備註：ggplot2本身的作圖語法本篇不涉及，基礎部分還請自行學習了。
示例檔案、R指令碼等的百度盤連結：
https://pan.baidu.com/s/1KUA5owf4y13y1RJbff-l7g
“pca_site.txt”是某PCA分析後，匯出的樣方（物件）排序座標，“pca_var.txt”是變數的座標。
“cap_site.txt”是某db-RDA（CAP）分析後，匯出的樣方（物件）排序座標，“cap_sp.txt”是物種（響應變數）排序座標，“cap_env.txt”是環境變數（解釋變數）的排序座標。
先以某PCA排序圖為例吧。
library(ggplot2)
#資料
pca_site <- read.delim('pca_site.txt', sep = '\t', stringsAsFactors = FALSE)
pca_site$group_all <- paste(pca_site$group1, pca_site$group2, sep = '_')
pca_var <- read.delim('pca_var.txt', sep = '\t', stringsAsFactors = FALSE)
常規的只展示物件的排序圖，PCA前兩軸，顏色區分分組，這裡假設原分析中前兩軸的貢獻率分別為30%和20%（貢獻率由實際的計算獲得）。
#常規僅樣方的點圖，單組樣式
p <- ggplot(data = pca_site, aes(x = PC1, y = PC2)) +
theme(panel.grid = element_blank(), panel.background = element_rect(color = 'black', fill = 'transparent'), legend.key = element_rect(fill = 'transparent')) +
labs(x = 'PCA1: 30%', y = 'PCA2: 20%')
p + geom_point(aes(color = group1)) +
scale_color_manual(values = c('red2', 'purple2', 'green3'))
如果存在多分組，可以分別以更多的顏色區分，或者點的形狀等。
#多組可以顏色表示
p + geom_point(aes(color = group_all)) +
scale_color_manual(values = c('red', 'purple', 'green', 'red4', 'purple4', 'green4'), limits = c('A_l', 'B_l', 'C_l', 'A_h', 'B_h', 'C_h'))
#或者使用點的顏色+形狀區分
p + geom_point(aes(color = group1, shape = group2)) +
scale_color_manual(values = c('red2', 'purple2', 'green3'), limits = c('A', 'B', 'C')) +
scale_shape_manual(values = c(17, 15), limits = c('l', 'h'))
#漸變色表示連續數值
p + geom_point(aes(color = group3, shape = group1)) +
scale_shape_manual(values = c(19, 17, 15), limits = c('A', 'B', 'C')) +
scale_color_gradientn(colors = colorRampPalette(c('#C0D857', '#3EB897', '#663490'))(7))
一些背景網格線的常選樣式。
#以上述某樣式繼續為例展示
p <- p + geom_point(aes(color = group1, shape = group2)) +
scale_color_manual(values = c('red2', 'purple2', 'green3'), limits = c('A', 'B', 'C')) +
scale_shape_manual(values = c(17, 15), limits = c('l', 'h'))
#一些常見背景樣式
p + theme(axis.line = element_line(colour = 'black'), panel.background = element_blank())
p + geom_vline(xintercept = 0, color = 'gray', size = 0.5) +
geom_hline(yintercept = 0, color = 'gray', size = 0.5)
p + theme(panel.grid.major = element_line(color = 'gray', size = 0.2))
有時能夠看到直接在原圖中標記物件名稱的。
#新增標籤，常規方法
p + geom_text(aes(label = name, color = group1), size = 3, vjust = 2, show.legend = FALSE)
#防止重疊的新增標籤方法
library(ggrepel)
p + geom_text_repel(aes(label = name, color = group1), size = 3, box.padding = unit(0.5, 'lines'), show.legend = FALSE)
p + geom_label_repel(aes(label = name, color = group1), size = 3, box.padding = unit(0.5, 'lines'), show.legend = FALSE)
這種在圖中，根據樣本新增分組橢圓或者多邊形的樣式，經常能看到。
#新增置信橢圓，注意它不是聚類
p + stat_ellipse(aes(color = group1), level = 0.95, linetype = 2, show.legend = FALSE)
p + stat_ellipse(aes(fill = group1), geom = 'polygon', level = 0.95, alpha = 0.1, show.legend = FALSE) +
scale_fill_manual(values = c('red', 'purple', 'green'))
#“偽聚類”，我也不知道怎麼稱呼，既不是真正的聚類也不是置信區間，僅用於標記分組而已
#這種樣式也經常看到吧
#參考自：https://stackoverflow.com/questions/42575769/how-to-modify-the-backgroup-color-of-label-in-the-multiple-ggproto-using-ggplot2
source('geom_enterotype.r') #見網盤附件
p + geom_enterotype(aes(fill = group1, color = group1, label = group1), show.legend = FALSE) +
scale_fill_manual(values = c('#ffa6a9', '#e8b4fd', '#c7ffc4'))
#還有分組多邊形這種
library(plyr)
group1_border <- ddply(pca_site, 'group1', function(df) df[chull(df[[2]], df[[3]]), ])
p + geom_polygon(data = group1_border, aes(fill = group1, color = group1), alpha = 0.1, show.legend = FALSE) +
scale_fill_manual(values = c('red', 'purple', 'green'))
圖中額外新增一些識別符號、標籤或者特殊形狀之類的，一般推薦後續用AI、PS等P圖上去更為方便；如果用程式碼新增的話，以一些文字為例。
#額外新增一些標籤等，例如
p + stat_ellipse(aes(fill = group1), geom = 'polygon', level = 0.95, alpha = 0.1, show.legend = FALSE) +
scale_fill_manual(values = c('red', 'purple', 'green')) +
annotate('text', label = 'A', x = -0.15, y = -0.10, size = 5, color = 'red2') +
annotate('text', label = 'B', x = 0.20, y = 0.09, size = 5, color = 'purple2') +
annotate('text', label = 'C', x = -0.20, y = -0.05, size = 5, color = 'green3') +
annotate('text', label = 'PERMANOVA: P<0.001', x = 0.2, y = 0.14, size = 3)
以上均展示了物件的位置，平常見到的大多數PCA排序圖也只展示了物件；如果期望將變數也呈現出來。
#新增變數，PCA 這種線性模型中，變數常以向量形式展示
p + geom_segment(data = pca_var, aes(x = 0, y = 0, xend = PC1, yend = PC2), arrow = arrow(length = unit(0.1, 'cm')), color = 'blue', size = 0.3) +
geom_text(data = pca_var, aes(x = PC1 * 1.1, y = PC2 * 1.1, label = name), color = 'blue', size = 3)
#CA 這種單峰模型中，變數則常直接展示為點，這裡順便以該示例資料作個展示，假設它是個 CA 的資料
p + geom_text(data = pca_var, aes(x = PC1, y = PC2, label = name), color = 'blue', size = 3) +
labs(x = 'CA1', y = 'CA2')
組合一下上述提到的方法，隨便來一個。
#象徵性來個組合樣式的
p <- ggplot(data = pca_site, aes(x = PC1, y = PC2)) +
geom_point(aes(color = group1, shape = group2)) +
geom_enterotype(aes(fill = group1, color = group1, label = group1), show.legend = FALSE) +
scale_color_manual(values = c('red2', 'purple2', 'green3'), limits = c('A', 'B', 'C')) +
scale_shape_manual(values = c(17, 15), limits = c('l', 'h')) +
scale_fill_manual(values = c('#ffa6a9', '#e8b4fd', '#c7ffc4')) +
guides(color = FALSE) +
theme(panel.grid.major = element_line(color = 'gray', size = 0.2), panel.background = element_rect(color = 'black', fill = 'transparent'),
legend.title = element_blank(), legend.key = element_rect(fill = 'transparent'), legend.position = c(0.92, 0.87), legend.background= element_blank()) +
labs(x = 'PCA1: 30%', y = 'PCA2: 20%') +
annotate('text', label = 'PERMANOVA: P<0.001', x = 0.2, y = 0.14, size = 3)
p
以及偶爾也能看到和其它圖形組合在一起的，例如結合箱線圖表示物件的分佈更為直觀。
#在圖的兩側新增箱線圖展示點的分佈
pc1_boxplot <- ggplot(data = pca_site, aes(x = group1, y = PC1)) +
geom_boxplot(aes(color = group1)) +
scale_color_manual(values = c('red2', 'purple2', 'green3'), limits = c('A', 'B', 'C')) +
theme(panel.grid = element_blank(), panel.background = element_rect(color = 'black', fill = 'transparent'),
legend.position= 'none', axis.title = element_text(color = 'transparent')) +
coord_flip()
pc2_boxplot <- ggplot(data = pca_site, aes(x = group1, y = PC2)) +
geom_boxplot(aes(color = group1)) +
scale_color_manual(values = c('red2', 'purple2', 'green3'), limits = c('A', 'B', 'C')) +
theme(panel.grid = element_blank(), panel.background = element_rect(color = 'black', fill = 'transparent'),
legend.position= 'none', axis.title = element_text(color = 'transparent'))
library(grid)
grid.newpage()
pushViewport(viewport(layout = grid.layout(3, 3)))
print(p, vp = viewport(layout.pos.row = 1:2, layout.pos.col = 1:2))
print(pc1_boxplot, vp = viewport(layout.pos.row = 3, layout.pos.col = 1:2))
print(pc2_boxplot, vp = viewport(layout.pos.row = 1:2, layout.pos.col = 3))
#這裡用了 grid 包實現組合，還有很多 R 包可用於組合，還請自行了解了
#如果覺得對齊效果不太好，在 print() 函數里除錯引數對齊
#或者後面用 AI、PS 調整下更方便
這裡以某db-RDA（CAP）的計算座標為例吧。
基本的作圖方法上文已作了描述，這裡直接先展示一例只包含樣方和環境變數的雙序圖，假設原分析中前兩個約束軸的解釋率分別為25%和15%（約束軸的解釋率由實際的計算獲得）。
library(ggplot2)
#資料
cap_site <- read.delim('cap_site.txt', sep = '\t', stringsAsFactors = FALSE)
cap_sp <- read.delim('cap_sp.txt', sep = '\t', stringsAsFactors = FALSE)
cap_env <- read.delim('cap_env.txt', sep = '\t', stringsAsFactors = FALSE)
#作圖，雙序圖
p <- ggplot(data = cap_site, aes(x = CAP1, y = CAP2)) +
geom_point(aes(color = group)) +
stat_ellipse(aes(fill = group), geom = 'polygon', level = 0.95, alpha = 0.1, show.legend = FALSE) +
scale_color_manual(values = c('red2', 'purple2', 'green3'), limits = c('A', 'B', 'C')) +
scale_shape_manual(values = c(17, 15), limits = c('l', 'h')) +
scale_fill_manual(values = c('red', 'purple', 'green')) +
theme(panel.grid = element_blank(), panel.background = element_rect(color = 'black', fill = 'transparent'),
legend.title = element_blank(), legend.key = element_rect(fill = 'transparent')) +
geom_vline(xintercept = 0, color = 'gray', size = 0.5) +
geom_hline(yintercept = 0, color = 'gray', size = 0.5) +
geom_segment(data = cap_env, aes(x = 0, y = 0, xend = CAP1, yend = CAP2), arrow = arrow(length = unit(0.2, 'cm')), color = 'blue', size = 0.3) +
geom_text(data = cap_env, aes(x = CAP1 * 1.1, y = CAP2 * 1.1, label = name), color = 'blue', size = 3) +
labs(x = 'CAP1: 25%', y = 'CAP2: 15%')
p
帶樣方、物種和環境變數的三序圖。
#考慮將物種變數加入，展示為三序圖
p + geom_segment(data = cap_sp, aes(x = 0, y = 0, xend = CAP1, yend = CAP2), arrow = arrow(length = unit(0.1, 'cm')), color = 'orange', size = 0.2) +
geom_text(data = cap_sp, aes(x = CAP1 * 1.1, y = CAP2 * 1.1, label = name), color = 'orange', size = 2)
#物種也用顏色區分下分類
#物種數量太多了，這裡選部分物種演示下好了
cap_sp_select <- cap_sp[1:10, ]
p + geom_segment(data = cap_sp_select, aes(x = 0, y = 0, xend = CAP1, yend = CAP2, color = sp_class),
arrow = arrow(length = unit(0.1, 'cm')), size = 0.2, show.legend = FALSE) +
geom_text(data = cap_sp_select, aes(x = CAP1 * 1.1, y = CAP2 * 1.1, label = name, color = sp_class), size = 2) +
scale_color_manual(values = c('red2', 'purple2', 'green3', '#8DD3C7', '#BEBADA', '#FB8072', '#80B1D3'),
limits = c('A', 'B', 'C', 'pa', 'pb', 'pr', 'a'))
#物種直接以點展示，而非向量
p + geom_point(data = cap_sp_select, aes(x = CAP1, y = CAP2), color = 'orange', size = 1)
p + geom_text(data = cap_sp_select, aes(x = CAP1, y = CAP2, label = name), color = 'orange', size = 2)
