目录
热图无显著性
R
# 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
R
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
R
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
R
# 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:
