Partial Correlation in the R

The Partial correlation is the correlation of two variables while controlling for a third or more other variables. The partial correlation coefficient is a measure of the strength of the linear relationship between two variables after entirely controlling for the effects of other variables.
Partial correlation is a concept closely related to correlation. It shows that fact that when we find a correlation between two variables, this doesn't necessarily mean that there is causality between them. Partial correlation quantifies the correlation between two variables when conditioned on one or several other variables. This means, when there is a correlation between two variables, the correlation might be partially explained by a third variable — the confounder (or the controlling variable), a common cause of the spurious correlation. After removing this part, what remains, is the partial correlation between those two variables.
The code
# create sample data frame
aku1= data.frame( x= c(1,2,3,4,5,6,7,7,7,8,9,12,15,20),
y= c(4,5,6,7,8,9,9,9,10,10,8,15,14,17))
cor(aku1)
# load library ppcor
library(ppcor)
# calculate Partial Correlation
pcor(aku1)
# create sample data frame
aku2 =data.frame( x= c(1,2,3,4,5,6,7,7,7,8,9,12,15,20),
y= c(4,5,6,7,8,9,9,9,10,10,8,15,14,17),
z= c(1,3,5,7,9,11,13,15,17,19,20,25,30,35))
# calculate Partial Correlation
pcor(aku2)
cor(aku2)