How to extract correlation coefficient value from correlation test in R?

To perform the correlation test in R, we need to use cor.test function with two variables and it returns so many values such as test statistic value, degrees of freedom, the p-value, the confidence interval, and the correlation coefficient value. If we want to extract the correlation coefficient value from the correlation test output then estimate function could be used as shown in below examples.

Example

 Live Demo

x1<-rnorm(20,5,2)
y1<-rnorm(20,5,1)
cor.test(x1,y1)

Output

Pearson's product-moment correlation
data: x1 and y1
t = -0.13423, df = 18, p-value = 0.8947
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.4675990 0.4167308
sample estimates:
cor
-0.03162132

cor.test(x1,y1)$estimate cor -0.08194057

Example

 Live Demo

x2<-runif(5000,2,5)
y2<-runif(5000,2,10)
cor.test(x2,y2)

Output

Pearson's product-moment correlation
data: x2 and y2
t = -1.4823, df = 4998, p-value = 0.1383
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.048653479 0.006760764
sample estimates:
cor
-0.02096246

cor.test(x2,y2)$estimate cor 0.01301688

Example

 Live Demo

x3<-runif(50,2,5)
y3<-runif(50,2,10)
cor.test(x3,y3)

Output

Pearson's product-moment correlation
data: x3 and y3
t = -0.80709, df = 48, p-value = 0.4236
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.3817626 0.1680496
sample estimates:
cor
-0.1157106

cor.test(x3,y3)$estimate cor 0.1031475

Example

 Live Demo

x4<-rexp(500,2.1)
y4<-rexp(500,5.75)
cor.test(y4,y4)

Output

Pearson's product-moment correlation
data: y4 and y4
t = Inf, df = 498, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
1 1
sample estimates:
cor
1

cor.test(y4,y4)$estimate cor 1

Example

 Live Demo

x5<-rpois(100000,2)
y5<-rpois(100000,5)
cor.test(y5,y5)

Output

Pearson's product-moment correlation
data: y5 and y5
t = 1.5006e+10, df = 99998, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
1 1
sample estimates:
cor
1

cor.test(y5,y5)$estimate cor 1