目录
热图无显著性
结果展示01：
热图+显著性
结果展示02：
ggplot2绘制三角热图
结果展示03：
corrplot绘制三角热图
结果展示04:
热图无显著性
# 示例数据
data(mtcars)
df <- mtcars# 计算相关矩阵
cor_matrix <- round(cor(df), 2)# reshape 成长格式
library(reshape2)
cor_df <- melt(cor_matrix)# 画热图
library(ggplot2)
ggplot(cor_df, aes(x = Var1, y = Var2, fill = value)) +geom_tile(color = "white") + ## 用色块（tiles）来构造热图geom_text(aes(label = sprintf("%.2f", value)), color = "black", family = "Times New Roman", size = 4) +scale_fill_gradient2(low = "#67a9cf", mid = "white", high = "#ef8a62",midpoint = 0, limit = c(-1, 1), name = "Correlation") +labs(title = "", x = "", y = "") +theme_bw(base_size = 14) +theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),text = element_text(family = "Times New Roman"), panel.grid = element_blank())结果展示01：
热图+显著性
install.packages(c("Hmisc", "reshape2", "ggplot2"))
library(Hmisc) # 用于计算相关性 + p 值
library(reshape2) # 数据转换
library(ggplot2) # 可视化# 加载必要包
library(Hmisc)
library(reshape2)
library(ggplot2)# 示例数据
df <- mtcars# 1. 计算相关性矩阵和显著性
res <- rcorr(as.matrix(df))
r_mat <- res$r
p_mat <- res$P# 2. 转换为长格式
r_df <- melt(r_mat)
p_df <- melt(p_mat)# 3. 添加显著性标记
p_df$signif <- cut(p_df$value,breaks = c(-Inf, 0.001, 0.01, 0.05, Inf),labels = c("***", "**", "*", ""))# 4. 合并 r 和 p
plot_df <- merge(r_df, p_df, by = c("Var1", "Var2"))# 对角线的 p 值设为空
plot_df$signif[plot_df$Var1 == plot_df$Var2] <- ""# 5. 生成标签（相关系数 + 显著性）
plot_df$label <- paste0(sprintf("%.2f", plot_df$value.x), plot_df$signif)# 6. 绘制热图
ggplot(plot_df, aes(x = Var2, y = Var1, fill = value.x)) +geom_tile(color = "white") +geom_text(aes(label = label), family = "Times New Roman", size = 4) +scale_fill_gradient2(low = "#67a9cf", mid = "white", high = "#ef8a62",midpoint = 0, limit = c(-1, 1), name = "Correlation") +labs(title = "", x = "", y = "") +theme_minimal(base_size = 14) +theme(axis.text.x = element_text(angle = 45, hjust = 1),axis.text.y = element_text(),panel.grid = element_blank(),text = element_text(family = "Times New Roman"))结果展示02：
ggplot2绘制三角热图
library(Hmisc)
library(reshape2)
library(ggplot2)# 准备数据
df <- mtcars
res <- rcorr(as.matrix(df))
r_mat <- res$r
p_mat <- res$P# 转长格式
r_df <- melt(r_mat, na.rm = FALSE)
p_df <- melt(p_mat, na.rm = FALSE)# 显著性标记
p_df$signif <- cut(p_df$value,breaks = c(-Inf, 0.001, 0.01, 0.05, Inf),labels = c("***", "**", "*", ""))# 合并
plot_df <- merge(r_df, p_df, by = c("Var1", "Var2"))
plot_df$signif[plot_df$Var1 == plot_df$Var2] <- ""
plot_df$label <- paste0(sprintf("%.2f", plot_df$value.x), plot_df$signif)# 只保留右上三角格子（包含对角线）
plot_df <- plot_df[as.numeric(plot_df$Var2) >= as.numeric(plot_df$Var1), ]# 构造对角线上方的变量名标签
diagonal_labels <- subset(plot_df, Var1 == Var2)
diagonal_labels$label <- as.character(diagonal_labels$Var1)
diagonal_labels$y_pos <- as.numeric(diagonal_labels$Var1) - 0.3 # 微微往上移# 绘图ggplot() +geom_tile(data = plot_df, aes(x = Var2, y = Var1, fill = value.x), color = "white") +geom_text(data = plot_df, aes(x = Var2, y = Var1, label = label), family = "Times New Roman", size = 4) +geom_text(data = diagonal_labels, aes(x = Var2, y = y_pos+1, label = label),family = "Times New Roman", size = 4) +scale_fill_gradient2(low = "#67a9cf", high = "#ef8a62", mid = "white",midpoint = 0, limit = c(-1, 1), name = "Correlation",labels = scales::number_format(accuracy = 0.1)) +# coord_fixed() + # 保持格子为正方形labs(title = "", x = "", y = "") +theme_minimal(base_size = 14) +expand_limits(y = max(as.numeric(plot_df$Var1)) + 1)+theme(axis.text.y = element_blank(),axis.ticks.y = element_blank(),axis.text.x = element_text(angle = 45, hjust = 1),panel.grid = element_blank(),text = element_text(family = "Times New Roman"),plot.title = element_text(hjust = 0.5))结果展示03：
corrplot绘制三角热图
# 示例数据
df <- mtcars
cor_matrix <- cor(df)par(family = "Times New Roman")corrplot(cor_matrix,method = "square", # 方格图type = "upper", # 只显示上三角diag = TRUE, # 显示对角线addCoef.col = "black", # 显示相关系数数字number.cex = 0.7, # 数值大小tl.col = "black", # 标签颜色tl.cex = 0.8, # 标签字体大小tl.srt = 45, # x轴标签角度（支持 45°/60° 等）col = colorRampPalette(c("#67a9cf", "white", "#ef8a62"))(200),mar = c(0,0,2,0) # 边距微调
)结果展示04:
