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.
x1<-rnorm(20,5,2) y1<-rnorm(20,5,1) cor.test(x1,y1)
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)$estimate cor -0.08194057
x2<-runif(5000,2,5) y2<-runif(5000,2,10) cor.test(x2,y2)
Pearson's product-moment correlation data: x2 and y2 t = -1.4823, df = 4998, p-value = 0.1383 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: -0.048653479 0.006760764 sample estimates: cor -0.02096246
cor.test(x2,y2)$estimate cor 0.01301688
x3<-runif(50,2,5) y3<-runif(50,2,10) cor.test(x3,y3)
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)$estimate cor 0.1031475
x4<-rexp(500,2.1) y4<-rexp(500,5.75) cor.test(y4,y4)
Pearson's product-moment correlation data: y4 and y4 t = Inf, df = 498, p-value < 2.2e-16 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: 1 1 sample estimates: cor 1
cor.test(y4,y4)$estimate cor 1
x5<-rpois(100000,2) y5<-rpois(100000,5) cor.test(y5,y5)
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