irlba | Fast truncated singular value decompositions | Machine Learning library

I have been comparing the performance of several PCA implementations from both Python and R, and noticed an interesting behavior:
While it seems impossible to compute the PCA of a sparse matrix in Python (the only approach would be scikit-learn's TruncatedSVD, yet it does not support the mean-centering required to be equivalent to a covariance solution for PCA. Their argumentation is, that it would destroy the sparsity property of the matrix. Other implementations like Facebook's PCA algorithm or the PCA/randomPCA method in scikit learn do not support sparse matrices for similar reasons.

While all of that makes sense to me, several R packages, like irlba, rsvd, etc., are able to handle sparse matrices (e.g. generated with rsparsematrix), and even allow for specific center=True arguments.

My question is, how R handles this internally, as it seems to be vastly more efficient than the comparable Python implementation. Does R still maintain the sparsity by doing Absolute Scaling instead (which would theoretically falsify the results, but at least maintain sparsity)? Or is there any way in which the mean can be stored explicitly for the zero values, and is only stored once (instead of for every value separately)?

To get put off hold: How does R internally store matrices with mean-centering without exploding RAM usage. Hope that is concise enough....

EDIT I was trying to figure out what is wrong with my code and I started to plot simple graphs to see how the arrow will look on smaller graphs. I tired the following command:

Please let me know if I'm simply doing this wrong...

I have a 47,194 row, 27 column numeric matrix with some missing values. I'm trying to use irlba to factor the matrix. In all previous R projects, I've used NA to indicate missing data. When I do with irlba, I get an error that data is missing. How do I indicate that a value is missing and that irlba should ignore it when factoring the matrix?

Of note: The documentation for irlba doesn't include sparse data. Every element has a value. There are examples with values of zero, but I can't do that because it will factor the value of zero, not ignore the value.

Code example by request:

I am currently trying to implement a SVD of a very large matrix using bigmemory and irlba. As far as I understand I have to adjust the mult command in the irlba package, which I have done like this:

I am new to R programming. For my course work, I am implementing Recommendation system using R. I already convert data table to matrix and then processed SVD=udv using irlba funtion. Now I have the following matrix u.

Now I need to classify them based on their sign. For example, here, first three are combination of(-, -) then last is (-,-), so they all must be in same community. Then 4th and 5th are (-, +), they are in same community and so on.