Calculate relative frequency of list terms and its sum in R?
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I have data saved in a long list. This is an example of the first six lines / records:  

    A <- list(c("JAMES","CHARLES","JAMES","RICHARD"),  
        c("JOHN","ROBERT","CHARLES"),  
        c("CHARLES","WILLIAM","CHARLES","MICHAEL","WILLIAM","DAVID","CHARLES","WILLIAM"),  
        c("CHARLES"),  
        c("CHARLES","CHARLES"),  
        c("MATTHEW","CHARLES","JACK"))  

Now I would like to calculate the relative frequency with which each unique term occurs in each line / record.  
Based on my example I would like to achieve an output similar to this:  

    [1] "JAMES" 0.5 "CHARLES" 0.25 "RICHARD" 0.25  
    [2] "JOHN" 0.3333333 "ROBERT" 0.3333333 "CHARLES" 0.3333333  
    [3] "CHARLES" 0.375 "WILLIAM" 0.375 "MICHAEL" 0.125 "DAVID" 0.125  
    [4] "CHARLES" 1  
    [5] "CHARLES" 1  
    [6] "MATTHEW" 0.3333333 "CHARLES" 0.3333333 "JACK" 0.3333333  

So far I only know how to calculate the relative frequency of individual terms, unfortunately; e.g.:  

    > (sapply(A, function(x)sum(grepl("JAMES", x))))/sapply(A, length)  
    [1] 0.5 0.0 0.0 0.0 0.0 0.0  

My example contains only ten unique terms, of course. But my actual data contains almost 200 unique terms so the approach above wouldn't be feasible. Therefore I'm looking for a different way which would allow me to calculate the relative frequency of all of the terms in just one go, please.  
In addition to that I would like to sum up these relative frequencies for each unique name over all lines / records.  
Based on my example above I would like to achieve an output similar to this one, please:  

    [1] "JAMES" 0.5  
    [2] "CHARLES" 3.291667  
    [3] "RICHARD" 0.25  
    [4] "JOHN" 0.3333333  
    [5] "ROBERT" 0.3333333  
    [6] "WILLIAM" 0.375  
    [7] "MICHAEL" 0.125  
    [8] "DAVID" 0.125  
    [9] "MATTHEW" 0.3333333  
    [10] "JACK" 0.3333333  

Thank you very much in advance for your consideration!