拿到一个陌生的图，第一步肯定得查查看是否有文献来源。于是，我用百度/谷歌识图搜索了一下，好家伙，不搜不知道，一搜吓一跳，19年Science发表过类似的图片后，各路大神都在复现，甚至还开发了专门的R包。
既然有现成的包，那咱也不客气了，直接拿来用就是了。
安装 ggcor
# Gitee地址安装(试了好几个) devtools::install_git("https://gitee.com/dr_yingli/ggcor") library(ggcor) library(ggplot2)
相关性图
# 以mtcars 数据集为例 # 不同的图形代码如下 library(patchwork) # layer of tile A <- quickcor(mtcars) + geom_colour() # layer of circle and trim the lower triangle B <- quickcor(mtcars, type = "upper") + geom_circle2() # layer of ellipse and not show diagonal C <- quickcor(mtcars, type = "lower", show.diag = FALSE) + geom_ellipse2() # layer of square and reorder correlation matrix by cluster D <- quickcor(mtcars, cluster = TRUE) + geom_square() # layer of confidence box E <- quickcor(mtcars, cor.test = TRUE) + geom_confbox() # different layer of upper/lower triangle F <- quickcor(mtcars, cor.test = TRUE) + geom_square(data = get_data(type = "lower", show.diag = FALSE)) + geom_mark(data = get_data(type = "upper", show.diag = FALSE)) + geom_abline(slope = -1, intercept = 12) (A+B+C)/(E+D+F)+ plot_annotation(tag_levels = 'A')
组合相关性图
library(dplyr) library(vegan) library(ggplot2) # 载入示例数据 data("varechem", package = "vegan") data("varespec", package = "vegan") # Mantel.test 检验计算矩阵相关性 mantel <- mantel_test(varespec, varechem, mantel.fun = 'mantel.randtest',spec.dist.method = 'bray', env.dist.method = 'euclidean', spec.select = list(Spec01 = 1:7, Spec02 = 8:18, Spec03 = 19:37 )) %>% mutate(r_value = cut(r, breaks = c(-Inf, 0.25, 0.5, Inf), labels = c('<0.25', '0.25-0.5', '>=0.5'), right = FALSE), p_value = cut(p.value, breaks = c(-Inf, 0.001, 0.01, 0.05, Inf), labels = c('<0.001', '0.001-0.01', '0.01-0.05', '>=0.05'), right = FALSE)) quickcor(varechem, type = "upper") + geom_square() + anno_link(aes(colour = p_value, size = r_value), data = mantel) + scale_size_manual(values = c(0.5, 1, 2)) + scale_colour_manual(values = c("#D95F02", "#1B9E77", "#A2A2A288")) + guides(size = guide_legend(title = "Mantel's r", override.aes = list(colour = "grey35"), order = 2), colour = guide_legend(title = "Mantel's p", override.aes = list(size = 3), order = 1), fill = guide_colorbar(title = "Pearson's r", order = 3))
环状热图
# 需要安装ambient包 install.packages('ambient') library(ambient) rand_correlate(100, 8) %>% ## require ambient packages quickcor(circular = TRUE, cluster = TRUE, open = 45) + geom_colour(colour = "white", size = 0.125) + anno_row_tree() + anno_col_tree() + set_p_xaxis() + set_p_yaxis()
参考
houyunhuang/ggcor · GitHub
dr.yingli/ggcor (gitee.com)
