R t-test cor.test

a = c(175, 168, 168, 190, 156, 181, 182, 175, 174, 179)
b = c(185, 169, 173, 173, 188, 186, 175, 174, 179, 180)
x<-t.test(a,b)
str(x)
class(x)

> str(x)
List of 9
 $ statistic  : Named num -0.947
  ..- attr(*, "names")= chr "t"
 $ parameter  : Named num 16
  ..- attr(*, "names")= chr "df"
 $ p.value    : num 0.358
 $ conf.int   : num [1:2] -11.01 4.21
  ..- attr(*, "conf.level")= num 0.95
 $ estimate   : Named num [1:2] 175 178
  ..- attr(*, "names")= chr [1:2] "mean of x" "mean of y"
 $ null.value : Named num 0
  ..- attr(*, "names")= chr "difference in means"
 $ alternative: chr "two.sided"
 $ method     : chr "Welch Two Sample t-test"
 $ data.name  : chr "a and b"
 - attr(*, "class")= chr "htest"
> class(x)
[1] "htest"
> x$p.value
[1] 0.3575549
> x$estimate
mean of x mean of y
    174.8     178.2
> x$estimate[1]
mean of x
    174.8
> x$estimate[2]
mean of y
    178.2
>
========================

> x<-cor.test(a,b)
> str(x)
List of 9
 $ statistic  : Named num -0.714
  ..- attr(*, "names")= chr "t"
 $ parameter  : Named int 8
  ..- attr(*, "names")= chr "df"
 $ p.value    : num 0.496
 $ estimate   : Named num -0.245
  ..- attr(*, "names")= chr "cor"
 $ null.value : Named num 0
  ..- attr(*, "names")= chr "correlation"
 $ alternative: chr "two.sided"
 $ method     : chr "Pearson's product-moment correlation"
 $ data.name  : chr "a and b"
 $ conf.int   : num [1:2] -0.758 0.455
  ..- attr(*, "conf.level")= num 0.95
 - attr(*, "class")= chr "htest"
> x$p.value
[1] 0.4955273
> x$estimate
       cor
-0.2447594
>