Scatterplot matrix with ggpairs()
The ggpairs() function of the GGally package allows to build a great scatterplot matrix.
Scatterplots of each pair of numeric variable are drawn on the left part of the figure. Pearson correlation is displayed on the right. Variable distribution is available on the diagonal.
# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables
library(GGally)
# Create data
data <- data.frame( var1 = 1:100 + rnorm(100,sd=20), v2 = 1:100 + rnorm(100,sd=27), v3 = rep(1, 100) + rnorm(100, sd = 1))
data$v4 = data$var1 ** 2
data$v5 = -(data$var1 ** 2)
# Check correlations (as scatterplots), distribution and print corrleation coefficient
ggpairs(data, title="correlogram with ggpairs()") Visualize correlation with ggcorr()
The ggcorr() function allows to visualize the correlation of each pair of variable as a square. Note that the method argument allows to pick the correlation type you desire.
# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables
library(GGally)
# Create data
data <- data.frame( var1 = 1:100 + rnorm(100,sd=20), v2 = 1:100 + rnorm(100,sd=27), v3 = rep(1, 100) + rnorm(100, sd = 1))
data$v4 = data$var1 ** 2
data$v5 = -(data$var1 ** 2)
# Check correlation between variables
#cor(data)
# Nice visualization of correlations
ggcorr(data, method = c("everything", "pearson")) Split by group
It is possible to use ggplot2 aesthetics on the chart, for instance to color each category.
# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables
library(GGally)
# From the help page:
data(flea)
ggpairs(flea, columns = 2:4, ggplot2::aes(colour=species)) Change plot types
Change the type of plot used on each part of the correlogram. This is done with the
upper and lower argument.
# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables
library(GGally)
# From the help page:
data(tips, package = "reshape")
ggpairs(
tips[, c(1, 3, 4, 2)],
upper = list(continuous = "density", combo = "box_no_facet"),
lower = list(continuous = "points", combo = "dot_no_facet")
)
