柱状图简介
柱状图（Bar Chart）是一种常用于显示分类变量数据的图形表示方式，主要通过矩形柱的长度或高度来反映各分类的数值大小。柱状图能够有效展示不同类别之间的对比情况，并直观地体现各类目之间的差异。其横轴通常用于表示分类变量，而纵轴则代表某种度量（如频率、百分比或其他数值）。每一个分类的柱子长度与其对应的数值成正比，从而使得不同类别之间的数据量可视化。
标签：#微生物组数据分析 #MicrobiomeStatPlot #基本柱状图 #R语言可视化 #Basic 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/BasicBarPlot
或公众号后台回复“MicrobiomeStatPlot”领取
这是Xuehui Huang课题组2021年发表于Nature Genetics上的文章，第一作者为Xin Wei，题目为：A quantitative genomics map of rice provides genetic insights and guides breeding https://doi.org/10.1038/s41588-020-00769-9
图 4 | QTNs 的遗传调查。
e，东亚、南亚和东南亚籼稻 QTNs 的等位基因频率；f，东亚、东南亚和东北亚粳稻 QTNs 的等位基因频率。所示的所有 QTG 都具有高度差异的等位基因频率（AF > 0.4）。各 QTG 的详细功能等位基因频率见补充数据集 4 和 5。
结果
在籼稻方面，东亚地区具有较多的早穗期等位基因、较多的抗稻瘟病等位基因和较强的抗低温发芽能力，但较少的高矿质营养利用效率等位基因（图4e），这与该地区昼长、病害胁迫严重、低温和大量施肥的特点相一致。同时，东北亚地区的粳稻耐寒性和早穗期等位基因频率较高（图4f），这与东北亚地区种植季节气温低、昼长的特点相一致。
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p_list = c("ggplot2", "dplyr", "readxl", "cols4all", "patchwork", "scales","patchwork", "reshape2", "ggsignif","tidyverse","ggh4x","grid","showtext","Cairo","ggpattern","ggpubr","ggbreak","dagitty","brms","broom","broom.mixed","ggdag","MetBrewer","latex2exp","readxl","ggsci")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 Load the packagesuppressWarnings(suppressMessages(library(ggplot2)))suppressWarnings(suppressMessages(library(dplyr)))suppressWarnings(suppressMessages(library(readxl)))suppressWarnings(suppressMessages(library(cols4all)))suppressWarnings(suppressMessages(library(patchwork)))suppressWarnings(suppressMessages(library(scales)))suppressWarnings(suppressMessages(library(patchwork)))suppressWarnings(suppressMessages(library(reshape2)))suppressWarnings(suppressMessages(library(ggsignif)))suppressWarnings(suppressMessages(library(tidyverse)))suppressWarnings(suppressMessages(library(ggh4x)))suppressWarnings(suppressMessages(library(MetBrewer)))suppressWarnings(suppressMessages(library(latex2exp)))suppressWarnings(suppressMessages(library(readxl)))suppressWarnings(suppressMessages(library(ggsci)))suppressWarnings(suppressMessages(library(ggdag)))suppressWarnings(suppressMessages(library(broom.mixed)))suppressWarnings(suppressMessages(library(broom)))suppressWarnings(suppressMessages(library(grid)))suppressWarnings(suppressMessages(library(showtext)))suppressWarnings(suppressMessages(library(Cairo)))suppressWarnings(suppressMessages(library(ggpattern)))suppressWarnings(suppressMessages(library(ggpubr)))suppressWarnings(suppressMessages(library(ggbreak)))suppressWarnings(suppressMessages(library(dagitty)))suppressWarnings(suppressMessages(library(brms)))
实战1
参考：
https://mp.weixin.qq.com/s/_7eCtIbvNChngVOhNuIfVA
参考：
https://mp.weixin.qq.com/s/44lUo1Ncsd7Ef_7Luj5pHA
参考：
https://mp.weixin.qq.com/s/N0KPinMiTS-9zRB8XcfZrA
# 数据# Creat datadf <- data.frame(group = c("A", "B", "C","D","F"),value1 = c(10, 20, 30, 20, 15),value2 = c(15, 25, 35, 15, 20))df1 <- melt(df)# 添加误差数据# Add error datadf1$sd <- c(1,2,3,2.5,1,1,2,3,2,1.5)# 误差线位置# Error bar positiondf1 <- df1 %>%group_by(group) %>%mutate(xx=cumsum(value))# 保证误差线可以对应其正确位置# Ensure that the error bars correspond to their correct positionsdf1$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))# 美化与主题调整# Beautification and theme adjustmentmycol1 <- c("#4E79A7","#F28E2B")mycol2 <- c("#85D4E3","#F4B5BD")# 分组柱形图，添加误差棒# Grouping bar graphs, adding error barsp1 <- ggplot(df1, aes(x = group, y = value, fill = variable)) +geom_bar(position = position_dodge(), stat = "identity", color = NA, width = 0.8) +scale_fill_manual(values = mycol1) +scale_y_continuous(expand = c(0, 0)) +theme_classic() +geom_errorbar(aes(ymin = value - sd, ymax = value + sd),position = position_dodge(width = 0.8),width = 0.4) +labs(y = 'Value') +theme(axis.text = element_text(size = 12),axis.title = element_text(size = 14))# 堆叠柱形图，添加误差棒# Stacked bar chart with error bars addeddf1 <- df1 %>%group_by(group) %>%mutate(position = cumsum(value))p2 <- ggplot(df1, aes(x = group, y = value, fill = variable)) +geom_bar(position = "stack", stat = "identity", color = NA, width = 0.8) +scale_fill_manual(values = mycol2) +scale_y_continuous(expand = c(0, 0)) +theme_classic() +geom_errorbar(aes(ymin = position - sd, ymax = position + sd),width = 0.4) +labs(y = 'Value') +theme(axis.text = element_text(size = 12),axis.title = element_text(size = 14))# 保存图形为PDF文件# Save graphics as PDF filesggsave("results/p1.pdf", plot = p1, width = 6, height = 4)ggsave("results/p2.pdf", plot = p2, width = 6, height = 4)# 组合图library(cowplot)width = 89height = 59p0 = plot_grid(p1, p2, labels = c("A", "B"), ncol = 2)ggsave("results/Simple_bar_plot1.pdf", p0, width = width * 3, height = height * 2, units = "mm")
实战2
利用ggpattern软件包绘制柱状图
# 测试使用内置数据集ToothGrowth# Test using the dataset ToothGrowthhead(ToothGrowth)table(ToothGrowth$supp)#>#> OJ VC#> 30 30table(ToothGrowth$dose)#>#> 0.5 1 2#> 20 20 20p21 <- ggplot(ToothGrowth, aes(x = factor(dose), y = len)) +geom_bar_pattern(aes(pattern_fill = factor(dose)),stat = "summary", fun = mean, position = "dodge", linewidth = 0.7,pattern = 'gradient', pattern_key_scale_factor = 1.5,pattern_fill2 = NA, fill = NA) +stat_summary(fun.data = 'mean_sd', geom = "errorbar", width = 0.15, linewidth = 0.6) +#theme_minimal(base_size = 16) +theme_classic()+scale_y_continuous(expand = c(0,0), limits = c(0, 35), breaks = seq(0, 35, 5)) +scale_pattern_fill_manual(values = c("#89A66D", "#538DB7", "#D8898A")) +labs(x = NULL, title = "Gradient Pattern", subtitle = 'Pattern Fill = Dose') +theme(legend.position = 'none') +geom_signif(comparisons = list(c("0.5", "1"), c("0.5", "2")),y_position = c(24, 30), map_signif_level = TRUE,test = wilcox.test, textsize = 6)#p21p22 <- ggplot(ToothGrowth, aes(x = factor(dose), y = len)) +geom_bar_pattern(aes(pattern_fill = factor(dose)),stat = "summary", fun = mean, position = "dodge", linewidth = 0.7,pattern = 'plasma', pattern_key_scale_factor = 1.5,pattern_fill2 = NA, fill = NA) +stat_summary(fun.data = 'mean_sd', geom = "errorbar", width = 0.15, linewidth = 0.6) +#theme_minimal(base_size = 16) +theme_classic()+scale_y_continuous(expand = c(0, 0), limits = c(0, 35), breaks = seq(0, 35, 5)) +scale_pattern_fill_manual(values = c("#89A66D", "#538DB7", "#D8898A")) +labs(x = NULL, title = "Plasma Pattern", subtitle = 'Pattern Fill = Dose') +theme(legend.position = 'none') +geom_signif(comparisons = list(c("0.5", "1"), c("0.5", "2")),y_position = c(24, 30), map_signif_level = TRUE,test = wilcox.test, textsize = 6)# p22# 保存为PDF# Save as PDFggsave("results/gradient_plasma.pdf", p21 + p22, width = 8, height = 5)# 柱子上添加图片p23 <- ggplot(ToothGrowth, aes(x = factor(dose), y = len)) +geom_bar_pattern(aes(pattern_type = factor(dose)),pattern = 'placeholder', pattern_type = 'bear',stat = "summary", fun = mean, position = "dodge",pattern_spacing = 0.08, pattern_density = 0.9,linewidth = 0.6, fill = 'white', colour = 'black') +stat_summary(fun.data = 'mean_sd', geom = "errorbar", width = 0.15, linewidth = 0.6) +#theme_minimal(base_size = 16) +theme_classic()+scale_y_continuous(expand = c(0, 0), limits = c(0, 35), breaks = seq(0, 35, 5)) +labs(x = NULL, title = "Placeholder Pattern", subtitle = 'Pattern Type = Dose') +theme(legend.position = 'none') +geom_signif(comparisons = list(c("0.5", "1"), c("0.5", "2")),y_position = c(24, 30), map_signif_level = TRUE,test = wilcox.test, textsize = 6)# 保存为PDF# Save as PDFggsave("results/placeholder_pattern.pdf", p23, width = 6, height = 5)# 条纹样式p24 <- ggplot(ToothGrowth, aes(x = factor(dose), y = len)) +geom_bar_pattern(aes(pattern_type = factor(dose)),pattern = 'magick', pattern_fill = "black",stat = "summary", fun = mean, position = "dodge",pattern_spacing = 0.08, pattern_density = 0.9,linewidth = 0.6, fill = 'white', colour = 'black') +stat_summary(fun.data = 'mean_sd', geom = "errorbar", width = 0.15, linewidth = 0.6) +#theme_minimal(base_size = 16) +theme_classic()+scale_y_continuous(expand = c(0, 0), limits = c(0, 35), breaks = seq(0, 35, 5)) +scale_pattern_type_discrete(choices = c('vertical2','hs_bdiagonal','gray100','gray0')) +labs(x = NULL, title = "Magick Pattern", subtitle = 'Pattern Type = Dose') +theme(legend.position = 'none') +geom_signif(comparisons = list(c("0.5", "1"), c("0.5", "2")),y_position = c(24, 30), map_signif_level = TRUE,test = wilcox.test, textsize = 6)# 保存为PDF# Save as PDFggsave("results/magick_pattern.pdf", p24, width = 6, height = 5)# 组合图library(cowplot)width = 89height = 59p0 = plot_grid(p21, p22, p23, p24, labels = c("A", "B", "C", "D"), ncol = 2)ggsave("results/ggpattern_bar_plot1.pdf", p0, width = width * 3, height = height * 3, units = "mm")
实战3
渐变色分组柱状图
参考：
https://mp.weixin.qq.com/s/La0W03eveYSLggKIh_gC3A
# 渐变色分组柱状图# Gradient color grouped bar chartdf <- tribble(~pattern, ~treat,~value,~sd, ~color,'S', 'blank', 0.253, 0.02,"brown",'S', '20', 0.243, 0.005,"green",'S', '40', 0.193, 0.02, "yellow",'P', 'blank', 0.17, 0.001,"brown",'P', '20', 0.08, 0.01,"green",'P', '40', 0.06, 0.001, "yellow")# 绘图pdf(file='results/Bar_plot_change_color01.pdf', height=10,width=12, family='sans')ggplot(df) +geom_col_pattern(aes(x = factor(pattern, levels = c('S','P')),y = value,group = factor(treat, levels = c('blank', '20', '40')),pattern_fill = color),pattern = 'gradient',color = NA, fill = NA,position = position_dodge2(padding = 0.3),width = 0.6) +geom_errorbar(aes(x = factor(pattern, levels = c('S','P')),ymin = value - sd, ymax = value + sd,color = factor(treat, levels = c('blank', '20', '40'))),position = position_dodge(width = 0.6),linewidth = 1.1,width = 0.1) +annotate("text", x = 'S', y = 0.29, label = "Anti-interference",size = 7, color = '#395529') +annotate("text", x = 'P', y = 0.21, label = "Interference",size = 7, color = '#691000') +geom_curve(aes(x = 0.7, y = 0.26, xend = 1.3, yend = 0.205),curvature = -0.3,linewidth = 1.2,color = '#324F28',arrow = arrow(length = unit(0.03, "npc"),ends = "last", type = "open")) +annotate(geom = 'line', x = c(1.8, 2.1, 2.2, 2.3), y = c(0.2, 0.1, 0.11, 0.09),linewidth = 1.2,color = "#AD480D",arrow = arrow(length = unit(0.03, "npc"),ends = "last", type = "open")) +scale_x_discrete(labels = c(expression(MnFe[2]*O[4]-S), expression(MnFe[2]*O[4]-P))) +scale_y_continuous(limits = c(0, 0.35), expand = expansion(mult = c(0, 0))) +scale_pattern_fill_manual(values = c('#F5CCB2', '#A9E1B6', '#F5F5A1'),labels = c('Blank', expression(20~mg%.%L^-1~HA),expression(40~mg%.%L^-1~HA))) +scale_color_manual(values = c("#8C3209", "#1A6A3E", "#8F8B00"),labels = c('Blank', expression(20~mg%.%L^-1~HA),expression(40~mg%.%L^-1~HA))) +xlab(NULL) +ylab(expression(italic(k)*(min^-1))) +ggthemes::theme_par(base_size = 16) +theme(legend.text.align = 0,legend.title = element_blank(),legend.position = c(0.8, 0.8),legend.background = element_blank(),legend.key.size = unit(1, 'cm'))dev.off()#> png#> 2
实战4
蝴蝶柱状图
参考：https://mp.weixin.qq.com/s/Iz9GR6hE2vIlziH_y7npew
# 生成图形所需数据集# Generate the data set required for the graphmydata<-data.frame(id=1:14,A=c(5.0,14.7,2.5,8.5,5.1,6.9,7.7,6.8,4.4,4.9,5.3,1.0,0.9,7.8),B=c(31.3,24.7,17.8,17.2,15.3,14.3,13.9,13.9,12.4,10.0,6.5,4.2,2.5,0.9),Label=c("Website","Customer & Employee Referral","Webinar","Facebook/Twitter/Other Social","Marketting & Advertising","Paid Serch","Other","Sales generated","Tradeshows","Parter","Linkedin","Events","Lead list","Emial Campaign"))# 设置颜色和字体# Setting colours and fontscolor_A <- "#D4AF37"color_B <- "#B22222"font_family <- "sans"# 制作右侧柱状图# Make a right-hand side bar chartp_right <- ggplot(mydata) +geom_hline(yintercept = mean(mydata$A), linetype = "dashed", size = 0.5, colour = "grey70") +geom_bar(aes(x = id, y = A), stat = "identity", fill = color_A, colour = NA) +ylim(-5.5, 16) +scale_x_reverse() +geom_text(aes(x = id, y = -4, label = Label), vjust = 0.5, size = 4, family = font_family) +geom_text(aes(x = id, y = A + 0.75, label = paste0(A, "%")), size = 4.5, family = font_family, fontface = "bold") +coord_flip() +theme_void()# 制作左侧柱状图# Creating the left-hand side bar chartp_left <- ggplot(mydata) +geom_hline(yintercept = -mean(mydata$B), linetype = "dashed", size = 0.5, colour = "grey70") +geom_bar(aes(x = id, y = -B), stat = "identity", fill = color_B, colour = NA) +ylim(-40, 0) +scale_x_reverse() +geom_text(aes(x = id, y = -B - 1.75, label = paste0(B, "%")), size = 4.5, family = font_family, fontface = "bold") +coord_flip() +theme_void()# 合并两侧图形# Merge both sides of the graphp_butterfly <- p_left + p_right +plot_layout(widths = c(1, 1))#p_butterfly# 保存为PDF Save as PDFggsave(filename = "results/butterfly_plot.pdf", plot = p_butterfly, width = 18, height = 6)
实战5
簇状柱状图
参考：https://mp.weixin.qq.com/s/WXEGN-O2u1m6YU-B_SBDgwhttps://mp.weixin.qq.com/s/j4xLG66Xei4mBZKW6uM00w
# 读取数据fig3a <- read_excel("data/nature_figure3.xlsx", sheet = "fig3a")# 数据处理：重命名和设置因子顺序# Data processing: renaming and setting factor orderfig3a <- fig3a %>%mutate(`Ancestries/Ethnicities` = factor(`Ancestries/Ethnicities`,levels = c('European', 'Hispanic', 'South-Asian', 'East-Asian', 'African')),group = factor(group, levels = c('within GWS loci', 'outside GWS loci')))# 计算各族裔的总遗传力# Calculation of total heritability by ethnicityfig3a1 <- fig3a %>%group_by(`Ancestries/Ethnicities`) %>%summarise(value = sum(Heritability)) %>%ungroup()# 定义X轴标签# Defining X-axis labelsx_labels <- c("EUR\n(n = 28,645)", "HIS\n(n = 4,939)", "SAS\n(n = 9,257)","EAS\n(n = 49,939)", "AFR\n(n = 15,149)")# 绘图p1 <- ggplot(data = fig3a, aes(x = `Ancestries/Ethnicities`, y = Heritability)) +geom_bar(stat = "identity", position = position_dodge(), aes(fill = group), width = 0.7) +geom_errorbar(aes(ymin = Heritability - SE, ymax = Heritability + SE, group = group),position = position_dodge(0.7), width = 0.2, color = "red", size = 0.8) +scale_fill_manual(values = c("#00afbb", "#e7b800"),labels = c(TeX(r"(Within GWS loci (21% of the genome): \textit{h}${^2}$${_G}{_W}{_S}$)"),TeX(r"(Outside GWS loci (79% of the genome): \textit{h}${^2}$${_o}{_t}{_h}{_e}{_r}$)"))) +scale_y_continuous(breaks = seq(0, 0.5, by = 0.1), limits = c(0, 0.5)) +scale_x_discrete(labels = x_labels) +theme_bw() +theme(panel.grid = element_blank(),panel.border = element_blank(),axis.line.y = element_line(),axis.ticks.x = element_blank(),axis.text.x = element_text(vjust = 10, size = 12),legend.title = element_blank(),legend.position = "top",legend.justification = c(0, 0),legend.key.size = unit(0.3, 'cm'),plot.margin = margin(t = 0.5, r = 1, b = 1, l = 0.5, unit = "cm")) +geom_hline(yintercept = 0, color = "gray") +coord_cartesian(clip = "off") +labs(x = "Ancestries\n(sample size)", y = "Heritability")# 输出图形# Output Graphics# 读取数据fig3b <- read_excel("data/nature_figure3.xlsx", sheet = "fig3b")# 数据处理：设置因子顺序# Data processing: setting the order of factorsfig3b <- fig3b %>%mutate(`Ancestries/Ethnicities` = factor(`Ancestries/Ethnicities`,levels = c('European', 'Hispanic', 'South-Asian', 'East-Asian', 'African')))# 添加文本注释数据# Adding Text Annotation Datafig3b.text <- data.frame(x = 1:5,y = 0.5,label = paste(c("310,092", "348,498", "334,237", "304,478", "371,622"),"SNPs in GWS loci"))# 绘图p2 <- ggplot(data = fig3b, aes(x = `Ancestries/Ethnicities`, y = Heritability)) +geom_bar(stat = "identity", position = position_fill(), aes(fill = group), width = 0.7, show.legend = FALSE) +scale_fill_manual(values = c("#e7b800", "#00afbb")) +geom_text(data = fig3b.text, aes(x = x, y = y, label = label),angle = 90, color = "white", size = 3.5, vjust = 0.5, hjust = 0.5) +scale_x_discrete(labels = c("EUR\n(1,130,264)", "HIS\n(1,277,112)", "SAS\n(1,222,935)","EAS\n(1,110,588)", "AFR\n(1,180,574)")) +scale_y_continuous(breaks = seq(0, 1, by = 0.1), expand = expansion(mult = c(0, 0))) +labs(x = "Ancestries (total number of SNPs analysed)",y = TeX(r"(Proportion of SNP-based heritability within GWS loci $(\textit{h}{^2}{_G}{_W}{_S}/\textit{h}{^2}{_S}{_N}{_P})$)")) +theme_bw() +theme(panel.grid = element_blank(),panel.border = element_blank(),axis.line.y = element_line(),axis.ticks.x = element_blank(),axis.text.x = element_text(size = 12, vjust = 0.5),axis.text.y = element_text(size = 12),plot.margin = margin(t = 0.5, r = 1, b = 1, l = 0.5, unit = "cm")) +geom_hline(yintercept = 0.9, color = "red", linetype = "dashed")# 输出图形# Output Graphics# 组合图p3 <- p1+p2+plot_annotation(tag_levels = "a")# p3# 保存为PDF Save as PDFggsave(filename = "results/Clustered_Barplot1.pdf", plot = p3, width = 18, height = 6)
实战6
绘制显著性、误差线簇状柱状图
# 模拟数据# Simulated datadf <- data.frame(ID = paste0("sample", 1:60),Weight = sample(20:200, 60, replace = FALSE),species = rep(c("A", "B"),times=30),time = rep(c("E1", "E2", "E3"), each = 20))p61 <- ggplot(data=df, aes(x=species, y=Weight, fill=time))+geom_bar(stat = "identity", position = "dodge")# 图中给每个柱子加了误差线，因此我们要先求出平均值与标准差# Error bars are added to each column in the figure, so we need to find the mean and standard deviation firstresult1 <- aggregate(df$Weight, by = list(df$species, df$time), mean)colnames(result1) <- c("species", "time", "mean_weight")result2 <- aggregate(df$Weight, by = list(df$species, df$time), sd)colnames(result2) <- c("species", "time", "sd_weight")result1$sd_weight <- result2$sd_weightresult <- result1# 现在我们根据result绘制簇状柱状图# Now we draw a clustered bar chart based on resultresult$species<-factor(result$species,levels = c("A","B"))result$time<-factor(result$time,levels = c("E1","E2","E3"))# 配色optimized_cols <- c("#4E79A7", "#F28E2B", "#E15759")# 绘图p62 <- ggplot(result, aes(x=species, y=mean_weight, fill=time)) +geom_bar(position=position_dodge(), stat="identity", color="white", width=0.7) +geom_errorbar(aes(ymin=mean_weight-sd_weight, ymax=mean_weight+sd_weight),width=0.2, size=0.6, position=position_dodge(0.7), color="black") +scale_fill_manual(values=optimized_cols) +geom_signif(data=result, aes(xmin=0.71, xmax=1, annotations="**", y_position=188),textsize = 4, vjust = 0.05, tip_length = c(0.02, 0.02), manual=TRUE) +geom_signif(data=result, aes(xmin=0.71, xmax=1.31, annotations="*", y_position=192),textsize = 4, vjust = 0.05, tip_length = c(0.02, 0.02), manual=TRUE) +scale_y_continuous(name="Weight (g)\n", expand=c(0,0), limits=c(0, 200)) +scale_x_discrete(name="Species") +theme_classic() +theme(axis.line = element_line(color="black", size=1),axis.text = element_text(size=12, color="black"),axis.title = element_text(size=14, face="bold"),legend.title = element_blank(),legend.position = "top",legend.text = element_text(size=12),legend.key.size = unit(0.4, 'cm'),panel.grid.major = element_blank(),panel.grid.minor = element_blank())# 显示最终的图# Show final diagram# 保存为PDF Save as PDFggsave(filename = "results/Clustered_Barplot2.pdf", plot = p62, width = 8, height = 5)
实战7
柱状图截断坐标轴
# 载入数据# Load datatest_design <- read.csv("data/test_design.csv",row.names = 1)df2 <- test_design[,c(1,6)]# 统计平均值，以Group为基础统计，分别为AA，BB，CC，DD。# Calculate the average value based on Group, which are AA, BB, CC, and DD.mean_df <- aggregate(df2[,2],by=list(df2[,1]),FUN=mean)rownames(mean_df) <- mean_df[,1]# 统计标准差，以Group为基础统计，分别为AA，BB，CC，DD。# Statistical standard deviation, based on Group, AA, BB, CC, DD respectively.sd_df <- aggregate(df2[,2],by=list(df2[,1]),FUN=sd)rownames(sd_df) <- sd_df[,1]sd_df <- sd_df[,-1]# 可根据样品数计算Se,这里未进行# Se can be calculated based on the number of samples, but this is not done here# 合并数据框# Merge dataframesdf1 <- cbind(mean_df,sd_df)colnames(df1) <- c("group","mean","sd")colnames(df2) <- c("group","Gene_Abundance")# 绘图，geom_bar添加平均值柱子，geom_jitter用df2数据框加散点，geom_errorbar添加标准误误差棒。# Drawing, geom_bar adds mean value columns, geom_jitter uses the df2 data frame to add scatter points, and geom_errorbar adds standard error bars.p71 <- ggplot(df1, aes(x = group, y = mean, fill = group)) +geom_bar(stat = "identity", position = "dodge", width = 0.6, color = "black") +geom_errorbar(aes(ymin = mean - sd, ymax = mean + sd), width = 0.3, size = 0.7, color = "black", position = position_dodge(0.6)) +geom_jitter(data = df2, aes(x = group, y = Gene_Abundance, color = group), size = 2, width = 0.2, height = 0.02) +scale_fill_manual(values = c('#2D6DB1', '#DC1623')) +scale_color_manual(values = c('#2D6DB1', '#DC1623')) +scale_y_break(c(16, 18), space = 0.4, scales = 1, expand = c(0, 0)) +labs(y = "Gene Abundance", x = "") +theme_bw() +theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank(),panel.background = element_blank(),plot.background = element_blank(),axis.line = element_line(color = "black"),axis.text.x = element_text(size = 12, color = "black"),axis.text.y = element_text(size = 12, color = "black"),axis.title.x = element_text(size = 14),axis.title.y = element_text(size = 14))# 保存为PDFggsave(filename = "results/Bacteria_Barplot.pdf", plot = p71, width = 10, height = 6)# 真菌的例子# Example of fungitest_design <- read.csv("data/test_design.csv",row.names = 1)df2 <- test_design[,c(1,8)]# 统计平均值，以Group为基础统计，分别为AA，BB，CC，DD。# Calculate the average value based on Group, which are AA, BB, CC, and DD.mean_df <- aggregate(df2[,2],by=list(df2[,1]),FUN=mean)rownames(mean_df) <- mean_df[,1]# 统计标准差，以Group为基础统计，分别为AA，BB，CC，DD。# Statistical standard deviation, based on Group, AA, BB, CC, DD respectively.sd_df <- aggregate(df2[,2],by=list(df2[,1]),FUN=sd)rownames(sd_df) <- sd_df[,1]sd_df <- sd_df[,-1]# 可根据样品数计算Se,这里未进行# Se can be calculated based on the number of samples, but this is not done here# 合并数据框# Merge dataframesdf1 <- cbind(mean_df,sd_df)colnames(df1) <- c("group","mean","sd")colnames(df2) <- c("group","Gene_Abundance")# 绘图，geom_bar添加平均值柱子，geom_jitter用df2数据框加散点，geom_errorbar添加标准误误差棒。# Drawing, geom_bar adds mean value columns, geom_jitter uses the df2 data frame to add scatter points, and geom_errorbar adds standard error bars.p72 <- ggplot(df1, aes(x = group, y = mean, fill = group)) +geom_bar(stat = "identity", position = "dodge", width = 0.6, color = "black") +geom_errorbar(aes(ymin = mean - sd, ymax = mean + sd), width = 0.3, size = 0.7, color = "black", position = position_dodge(0.6)) +geom_jitter(data = df2, aes(x = group, y = Gene_Abundance, color = group), size = 2, width = 0.2, height = 0.02) +scale_fill_manual(values = c("#F2B379", "#DD5F60")) +scale_color_manual(values = c("#F2B379", "#DD5F60")) +scale_y_break(c(16, 18), space = 0.4, scales = 1, expand = c(0, 0)) +labs(y = "Gene Abundance", x = "") +theme_bw() +theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank(),panel.background = element_blank(),plot.background = element_blank(),axis.line = element_line(color = "black"),axis.text.x = element_text(size = 12, color = "black"),axis.text.y = element_text(size = 12, color = "black"),axis.title.x = element_text(size = 14),axis.title.y = element_text(size = 14))# 保存为PDF# Save as PDFggsave(filename = "results/Fungi_Barplot.pdf", plot = p72, width = 10, height = 6)
实战8
正负y轴比较柱状图和误差棒
参考：
https://mp.weixin.qq.com/s/_T_5Rp0dfBccHZaZbiPQUQ
# 测试数据# Test datadf <- read.csv("data/test_otu1.csv",row.names = 1)# 构建测试数据框，构建"Microbes"的数据# Build a test data frame and build the data of "Microbes"plot_data <- df[1000:1019,c(7,12)]colnames(plot_data) <- c("Microbes","error_bar")# 误差棒随机弄的数据，需要自己计算，这里直接除以10倍作为误差棒，没有实际意义# Error bars are random data, you need to calculate it yourself. Here, directly dividing by 10 is used as the error bar, which has no practical significanceplot_data$error_bar = plot_data$error_bar/10# 随机选择1-10天作为纵坐标时间，进行绘图# Randomly select 1-10 days as the vertical axis time for drawingDays <- as.data.frame(c(1:10,1:10))plot_data <- cbind(plot_data,Days)# 添加分组颜色，进行绘图# Add group colors for drawingGroup <- as.data.frame(c(rep("#77A779",10),rep("#DA5E9A",10)))plot_data <- cbind(plot_data,Group)# 将随机选择的数据命名为微生物，误差棒和时间，实际可根际自己数据构建此数据框# Name the randomly selected data as microorganisms, error bars and time. You can actually construct this data frame with your own datacolnames(plot_data)<-c("Microbes","Error_bar","Days","Group")# 将一组数据转化为负值,用于负y轴# Convert a set of data into negative values for negative y axisplot_data[11:20,]$Microbes<--plot_data[11:20,]$Microbes# 绘图，geom_col添加柱状图，geom_errorbar添加误差棒，geom_hline添加坐标x轴# Drawing, geom_col adds a bar chart, geom_errorbar adds an error bar, geom_hline adds a coordinate x-axispdf("results/microbes_growth.pdf", width = 6, height = 4)ggplot(plot_data, aes(x = Days, y = Microbes)) +geom_col(position = position_dodge(width = 0.5), width = 0.6, size = 0.3, fill = plot_data$Group) +geom_errorbar(aes(ymin = Microbes - Error_bar, ymax = Microbes + Error_bar), width = 0.2, size = 0.8) +labs(y = "Growth", x = "Years") +geom_hline(aes(yintercept = 0), size = 0.5, colour = "gray2") +theme_bw() +theme(panel.grid = element_blank(),axis.text = element_text(colour = 'black', size = 12),axis.title = element_text(size = 14),plot.margin = unit(c(1, 1, 1, 1), "cm"))dev.off()#> png#> 2
实战9
简单直方图添加标注线和矩形框标注
参考：https://mp.weixin.qq.com/s/5dTEKBkQ5ZG6m-c1F3x3BQ
# 数据# Creat datadf <- data.frame(x=c(0,1,2,3,4),y=c(1200,850,450,300,150))# 自定义配色# Custom Colourwaysfill_color <- "#999999"line_color <- "#000000"vline_color <- "#D55E00"rect_fill <- "#D55E00"# 优化后的直方图# Optimised histogramp91 <- ggplot(df, aes(x = x, y = y)) +# 使用黑色边框和灰色填充绘制柱状图# Drawing a bar chart with a black border and grey fillgeom_col(width = 1, color = line_color, fill = fill_color) +# 设置坐标轴标签# Setting Axis Labelslabs(x = "Niche width", y = "Number of ESVs") +# 去除背景并优化图形样式# Remove backgrounds and optimise graphic stylestheme_classic(base_size = 14) +# 设置x轴和y轴的线条# Setting the x-axis and y-axis linestheme(axis.line = element_line(color = line_color),axis.text = element_text(size = 12),axis.title = element_text(size = 14)) +# 添加一条垂直虚线辅助线# Add a vertical dashed auxiliary linegeom_vline(xintercept = 4, linetype = "dashed", color = vline_color, size = 1) +# 添加红色矩形框并设置透明度# Add a red rectangular box and set the transparencygeom_rect(aes(xmin = 4.02, xmax = 4.5, ymin = 0, ymax = 1200),fill = rect_fill, alpha = 0.2) +# 设置y轴范围以防止矩形框覆盖# Set the y-axis range to prevent the rectangular box from covering thescale_y_continuous(limits = c(0, 1300)) +# 确保x轴和y轴的比例合适，优化整体可视化效果# Ensure that the x-axis and y-axis are properly scaled to optimise the overall visualisationcoord_cartesian(clip = "off")# 保存为PDF# Save as PDFggsave(filename = "results/Barplot_stripline.pdf", p91, width = 10, height = 6)
实战10
用柱状图展示混杂因素的变异解释度
# 读取数据permanova.res <- read.csv("data/permanova_res.csv", sep = ",")# 筛选显著性结果# Screening for Significance Resultspresult <- permanova.res[permanova.res$Pvalue < 0.05, ]padj.result <- permanova.res[permanova.res$Padjust < 0.05, ]# 优化配色，符合期刊标准# Optimised colour scheme to meet journal standardsanova.cols <- c("Demography" = "#1b9e77","Disease" = "#d95f02","Life style" = "#7570b3","Medication" = "#e7298a","Physiology" = "#66a61e")# 创建柱状图并进行优化# Create bar charts and optimise themp10 <- ggplot(permanova.res, aes(x = reorder(ID, R2), y = R2, fill = Group)) +geom_bar(stat = 'identity', width = 0.7) + # 控制柱状图宽度coord_flip() +ylab("Adonis R2") +xlab("") +scale_fill_manual(values = anova.cols) + # 使用优化后的配色 Using an optimised colour scheme#theme_minimal(base_size = 14) + # 使用简洁主题，调整基础字体大小 Use the Simplicity theme and adjust the base font sizetheme_classic()+theme(axis.text.x = element_text(size = 12), # 调整x轴标签字体大小 Adjust x-axis label font sizeaxis.text.y = element_text(size = 12), # 调整y轴标签字体大小 Adjust y-axis label font sizeaxis.title.y = element_text(size = 14), # 调整y轴标题字体大小 Adjust y-axis title font sizelegend.position = "top", # 调整图例位置 Adjustment of legend positionlegend.title = element_blank()) + # 去除图例标题 Remove legend title# 添加显著性标记，黑色星号用于未校正p值，红色用于校正后的p值# Significance markers added, black asterisks for uncorrected p-values, red for corrected p-valuesgeom_text(data = presult, aes(ID, R2), label = "*", col = "black",nudge_y = 0.005, nudge_x = -0.15, size = 5) +geom_text(data = padj.result, aes(ID, R2), label = "*", col = "red",nudge_y = 0.01, nudge_x = -0.15, size = 5) +# 控制坐标范围，确保标签不会被截断# Control coordinate range to ensure labels are not truncatedscale_y_continuous(expand = c(0, 0)) +# 增加图形间距，避免文字重叠# Increase graphic spacing to avoid overlapping texttheme(plot.margin = margin(5, 5, 5, 5))# 保存为PDF# Save as PDFggsave(filename = "results/Barplot_variation.pdf", p10, width = 10, 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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