计算列表项的相对频率及其在R?中的总和 [英] Calculate relative frequency of list terms and its sum in R?

问题描述

我的数据保存在很长的列表中.这是前六行/记录的示例:

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"))  

现在，我想计算每个行/记录中每个唯一术语出现的相对频率.
根据我的示例，我希望获得类似于以下的输出:

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  

不幸的是，到目前为止，我只知道如何计算各个术语的相对频率.例如:

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  

当然，我的示例仅包含十个唯一术语.但是我的实际数据包含近200个唯一术语，因此上述方法不可行.因此，我正在寻找一种不同的方法，该方法可以让我一次计算所有术语的相对频率.
除此之外，我想总结所有行/记录中每个唯一名称的相对频率.
根据我上面的示例，我想获得类似于此示例的输出，请:

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  

非常感谢您的考虑！

推荐答案

您可以使用?table和?aggregate:

BL <- lapply(A, function(x)table(x)/length(x))
## turn list into a vector
B <- unlist(BL)

## sum all frequencies
aggregate(B, list(names(B)), FUN=sum)
#   Group.1         x
#1  CHARLES 3.2916667
#2    DAVID 0.1250000
#3     JACK 0.3333333
#4    JAMES 0.5000000
#5     JOHN 0.3333333
#6  MATTHEW 0.3333333
#7  MICHAEL 0.1250000
#8  RICHARD 0.2500000
#9   ROBERT 0.3333333
#10 WILLIAM 0.3750000

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