# Scatter Plot Matrices - R Base Graphs

Previously, we described the essentials of R programming and provided quick start guides for importing data into R.

Here, we’ll describe how to produce a matrix of scatter plots. This is useful to visualize correlation of small data sets. The R base function pairs() can be used.

1. Launch RStudio as described here: Running RStudio and setting up your working directory

2. Prepare your data as described here: Best practices for preparing your data and save it in an external .txt tab or .csv files

3. Import your data into R as described here: Fast reading of data from txt|csv files into R: readr package.

Here, we’ll use the R built-in iris data set.

# Data

iris data is used in the following examples. iris data set gives the measurements in centimeters of the variables sepal length and width, and petal length and width, respectively, for 50 flowers from each of 3 species of iris. The species are Iris setosa, versicolor, and virginica.

``head(iris)``
``````##   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``````

# R base scatter plot matrices: pairs()

• Basic plots:
``pairs(iris[,1:4], pch = 19)`` • Show only upper panel:
``pairs(iris[,1:4], pch = 19, lower.panel = NULL)`` Note that, to keep only lower.panel, use the argument 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)`````` • 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)`````` # Use the R package psych

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
)`````` If lm = TRUE, linear regression fits are shown for both y by x and x by y. Correlation ellipses are also shown. Points may be given different colors depending upon some grouping variable.

# Infos

This analysis has been performed using R statistical software (ver. 3.2.4).

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