I'm looking for something along the lines of
which ( table ( x ) == max ( table ( x ) ) )
to find the most common level of one factor
by several other factors. For instance, I've got
> X <- data.frame (
+ x = factor ( sample ( c ( "A" , "B" , "C" , "D" ) , 20 , r = T ) )
+ , z1 = factor ( sample ( c ( "Before" , "After" ) , 20 , r = T ) )
+ , z2 = factor ( sample ( c ( "Red" , "Green" , "Blue" ) , 20 , r =
T ) )
+ , z3 = factor ( sample ( 0:6 , 20 , r = T ) )
+ )
> X
x z1 z2 z3
1 D After Blue 0
2 D Before Green 3
3 A Before Red 5
4 C After Green 6
5 C Before Green 6
6 C Before Green 0
7 C Before Red 1
8 C Before Red 5
9 A Before Blue 3
10 A After Green 4
11 D After Red 3
12 C After Green 5
13 A After Red 0
14 B After Red 6
15 B Before Red 3
16 A Before Blue 4
17 B Before Blue 5
18 A After Blue 1
19 B Before Green 1
20 C Before Red 2
>
and i would like to be able to say which category of x was the
most common for each combination of z1, z2, and z3. So, here,
which category of x was the most common for Before,Red,0;
Before,Red,1; ... Before,Red,6; Before,Green,0; Before,Green,1;
... Before,Green,6;...
This seems simple rather as i type it out, but i havent been
able to come up with the right approach so far. its friday night
so maybe i should just go home and wait until monday...
--
David
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