getting "PC1" insted of variable name in principal component analysis

Question

I have some data that looks like this:

head(data)
   net1re net2re net3re net4re net5re net6re
24      3      2      1      2      3      3
33      1      1      1      1      1      2
30      3      1      1      1      1      3
22      2      1      1      1      1      1
31      3      2      1      1      1      2
1       2      1      1      1      1      2

I'm running principal component analysis as follows:

library(psych)
fit <- principal(data[,1:6], rotate="varimax")
data$friendship=fit$scores

This creates the variable "friendship" which I can call on the console:

> colnames(data)
[1] "net1re"     "net2re"     "net3re"     "net4re"     "net5re"    
[6] "net6re"     "friendship"

But when I want to view my data, instead of the variable name I get "PC1":

> head(data)
   net1re net2re net3re net4re net5re net6re         PC1
24      3      2      1      2      3      3  1.29231531
33      1      1      1      1      1      2 -0.68448111
30      3      1      1      1      1      3  0.02783916
22      2      1      1      1      1      1 -0.67371031
31      3      2      1      1      1      2  0.10251282
1       2      1      1      1      1      2 -0.44345075

This becomes a major trouble because I need to repeat that with diffrent variables and all the results get "PC1".

Why is this happening and how can I assign the variable name instead of "PC1".

Thanks

Answer 1

This unusual effect appears becausefit$scores is a matrix:

str(data)
#'data.frame':  6 obs. of  7 variables:
# $ net1re    : int  3 1 3 2 3 2
# $ net2re    : int  2 1 1 1 2 1
# $ net3re    : int  1 1 1 1 1 1
# $ net4re    : int  2 1 1 1 1 1
# $ net5re    : int  3 1 1 1 1 1
# $ net6re    : int  3 2 3 1 2 2
# $ friendship: num [1:6, 1] 1.1664 -1.261 0.0946 -0.5832 1.1664 ...
#  ..- attr(*, "dimnames")=List of 2
#  .. ..$ : chr  "24" "33" "30" "22" ...
#  .. ..$ : chr "PC1"

To get the desired result, you can use

data$friendship=as.vector(fit$scores)

or

data$friendship=fit$scores[,1]

In either case, the output will be:

data
#   net1re net2re net3re net4re net5re net6re  friendship
#24      3      2      1      2      3      3  1.16635312
#33      1      1      1      1      1      2 -1.26098965
#30      3      1      1      1      1      3  0.09463653

str(data)
#'data.frame':  6 obs. of  7 variables:
# $ net1re    : int  3 1 3 2 3 2
# $ net2re    : int  2 1 1 1 2 1
# $ net3re    : int  1 1 1 1 1 1
# $ net4re    : int  2 1 1 1 1 1
# $ net5re    : int  3 1 1 1 1 1
# $ net6re    : int  3 2 3 1 2 2
# $ friendship: num  1.1664 -1.261 0.0946 -0.5832 1.1664 ...