knitr::opts_chunk$set( message = FALSE, collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", fig.align = "center", out.width = "100%" )
The goal of ggcor
is to provide a set of functions that can be used to visualize a correlation matrix quickly.
Now ggcor
is not on cran, You can install the development version of ggcor from GitHub with:
# install.packages("devtools") devtools::install_github("houyunhuang/ggcor")
If you are in the mainland of China, you can install ggcor from Gitee with:
# install.packages("devtools") devtools::install_git("https://gitee.com/houyunhuang/ggcor.git")
library(ggplot2) library(ggcor) set_scale() quickcor(mtcars) + geom_square() quickcor(mtcars, type = "upper") + geom_circle2() 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), size = 2.5) + geom_abline(slope = -1, intercept = 12)
library(dplyr) data("varechem", package = "vegan") data("varespec", package = "vegan") mantel <- mantel_test(varespec, varechem, spec.select = list(Spec01 = 1:7, Spec02 = 8:18, Spec03 = 19:37, Spec04 = 38:44)) %>% mutate(rd = cut(r, breaks = c(-Inf, 0.2, 0.4, Inf), labels = c("< 0.2", "0.2 - 0.4", ">= 0.4")), pd = cut(p.value, breaks = c(-Inf, 0.01, 0.05, Inf), labels = c("< 0.01", "0.01 - 0.05", ">= 0.05"))) quickcor(varechem, type = "upper") + geom_square() + anno_link(aes(colour = pd, size = rd), 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))
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()
d1 <- rand_dataset(20, 30) %>% gcor_tbl(cluster = TRUE) p <- matrix(sample(LETTERS[1:4], 90, replace = TRUE), nrow = 30, dimnames = list(paste0("sample", 1:30), paste0("Type", 1:3))) %>% gcor_tbl(name = "Type", row.order = d1) %>% qheatmap(aes(fill = Type)) + coord_fixed() + remove_y_axis() d2 <- data.frame(x = sample(paste0("var", 1:20), 200, replace = TRUE)) set_scale() quickcor(d1) + geom_colour(aes(fill = value)) + anno_hc_bar(width = 1) + anno_row_custom(p) + anno_row_tree() + anno_hc_bar(pos = "top") + anno_bar(d2, aes(x = x), height = 0.12) + anno_col_tree(height = 0.12)
To cite the ggcor
package in publications use:
Houyun Huang, Lei Zhou, Jian Chen and Taiyun Wei(2020). ggcor: Extended tools for correlation analysis and visualization. R package version 0.9.7.
A BibTeX entry for LaTeX users is
@Manual{ entry = "manual", title = {ggcor: Extended tools for correlation analysis and visualization}, author = {Houyun Huang, Lei Zhou, Jian Chen and Taiyun Wei}, year = {2020}, note = {R package version 0.9.7}, url = {https://github.com/houyunhuang/ggcor}, }
The above citation information can be generated by calling citation("ggcor")
in R.
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