The best example to plot a correlation graph with ggplot2

Question

I made a partial correlation analysis, with the ggm package

list = list(mtcars, mtcars)
    list = lapply(list, function(x) x %>%
                    mutate(gear = as.factor(gear)))

library(ggm)
lapply(list, function(x) {
  sapply(split(x, x$gear), function(x) {
    pcor(u = c('mpg', 'disp', 'hp', 'vs'), S = var(x))
  })
})

and with the pcor package

pcorr1 = list %>% 
  map(function(x) split(x[c('mpg', 'disp', 'hp', 'vs')], x$gear))
coeff = c("pearson", "spearman")
res = lapply(1:2, function(x) lapply(seq(coeff), function(x) {
  lapply(pcorr1[[x]], function(y) pcor(y, method = coeff[[x]]))}))

Can anyone recommend a way how to compute such correlation in a graph with ggplot2?

Thanks

UPFATE Just to make understand I am wondering wether it is possible to use the correlation coefficients as y and on x all level of grouping variable (it should be a sort of barplot)

Answer 1

I'm having trouble understanding your expected output, but perhaps you could use pairs plots to show the correlation between variables and label each plot with your ggm::pcor() value? E.g.

library(tidyverse)
# install.packages("ggm")
library(ggm)

list_of_mtcars = list(mtcars, mtcars)
list_to_plot = lapply(list_of_mtcars, function(x) x %>%
                mutate(gear = as.factor(gear)))

ggm_pcor_vals <- lapply(list_to_plot, function(x) {
  sapply(split(x, x$gear), function(x) {
    pcor(u = c('mpg', 'disp', 'hp', 'vs'), S = var(x))
  })
})

pcorr1 <- list_to_plot %>% 
  map(function(x) split(x[c('mpg', 'disp', 'hp', 'vs')], x$gear))
coeff <- c("pearson", "spearman")
res <- lapply(1:2, function(x) lapply(seq(coeff), function(x) {
  lapply(pcorr1[[x]], function(y) cor(y, method = coeff[x]))}))

library(GGally)
#> Registered S3 method overwritten by 'GGally':
#>   method from   
#>   +.gg   ggplot2
map(1:3, \(y) map(pcorr1,
     \(x) ggpairs(as.data.frame(x[[y]])) + 
       ggtitle(paste("ggm pcor mpg vs disp given hp and vs =", round(ggm_pcor_vals[[1]][[y]], 4), "when gear =", names(x)[[y]]))))
#> [[1]]
#> [[1]][[1]]

#> 
#> [[1]][[2]]

#> 
#> 
#> [[2]]
#> [[2]][[1]]

#> 
#> [[2]][[2]]

#> 
#> 
#> [[3]]
#> [[3]][[1]]

#> 
#> [[3]][[2]]

^{Created on 2023-09-05 with reprex v2.0.2}

Or is this totally off the mark?

NB. it's better to avoid using the word list as a name for your list, and also it looks like you have 'duplicate' plots in the map() output, but I think it's because you have list(mtcars, mtcars) as your input.

Edit 1

Based on your update, it sounds like you want a barplot of the pcor values? What do you want your barplot to look like? I.e. how would you change this:

library(tidyverse)
# install.packages("ggm")
library(ggm)

list_of_mtcars = list(mtcars, mtcars)
list_to_plot = lapply(list_of_mtcars, function(x) x %>%
                        mutate(gear = as.factor(gear)))

ggm_pcor_vals <- lapply(list_to_plot, function(x) {
  sapply(split(x, x$gear), function(x) {
    pcor(u = c('mpg', 'disp', 'hp', 'vs'), S = var(x))
  })
})

par(mfrow = c(1, 2))
lapply(ggm_pcor_vals, barplot)

^{Created on 2023-09-08 with reprex v2.0.2}

Answer 2

library(tidyverse)
library(corrplot)

(split_mtcars <- 
  select(mtcars,c('mpg', 'disp', 'hp', 'vs')) |> 
  split(mtcars$gear))

(corrs_from_ds <- map(split_mtcars,
                        cor))
imap(corrs_from_ds,
    \(x,y)corrplot::corrplot(x,title=paste0("Gear :",y),
                             mar=c(0,0,1,0)))