R 分类变量到矩阵的向量

我有一个数据框，它有唯一的id和分类变量。我需要将所有唯一ID压缩到一行中，将所有适用的分类变量压缩到不同的向量中，以便以一个矩阵结束，用于一些回归分析。例：

id    cat
1     a
2     b
1     b
3     c
4     a
2     a
4     c
3     c

output:
id   cat.a   cat.b   cat.c
1    1       1       0
2    1       1       0
3    0       0       2
4    1       0       1

---

我已经在有用的软件包中研究了build.x函数，但无法解决将其折叠为单个id的问题，这看起来就像是重塑数据

library(reshape2)
dcast(df, id ~ cat)

# Using cat as value column: use value.var to override.
# Aggregation function missing: defaulting to length
#   id a b c
# 1  1 1 1 0
# 2  2 1 1 0
# 3  3 0 0 2
# 4  4 1 0 1

虽然对于这样一个简单的问题来说，这可能是矫枉过正了。正如@Seth在评论中指出的那样，您可以使用

表

with(df, table(id, cat))
#    cat
# id  a b c
#   1 1 1 0
#   2 1 1 0
#   3 0 0 2
#   4 1 0 1

（使用此数据：）

---

我认为这可以在不使用任何必需的库的情况下完成所需的任务——尽管它确实使用了两个嵌套循环，所以速度可能会很慢

## setting up the data you gave as an example in your question
dat=matrix(c(1,2,1,3,4,2,4,3,'a','b','b','c','a','a','c','c'),ncol=2)
data=data.frame(dat)

## determine the categories as defined by your data 
cats <- levels(data$X2)

## create a blank matrix
out=matrix(0,nrow=length(levels(data$X1)),ncol=length(levels(data$X2)))

## what is the lowest value of your first column
i=min(as.numeric(data$X1))

## j will serve as a counter for the rows in the out matrix
j=1
while(i<=max(as.numeric(data$X1)))
    {
        ## find the unique values associated with the first 'i' 
        idi <- which(as.numeric(data$X1)==i)
        ## set up a counter that corresponds to the columns of your out matrix
        k=1
        while(k<= length(cats)) {
            ## determine the values associated with the particular category 
            out[j,k] <- length(which(data[idi,2]==cats[k]))
            k=k+1
        }
        i=i+1
        j=j+1
    }

##设置您在问题中给出的数据作为示例
dat=矩阵（c（1,2,1,3,4,2,4,3，'a'，'b'，'b'，'c'，'a'，'a'，'c'，'c'，ncol=2）
数据=数据帧（dat）
##确定数据定义的类别
猫的桌子（id，cat）离你想要的很近吗？@Seth这正是我想要的，干杯！说得好！这只是我在自己的编码实践中的习惯。
## setting up the data you gave as an example in your question
dat=matrix(c(1,2,1,3,4,2,4,3,'a','b','b','c','a','a','c','c'),ncol=2)
data=data.frame(dat)

## determine the categories as defined by your data 
cats <- levels(data$X2)

## create a blank matrix
out=matrix(0,nrow=length(levels(data$X1)),ncol=length(levels(data$X2)))

## what is the lowest value of your first column
i=min(as.numeric(data$X1))

## j will serve as a counter for the rows in the out matrix
j=1
while(i<=max(as.numeric(data$X1)))
    {
        ## find the unique values associated with the first 'i' 
        idi <- which(as.numeric(data$X1)==i)
        ## set up a counter that corresponds to the columns of your out matrix
        k=1
        while(k<= length(cats)) {
            ## determine the values associated with the particular category 
            out[j,k] <- length(which(data[idi,2]==cats[k]))
            k=k+1
        }
        i=i+1
        j=j+1
    }