I have data with 6 rows and 4 columns. My data are assigned to variable `data`. 

I wanted to calculate PCA using `eigen` function in R:

```
S <- cov(data)
s.eigen <- eigen(S)
PCA <- data %*% s.eigen$vector
PCA
         [,1]      [,2]     [,3]      [,4]
[1,] 2.126205 -4.304502 3.865308 -2.190870
[2,] 1.909724 -3.670697 3.920960 -2.055483
[3,] 5.767312 -3.350387 4.069640 -2.175097
[4,] 4.781725 -3.338716 3.614298 -2.141244
[5,] 6.930657 -4.189078 3.887408 -2.075011
[6,] 6.054994 -4.212873 3.800341 -2.146195`

```

While, `prcomp` function returns the same rotation equal to eigen vectors, but final result of PCA is different:

```
            PC1        PC2          PC3         PC4
[1,] -2.4688982 -0.4601265 -0.005648564  0.06022000
[2,] -2.6853791  0.1736787 -0.061301164 -0.07516717
[3,]  1.1722093  0.4939885 -0.209980567  0.04444716
[4,]  0.1866223  0.5056591  0.245361119  0.01059379
[5,]  2.3355544 -0.3447025 -0.027749084 -0.05563912
[6,]  1.4598913 -0.3684974  0.059318259  0.01554534`

```

Why I get two different results for PCA using those two methods?