Demo dataset to be used for a plot.
# load a built in dataset e.g. iris flower dataset
head(iris, 10)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
# Plot
pairs(iris[, 1:4], pch = 19)
# Show only UPPER panel
pairs(iris[,1:4], pch = 19, lower.panel = NULL)
# Show only LOWER panel
pairs(iris[,1:4], pch = 19, upper.panel = NULL)
# Color points by groups (species)
my_cols <- c("#00AFBB", "#E7B800", "#FC4E07")
pairs(iris[,1:4], pch = 19, cex = 0.5,
col = my_cols[iris$Species],
lower.panel=NULL)
my_cols <- c("#00AFBB", "#E7B800", "#FC4E07")
pairs(iris[,1:4], pch = 19, cex = 0.5,
col = my_cols[iris$Species],
upper.panel=NULL)
Add correlations on the lower panels: The size of the text is proportional to the correlations.
#Add correlations on the lower panels: The size of the text is proportional to the correlations.
# Correlation panel
panel.cor <- function(x, y){
usr <- par("usr"); on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
r <- round(cor(x, y), digits=2)
txt <- paste0("R = ", r)
cex.cor <- 0.8/strwidth(txt)
text(0.5, 0.5, txt, cex = cex.cor * r)
}
# Customize upper panel
upper.panel<-function(x, y){
points(x,y, pch = 19, col = my_cols[iris$Species])
}
# Create the plots
pairs(iris[,1:4],
lower.panel = panel.cor,
upper.panel = upper.panel)
Add correlations on the scatter plots
# Customize upper panel
upper.panel<-function(x, y){
points(x,y, pch=19, col=c("red", "green3", "blue")[iris$Species])
r <- round(cor(x, y), digits=2)
txt <- paste0("R = ", r)
usr <- par("usr"); on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
text(0.5, 0.9, txt)
}
pairs(iris[,1:4], lower.panel = NULL,
upper.panel = upper.panel)
The function pairs.panels [in psych package] can be also used to create a scatter plot of matrices, with bivariate scatter plots below the diagonal, histograms on the diagonal, and the Pearson correlation above the diagonal.
library(psych)
pairs.panels(iris[,-5],
method = "pearson", # correlation method
hist.col = "#00AFBB",
density = TRUE, # show density plots
ellipses = TRUE # show correlation ellipses
)