使用nlevels()进行R-ddply总结不起作用

Question

使用plyr包总结数据时，似乎不可能使用nlevels（）函数。

我的数据集结构如下：

>aer <- read.xlsx("XXXX.xlsx", sheetIndex=1)
>aer$ID <- as.factor(aer$ID)
>aer$description <- as.factor(aer$description)    
>head(aer)
  ID SOC   start        end         days  count severity relation
1  1 410   2015-04-21   2015-04-28    7     1        1        3
2  1 500   2015-01-30   2015-05-04   94     1        1        3
3  1 600   2014-11-25   2014-11-29    4     1        1        3
4  1 600   2015-01-02   2015-01-07    5     1        1        3
5  1 600   2015-01-26   2015-03-02   35     1        1        3
6  1 600   2015-04-14   2015-04-17    3     1        1        3
> dput(head(aer,4))
structure(list(ID = structure(c(1L, 1L, 1L, 1L), .Label = c("1", 
"2", "3", "4", "5", "6", "7", "8", "9", "10", "12", "13", "14", 
"15"), class = "factor"), SOC = c(410, 500, 600, 600),  
start = structure(c(16546, 16465, 16399, 16437), class = "Date"), 
end = structure(c(16553, 16559, 16403, 16442), class = "Date"), 
days = c(7, 94, 4, 5), count = c(1, 1, 1, 1), severity = c(1, 
1, 1, 1), relation = c(3, 3, 3, 3)), .Names = c("ID", "SOC", 
"description", "start", "end", "days", "count", "severity", "relation"
), row.names = c(NA, 4L), class = "data.frame")

我想知道的是，当使用变量"SOC"划分数据集时，在创建的数据部分中，"ID"变量中存在多少级别我想将这些信息与其他一些变量一起总结到一个新的数据集中。因此，我想使用这样的plyr包：

summaer2 <- ddply(aer, c("SOC"), summarise,
    participants    = nlevels(ID), 
    events          = sum(count),
    min_duration    = min(days), 
    max_duration    = max(days),
    max_severity    = max(severity))

这将返回以下错误：

Error in Summary.factor(c(4L, 5L, 11L, 11L, 14L, 14L), na.rm = FALSE) : 
‘max’ not meaningful for factors

有人能给我一些如何达到目标的建议吗？或者我做错了什么？

非常感谢！

Answer 1

更新：

用length(unique(ID))代替nlevels(ID)似乎给了我想要的输出：

> head(summaer2)
   SOC participants events min_duration max_duration max_severity
1  100            4      7            1           62            2
2  410            9     16            1           41            2
3  431            2      2          109          132            1
4  500            5      9           23          125            2
5  600            8     19            1           35            1
6 1040            1      1           98           98            2