ggcor

The ggcor package can be used to visualize simply and directly a correlation matrix based on ‘ggplot2’. It provides a solution for reordering the correlation matrix, displaying the different significance level on the plot and other details. The most important parts, It also provides a graphical display of any correlation analysis and their combination (such as Mantel test, Partial correlation analysis, and so on).

Installation

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")

Draw correlation plot quickly

This is a basic example which shows you how to draw correlation plot quickly:

library(ggplot2)
library(ggcor)
#> Registered S3 method overwritten by 'ggcor':
#>   method            from
#>   print.correlation nlme
quickcor(mtcars) + geom_colour()

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)

Grouped by rows

grp <- mtcars$gear
quickcor(mtcars[-10], type = "lower", group = grp) + 
  geom_colour() +
  add_diag_label(hjust = 0) +
  expand_axis(x = 12) +
  remove_axis("x") +
  facet_wrap(~.group)
#> Warning in cor(x, y, use = use, method = method): 标准差为零

#> Warning in cor(x, y, use = use, method = method): 标准差为零

Mantel test plot

library(vegan)
#> Loading required package: permute
#> Loading required package: lattice
#> This is vegan 2.5-6
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
data("varechem")
data("varespec")
set.seed(20191224)
sam_grp <- sample(paste0("sample", 1:3), 24, replace = TRUE)
mantel01 <- fortify_mantel(varespec, varechem, group = sam_grp,
                           spec.select = list(spec01 = 1:5, 
                                              spec02 = 6:12,
                                              spec03 = 7:18,
                                              spec04 = 20:29,
                                              spec05 = 30:44),
                           mantel.fun = "mantel.randtest")
quickcor(mantel01, legend.title = "Mantel's r") + 
  geom_colour() + geom_cross() + facet_grid(rows = vars(.group))

mantel02 <- fortify_mantel(varespec, varechem, 
                         spec.select = list(1:10, 5:14, 7:22, 9:32)) %>% 
  mutate(r = 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() + 
  add_link(mantel02, mapping = aes(colour = p.value, size = r),
           diag.label = TRUE) +
  scale_size_manual(values = c(0.5, 1.5, 3)) +
  add_diag_label() + remove_axis("x")

network

library(tidygraph)
#> 
#> Attaching package: 'tidygraph'
#> The following object is masked from 'package:stats':
#> 
#>     filter
library(ggraph)
net <- fast_correlate(varespec) %>% 
  as_tbl_graph(r.thres = 0.5, p.thres = 0.05) %>% 
  mutate(degree = tidygraph::centrality_degree(mode = "all"))
#> 

ggraph(net, "circle") + 
  geom_edge_fan(aes(edge_width = r, edge_linetype = r < 0), 
                edge_colour = "grey80", show.legend = FALSE) +
  geom_node_point(aes(size = degree, colour = name), 
                  show.legend = FALSE) +
  geom_node_text(aes(x = x * 1.08, y = y * 1.08, 
                     angle = -((-node_angle(x, y) + 90) %% 180) + 90, 
                     label = name), size = 4, hjust= "outward",
                 show.legend = FALSE)  +
  scale_edge_color_gradientn(colours = c("blue", "red")) +
  scale_edge_width_continuous(range = c(0.1, 1)) +
  coord_fixed(xlim = c(-1.5, 1.5), ylim = c(-1.5, 1.5)) +
  theme_graph()