabletest1 <- gather(mydata, E1, E2, -ID)my_order<- c("Others","Ireland","Poland","Belgium","Sweden","Switzerland","Denmark","Brazil","South Korea","Japan","India","France","Netherlands","Spain","Australia","Italy","Canada","United Kingdom","Germany","China","United States")# Reorder the levels of the ID variable# ID排序test1$ID <- factor(test1$ID, levels = my_order)# 绘图# Plotp61 <- ggplot(test1, aes(x=E1, y=E2,alluvium = ID)) +geom_alluvium(aes(fill= ID),alpha= 0.4, width = 0.4) +geom_bar(stat="identity",aes(fill=ID), width = 0.4) +scale_y_continuous(expand = c(0,0)) +scale_fill_manual(values=c("#a6cee3","#1f78b4","#b2df8a","#33a02c","#fb9a99","#e31a1c","#fdbf6f","#ff7f00","#cab2d6","#6a3d9a","#ffff99","#ffed6f","#ccebc5","#bc80bd","#d9d9d9","#fccde5","#b3de69","#fdb462","#80b1d3","#fb8072","#8dd3c7"))+theme_classic() +theme(panel.background = element_rect(fill="white", colour="white", size=0.25),axis.line = element_line(colour="black", size=0.5),axis.title = element_text(size=13, color="black"),axis.text = element_text(size=12, color="black"),legend.text = element_text(size =10),aspect.ratio = 1,plot.title = element_text(size = 10),axis.text.x = element_text(angle = 45, hjust = 1)) +guides(fill = guide_legend(reverse = TRUE))#p61ggsave("results/phylum_stack_line01.pdf", p61, width = 12, height = 6)
使用此脚本，请引用下文：
Yong-Xin Liu, Lei Chen, Tengfei Ma, Xiaofang Li, Maosheng Zheng, Xin Zhou, Liang Chen, Xubo Qian, Jiao Xi, Hongye Lu, Huiluo Cao, Xiaoya Ma, Bian Bian, Pengfan Zhang, Jiqiu Wu, Ren-You Gan, Baolei Jia, Linyang Sun, Zhicheng Ju, Yunyun Gao, Tao Wen, Tong Chen. 2023. EasyAmplicon: An easy-to-use, open-source, reproducible, and community-based pipeline for amplicon data analysis in microbiome research. iMeta 2: e83. https://doi.org/10.1002/imt2.83
Copyright 2016-2024 Defeng Bai baidefeng@caas.cn, Chuang Ma 22720765@stu.ahau.edu.cn, Jiani Xun 15231572937@163.com, Yong-Xin Liu liuyongxin@caas.cn
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