create a matrix from data frame

Question

I have a data frame with categorical values

Names   Dis   Del
    A   0-2   0-2
    A   2-4   0-2
    A   6-8   6-8
    B  8-10  8-10
    C   10+   10+

What I want is output in the number of count as per this data

       0-2  2-4  6-8  8-10  10+      
 0-2     1                       
 2-4     1                    
 6-8               1           
8-10                     1   
 10+                          1  

I also want to export this data which was created out of this data frame.

Answer 1

From the comments of @mtoto & @jogo:

table(mydf[-1])

or:

xtabs(data=mydf, ~ Dis+Del)

Both give:

      Del
Dis    0-2 10+ 6-8 8-10
  0-2    1   0   0    0
  10+    0   1   0    0
  2-4    1   0   0    0
  6-8    0   0   1    0
  8-10   0   0   0    1

If you want to get the levels in the correct order ( 10+ as last one):

mydf$Dis <- factor(mydf$Dis, levels = c("0-2","2-4","6-8","8-10","10+"))
mydf$Del <- factor(mydf$Del, levels = c("0-2","6-8","8-10","10+"))

Now you get:

      Del
Dis    0-2 6-8 8-10 10+
  0-2    1   0    0   0
  2-4    1   0    0   0
  6-8    0   1    0   0
  8-10   0   0    1   0
  10+    0   0    0   1

---

Used data:

mydf <- read.table(text="Names   Dis   Del
    A   0-2   0-2
    A   2-4   0-2
    A   6-8   6-8
    B  8-10  8-10
    C   10+   10+", header=TRUE)

Answer 2

I think you're looking for the dcast function from the reshape2 package.

df <- data.frame(Dis = c("0-2","2-4", "6-8", "8-10", "10+"),
                 Del = c("0-2", "0-2", "6-8", "8-10", "10+"))

Convert the columns you want to reshape by to factors.

df$Dis <- as.factor(df$Dis)
df$Del <- as.factor(df$Del)

Add a count columnt to reduce:

df$counts <- 1

Then apply the dcast function. We use the two named columns to set us cols/rows of the new matrix. The fun.aggregate ensures that if you have multiple occurrences of the same combination then you get the count of the occurrences. If you want a binary 0/1, then set this to max

wide_df <- dcast(df,
                  Dis ~ Del,
                  value.var = "counts",
                  fun.aggregate = sum)

Here is the result:

print(wide_df)
   Dis 0-2 10+ 6-8 8-10
1  0-2   1   0   0    0
2  10+   0   1   0    0
3  2-4   1   0   0    0
4  6-8   0   0   1    0
5 8-10   0   0   0    1

To get the same ordering as in your question, you can set the factors in the first step to be ordered with whatever order you desire.