华夫饼图简介
华夫饼图(waffle chart)分为块状华夫饼图和点状华夫饼图。它是展示总数据的组类别情况的一种有效图表。它是西方的一种由小方格组成的面包，所以这种图表因此得名为华夫饼图。
参考：https://mp.weixin.qq.com/s/9zW4EupcGSNoDH_vy23fPw https://mp.weixin.qq.com/s/0iztINEslPhhIyDXbh_I3w
标签：#微生物组数据分析 #MicrobiomeStatPlot #R语言可视化 #华夫饼图 #Waffle chartt
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/WaffleChart
或公众号后台回复“MicrobiomeStatPlot”领取
这是来自于范德堡大学医学中心Paula J. Hurley课题组2022年发表于Nature Communications上的一篇论文。论文题目为：Single cell analysis of cribriform prostate cancer reveals cell intrinsic and tumor microenvironmental pathways of aggressive disease. https://doi.org/10.1038/s41467-022-33780-1
图 1c | 患者临床特征。
结果
总体而言，患者患有 2-5 级前列腺癌，即 pT3aN0/X 期或 pT3bN0 期（图 1c 和补充表 1）。
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", "RColorBrewer", "ggforce", "dplyr", "waffle")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(RColorBrewer)))suppressWarnings(suppressMessages(library(ggforce)))suppressWarnings(suppressMessages(library(dplyr)))suppressWarnings(suppressMessages(library(waffle)))
实战
# 1. Block Waffle Chart 块状华夫饼图# Data Preparationnrows <- 10category_counts <- round(table(mpg$class) * (nrows * nrows / length(mpg$class)))sorted_counts <- sort(category_counts)categories <- names(sorted_counts)df <- expand.grid(row = 1:nrows, column = 1:nrows)df$category <- factor(rep(categories, sorted_counts), levels = categories)# Color Palettecolors <- brewer.pal(length(sorted_counts), "Paired")# Plotblock_waffle_plot <- ggplot(df, aes(x = column, y = row, fill = category)) +geom_tile(color = "grey80", size = 0.1) +scale_fill_manual(values = colors, name = "Category") +coord_fixed(ratio = 1) +scale_x_reverse(expand = c(0, 0)) +scale_y_reverse(expand = c(0, 0)) +labs(title = "Block Waffle Chart of Categories") +theme_minimal(base_size = 14) +theme(panel.grid = element_blank(),axis.text = element_blank(),axis.title = element_blank(),axis.ticks = element_blank(),legend.position = "right",legend.title = element_text(size = 12),legend.text = element_text(size = 10))# Save Plotggsave("results/block_waffle_chart.pdf", plot = block_waffle_plot, width = 8, height = 8)#2.Dot Waffle Chart 点状华夫饼图# Data Preparationdf$category <- factor(rep(categories, sorted_counts))# Plotdot_waffle_plot <- ggplot(df, aes(x0 = column, y0 = row, fill = category, r = 0.4)) +geom_circle(color = "grey40", size = 0.2) +scale_fill_manual(values = colors, name = "Category") +coord_fixed(ratio = 1) +scale_x_reverse(expand = c(0, 0)) +scale_y_reverse(expand = c(0, 0)) +labs(title = "Dot Matrix Distribution by Category") +theme_minimal(base_size = 14) +theme(panel.grid = element_blank(),legend.position = "right",legend.title = element_text(size = 12),legend.text = element_text(size = 10))# Save Plotggsave("results/dot_waffle_chart.pdf", plot = dot_waffle_plot, width = 8, height = 8)#3. Stacked Waffle Chart 堆积华夫饼图# Data Preparationunit_size <- 100category_data <- as.data.frame(table(mpg$class) * (nrows * nrows))colnames(category_data) <- c("category", "count")category_data <- arrange(category_data, desc(count))category_data$count <- category_data$count / unit_size# Expanded Data for Plottingexpanded_data <- expand.grid(row = 1:10, column = seq_len(ceiling(sum(category_data$count) / 10)))category_vector <- rep(category_data$category, category_data$count)expanded_data <- expanded_data[1:length(category_vector), ]expanded_data$category <- factor(category_vector, levels = category_data$category)# Color Palettecolors <- brewer.pal(nrow(category_data), "Set1")# Plotstacked_waffle_plot <- ggplot(expanded_data, aes(x = column, y = row, fill = category)) +geom_point(color = "black", shape = 21, size = 4) +scale_fill_manual(values = colors, name = "Category") +coord_fixed(ratio = 1) +labs(title = "Stacked Waffle Chart of Categories", x = "Each Square = 100") +theme_minimal(base_size = 14) +theme(panel.grid = element_blank(),axis.text.x = element_text(size = 10),axis.title.x = element_text(size = 12),legend.position = "right")# Save Plotggsave("results/stacked_waffle_chart.pdf", plot = stacked_waffle_plot, width = 8, height = 8)#4. Faceted Waffle Chart 分面华夫饼图# Data Preparationstorms_summary <- storms %>%filter(year >= 2010) %>%count(year, status)# Plotfaceted_waffle_plot <- ggplot(storms_summary, aes(fill = status, values = n)) +geom_waffle(color = "white", size = 0.3, n_rows = 10, flip = TRUE) +facet_wrap(~ year, nrow = 1, strip.position = "bottom") +scale_fill_brewer(palette = "Paired") +scale_x_discrete() +scale_y_continuous(labels = scales::label_number(scale = 1), expand = c(0, 0)) +labs(title = "Faceted Waffle Chart of Storms",subtitle = "Distribution of Storm Statuses (2010 onwards)",x = "Year",y = "Count") +theme_minimal(base_size = 14) +theme(panel.grid = element_blank(),axis.text.x = element_text(size = 10),axis.title.x = element_text(size = 12),legend.position = "right",legend.title = element_text(size = 12),legend.text = element_text(size = 10))# Save Plotggsave("results/faceted_waffle_chart.pdf", plot = faceted_waffle_plot, width = 12, height = 6)
排版Combo plots
组合多个子图为发表格式
library(cowplot)width = 89height = 59p0 = plot_grid(block_waffle_plot, dot_waffle_plot, stacked_waffle_plot, faceted_waffle_plot, labels = c("A", "B", "C", "D"), ncol = 2)ggsave("results/Waffle_plot.pdf", p0, width = width * 5, height = height * 4, units = "mm")
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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