The larger the dataset, the lower the percentage variance explained tends to be, which makes intuitive sense. Additionally, the usual way this is calculated rather under estimates it as argued by Greenacre (e.g. In CA in practice). His ca package provides options for the alternatives he suggests.
Bootstrapping might be one option. See Ringrose's package cabootcrs although the cases will be overwhelming you can at least see the stability of the variables.