Does factor in Stata do the same thing as princomp in R?

Edit: for spelling and based on initial response, because I am still struggling with the syntax

I need to recreate a variable in R that was created in Stata using the factor command.

I am looking for the analogous command in R to Stata's factor command, written for example as:

factor v1 v2 v3 v4 v5 
score newvar1

which, according to this Stata documentation, is a principal-factor analysis.

I am trying to use the factor analysis fa() command in R do the same thing. However, I am struggling with the syntax. I think the analogous R code to the Stata code above should look something like this:

library(psych)    
df$newvar1 <- fa(df, values = c(v1, v2, v3, v4, v5))

However, when I trial it out using the mtcars dummy data, I do not get a factor variable as a result:

mtcars <- mtcars
library('psych')
mtcars$newvar1 <- fa(mtcars, values=c(mpg, cyl, hp))

> Error in $<-.data.frame(*tmp*, fa, value = list(residual =
> c(0.124759140589603, : replacement has 49 rows, data has 32 In
> addition: Warning message: In fa.stats(r = r, f = f, phi = phi, n.obs
> = n.obs, np.obs = np.obs, : The estimated weights for the factor scores are probably incorrect. Try a different factor score
> estimation method.

Am I missing some argument, or am I misinterpreting the syntax?

Here is the equivalent in R:

library(psych)
set.seed(123)
data(mtcars)
f <- fa(mtcars[,c("mpg", "disp", "hp", "qsec")], nfactors=1, SMC=TRUE, fm="pa", rotate="none", max.iter=1)
#> maximum iteration exceeded
loadings(f)
#> 
#> Loadings:
#>      PA1   
#> mpg  -0.860
#> disp  0.872
#> hp    0.928
#> qsec -0.633
#> 
#>                 PA1
#> SS loadings    2.76
#> Proportion Var 0.69

^{Created on 2023-01-10 by the reprex package (v2.0.1)}

Stata:

. factor mpg disp hp qsec, pf factors(1)
(obs=32)

Factor analysis/correlation                      Number of obs    =         32
    Method: principal factors                    Retained factors =          1
    Rotation: (unrotated)                        Number of params =          4

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      2.75999      2.42226            0.9543       0.9543
        Factor2  |      0.33773      0.42422            0.1168       1.0711
        Factor3  |     -0.08649      0.03268           -0.0299       1.0412
        Factor4  |     -0.11917            .           -0.0412       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(6)  =   94.62 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    ---------------------------------------
        Variable |  Factor1 |   Uniqueness 
    -------------+----------+--------------
             mpg |  -0.8595 |      0.2613  
            disp |   0.8719 |      0.2397  
              hp |   0.9277 |      0.1393  
            qsec |  -0.6327 |      0.5997  
    ---------------------------------------

Note, the fa() function with fm="pa" does iterated principal axis factoring by default (equivalent to Stata's, ipf option). To have fa() do the one-shot principal axis factoring solution, set max.iter=1.