Choice combination, which type of analyses?

Question

I've datas as follows:

           data=structure(list(Id = c(2L, 6L, 8L, 10L, 11L, 13L, 15L, 16L, 17L, 
20L, 21L, 23L, 24L, 31L, 33L, 38L, 39L, 40L, 50L, 52L, 53L, 54L, 
55L, 56L, 57L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 
69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 
82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 
95L, 96L, 97L, 98L, 99L, 100L, 101L, 102L, 103L, 104L, 105L, 
106L, 107L, 108L, 109L, 110L, 111L, 112L, 113L, 114L, 115L, 117L, 
118L, 119L, 120L, 121L, 122L, 123L, 125L, 126L, 127L, 128L, 129L, 
130L, 131L, 132L, 133L, 134L, 135L, 136L, 137L, 138L, 139L, 140L, 
141L, 142L, 143L, 144L, 145L, 146L, 147L, 148L, 149L, 150L, 151L, 
152L, 153L, 154L, 155L, 156L, 157L, 158L, 159L, 160L, 161L, 162L, 
163L, 164L, 165L, 166L, 167L, 168L, 170L, 171L, 172L, 173L, 174L, 
175L, 176L, 177L, 178L, 179L, 180L, 181L, 182L, 183L, 184L, 185L, 
186L, 187L, 188L, 189L, 190L, 191L, 192L, 193L, 194L, 195L, 196L, 
197L, 198L, 199L, 200L, 201L, 202L, 203L, 204L, 205L, 206L, 207L, 
208L, 209L, 210L, 211L, 212L, 213L, 214L, 215L, 216L, 217L, 218L, 
219L, 220L, 221L, 222L, 223L, 224L, 225L, 226L, 227L, 228L, 229L, 
230L, 231L, 232L, 233L, 234L, 235L, 237L, 238L, 240L, 241L, 242L, 
243L, 244L, 245L, 246L, 247L, 248L, 249L, 250L, 251L, 252L, 253L, 
254L, 255L, 256L, 257L, 258L, 259L, 260L, 261L, 262L, 263L, 264L, 
265L, 266L, 267L, 268L, 269L, 270L, 271L, 272L, 273L, 274L, 275L, 
276L, 277L, 278L, 279L, 280L, 281L, 282L, 283L, 284L, 285L, 287L, 
289L, 291L), `5` = c(1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 1, 0, 0), `3` = c(0, 1, 0, 1, 1, 0, 0.5, 1, 1, 0, 1, 1, 
0.5, 1, 1, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 0.5, 1, 1, 1, 0, 0.5, 1, 0, 0.5, 1, 0, 1, 1, 1, 
1, 0, 1, 1, 1, 1, 0, 1, 0.5, 1, 1, 1, 1, 1, 0.5, 1, 1, 0.5, 0, 
1, 0.5, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 
1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0.5, 1, 1, 
1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0.5, 
1, 0, 0.5, 0, 0, 1, 1, 0, 1, 0, 0.5, 1, 0, 0, 1, 1, 1, 1, 1, 
1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 
1, 0, 0, 0, 0, 0.5, 1, 0.5, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 
1, 0.5, 0, 0, 0, 1, 0, 0, 0, 0.5, 0, 1, 0, 1, 0, 1, 0, 1, 1, 
0, 1, 0, 1, 0.333333333333333, 0.5, 0, 0, 1, 1, 0, 1, 0, 0, 1, 
0, 0, 1, 0, 0, 1, 1, 0.5, 1, 0, 0.5, 0, 0, 0, 1, 0, 0.5, 1, 0, 
1, 0, 1, 0, 0, 0, 0, 1, 0), `1` = c(0, 0, 1, 0, 0, 0, 0.5, 0, 
0, 1, 0, 0, 0.5, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0.5, 0, 
0.5, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0.5, 
0, 0, 0.5, 1, 0, 0.5, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 
0, 0.5, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0.5, 0, 1, 0.5, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 
1, 1, 0, 1, 0, 1, 1, 1, 1, 0.5, 0, 0.5, 0, 1, 1, 0, 1, 1, 1, 
1, 0, 0, 0, 0, 0.5, 1, 1, 1, 0, 1, 1, 1, 0.5, 1, 0, 1, 0, 1, 
0, 1, 0, 0, 1, 0, 1, 0, 0.333333333333333, 0, 0, 1, 0, 0, 1, 
0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0.5, 0, 1, 0, 1, 1, 1, 0, 1, 
0.5, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1), `2` = c(0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.333333333333333, 
0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), `4` = c(0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0), D = c(1, 
0, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 0, 0.5, 0.5, 0, 0.5, 0, 0, 
0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 
1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 
1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0.5, 0, 1, 1, 0, 1, 1, 1, 
0, 0.5, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0.5, 1, 1, 1, 
1, 1, 1, 0, 1, 0.5, 0.5, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 
1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0.5, 
1, 0.5, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 
1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 
0.5, 1, 1, 1, 1, 1, 0, 0.5, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 
1, 0, 1, 0, 1, 1, 1, 1, 0.5, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), B = c(0, 1, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 
0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), A = c(0, 0, 0.5, 0.5, 
0.5, 0.5, 0.5, 0, 0, 1, 0.5, 0.5, 0, 0.5, 1, 1, 1, 1, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
1, 0, 0, 0, 0, 0, 0, 0.5, 1, 0, 0, 1, 0, 0, 0, 1, 0.5, 1, 0, 
0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 1, 0, 
0.5, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0.5, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 
0, 0, 0, 0, 0.5, 0, 0, 1, 0, 1, 0.5, 1, 0, 0, 0, 0, 0, 1, 0, 
1, 0, 0, 0, 0, 0.5, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0), E = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), C = c(0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)), .Names = c("Id", "5", "3", 
"1", "2", "4", "D", "B", "A", "E", "C"), row.names = c(NA, 250L
), class = "data.frame")

for Id represent a customer. There is two products, the first product is represented by its few versions 1,2,3,4,5 and the product 2 is represented by versions A,B,C,D.

A customer will buy the two types of products. He can also buy one or few versions of the same product. For example for the first product, he can buy version 1 and 3, and the second version A and B.

As you see in my datas, I ponderate the sum in order to have for sum(product1)=1 and sum(product2)=1.

I want to study the combinaison between choices between the two products (1,2,3,4,5) and (A,B,C,D) in the other side.

The typr of question that I'm trying to investigate, for example : Are poeple that take version 1 and 3 are more likely to take the version D of the other product ?

How to do this statistically ? because there is combinaison in the same product that complicate the analyses ?

Thanks you a lot !

EDIT 2 : I've used table before extracted information seperately and ponderate them :

         A A,B A,D A,E   B   C   D   E
  1      23   0   9   1  11   7  82   2
  1,2     4   0   0   0   0   0  10   0
  1,2,3   0   0   1   0   2   1   0   0
  1,3     3   0   9   0   2   0  25   3
  2       2   1   0   0   2   1  18   1
  2,3     1   0   0   0   0   0   6   0
  3      19   0  13   0  19  11 172   5
  4       0   0   0   0   1   2   9   1
  5       1   0   1   0   0   1   9   1

EDIT 3 :my original datas was, do you think that I should work with instead and do you have an idea which type of analyses suits this type of datas ?

    data=structure(list(Id = c(2L, 6L, 8L, 10L, 11L, 13L, 15L, 16L, 17L, 
20L, 21L, 23L, 24L, 31L, 33L, 38L, 39L, 40L, 50L, 52L, 53L, 54L, 
55L, 56L, 57L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 
69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 
82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 
95L, 96L, 97L, 98L, 99L, 100L, 101L, 102L, 103L, 104L, 105L, 
106L, 107L, 108L, 109L, 110L, 111L, 112L, 113L, 114L, 115L, 117L, 
118L, 119L, 120L, 121L, 122L, 123L, 125L, 126L, 127L, 128L, 129L, 
130L, 131L, 132L, 133L, 134L, 135L, 136L, 137L, 138L, 139L, 140L, 
141L, 142L, 143L, 144L, 145L, 146L, 147L, 148L, 149L, 150L, 151L, 
152L, 153L, 154L, 155L, 156L, 157L, 158L, 159L, 160L, 161L, 162L, 
163L, 164L, 165L, 166L, 167L, 168L, 170L, 171L, 172L, 173L, 174L, 
175L, 176L, 177L, 178L, 179L, 180L, 181L, 182L, 183L, 184L, 185L, 
186L, 187L, 188L, 189L, 190L, 191L, 192L, 193L, 194L, 195L, 196L, 
197L, 198L, 199L, 200L, 201L, 202L, 203L, 204L, 205L, 206L, 207L, 
208L, 209L, 210L, 211L, 212L, 213L, 214L, 215L, 216L, 217L, 218L, 
219L, 220L, 221L, 222L, 223L, 224L, 225L, 226L, 227L, 228L, 229L, 
230L, 231L, 232L, 233L, 234L, 235L, 237L, 238L, 240L, 241L, 242L, 
243L, 244L, 245L, 246L, 247L, 248L, 249L, 250L, 251L, 252L, 253L, 
254L, 255L, 256L, 257L, 258L, 259L, 260L, 261L, 262L, 263L, 264L, 
265L, 266L, 267L, 268L, 269L, 270L, 271L, 272L, 273L, 274L, 275L, 
276L, 277L, 278L, 279L, 280L, 281L, 282L, 283L, 284L, 285L, 287L, 
289L, 291L, 292L, 293L, 294L, 295L, 296L, 297L, 298L, 299L, 300L, 
301L, 302L, 303L, 304L, 305L, 306L, 308L, 309L, 310L, 311L, 312L, 
313L, 314L, 315L, 316L, 317L, 318L, 319L, 320L, 321L, 322L, 323L, 
324L, 325L, 326L, 327L, 328L, 329L, 330L, 334L, 335L, 336L, 337L, 
338L, 339L, 340L, 342L, 343L, 344L, 345L, 346L, 347L, 348L, 349L, 
350L, 351L, 352L, 353L, 354L, 355L, 356L, 357L, 358L, 359L, 360L, 
361L, 362L, 363L, 364L, 365L, 366L, 367L, 368L, 369L, 370L, 371L, 
372L, 373L, 374L, 376L, 377L, 378L, 379L, 380L, 381L, 382L, 383L, 
384L, 385L, 386L, 387L, 388L, 389L, 391L, 392L, 393L, 395L, 396L, 
397L, 398L, 399L, 400L, 401L, 402L, 403L, 404L, 405L, 406L, 407L, 
408L, 409L, 410L, 411L, 412L, 413L, 414L, 415L, 416L, 417L, 418L, 
419L, 420L, 421L, 422L, 423L, 424L, 425L, 426L, 427L, 429L, 430L, 
431L, 432L, 433L, 435L, 436L, 437L, 438L, 439L, 440L, 441L, 442L, 
443L, 444L, 445L, 446L, 447L, 448L, 449L, 450L, 451L, 452L, 453L, 
454L, 455L, 456L, 457L, 458L, 459L, 460L, 461L, 462L, 463L, 464L, 
465L, 466L, 467L, 468L, 469L, 470L, 471L, 472L, 473L, 474L, 475L, 
476L, 477L, 478L, 479L, 480L, 481L, 482L, 483L, 484L, 485L, 486L, 
487L, 488L, 489L, 490L, 491L, 492L, 493L, 494L, 495L, 496L, 497L, 
498L, 499L, 500L, 501L, 502L, 503L, 504L, 505L, 506L, 507L, 508L, 
509L, 510L, 511L, 512L, 513L, 514L, 515L, 516L, 517L, 518L, 519L, 
520L, 521L, 523L, 524L, 525L, 526L, 527L, 528L, 529L, 530L, 531L, 
532L, 533L, 534L, 535L, 536L, 538L, 539L, 540L, 541L, 542L, 543L, 
544L, 545L), Product1 = c("5", "3", "1", "3", "3", "5", "1,3", 
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"1", "3", "1", "1", "1", "1,3", "1", "3", "1", "3", "1", "3", 
"1", "3", "3", "1", "3", "1", "3", "1,2,3", "2,3", "5", "1", 
"3", "3", "1", "3", "1", "5", "3", "1", "1", "3", "1", "1", "3", 
"3", "1,3", "3", "1", "2,3", "1", "1", "1", "3", "1", "1,3", 
"3", "1", "3", "4", "3", "1", "1", "1", "5", "3", "1", "1", "1", 
"3", "3", "3", "3", "1,2", "3", "1", "1", "1,3", "3", "1", "1", 
"1", "3", "1,3", "3", "3", "3", "1", "3", "3", "3", "3", "3", 
"4", "1", "1", "3", "1,3", "1", "1", "3", "1", "1", "1", "1,3", 
"3", "1", "1,3", "3", "1", "1", "3", "3", "3", "1,3", "1", "1", 
"3", "1", "3", "3", "3", "5", "1", "1,2", "1", "1", "2", "3", 
"2", "1,3", "3", "3", "1", "1", "1,3", "3", "1", "3", "1", "1", 
"3", "1", "1", "3", "2", "2,3", "4", "1", "1", "3", "1", "3", 
"1,2", "2", "1,3", "1", "3", "3", "1", "3", "3", "1,2", "1", 
"3", "3", "3", "3", "3", "1", "1,3", "1", "3", "1", "3", "4", 
"3", "1,3", "1,2", "3", "3", "1", "3", "1", "1,3", "3", "1", 
"1,3", "1", "5", "3", "3", "2", "1,3", "3", "1,3", "3", "3", 
"1,2", "3", "1,3", "3", "3", "1", "1,2", "1,2", "3", "2", "1,2,3", 
"1", "3", "3", "3", "1", "1", "3", "5", "3", "1,2,3", "3", "3", 
"3", "1", "1,2", "3", "1", "3", "3", "1,3", "1,3", "2", "3", 
"3", "1", "3", "1,2", "2", "2,3", "2", "3", "1,3", "2", "3", 
"3", "1", "1", "3", "1,3", "3", "2", "2", "3", "2,3", "1", "1", 
"1", "1", "3", "3", "1", "4", "3", "2", "1", "1", "2", "1", "1", 
"3", "3", "1,3", "1,3", "2", "1,2", "3", "3", "3", "2", "3", 
"1,2", "1,3", "3", "1,2", "1", "3", "3", "2", "1", "3", "3", 
"1", "3", "1,3", "3", "2", "3", "2", "3", "1,2,3", "1", "1", 
"3", "1", "1", "1", "1", "2", "2", "1"), Product2 = c("D", "B", 
"A,D", "A,D", "A,D", "A,D", "A,D", "D", "D", "A", "A,D", "A,D", 
"E", "A,D", "A", "A", "A", "A", "C", "D", "B", "C", "D", "D", 
"D", "D", "D", "D", "D", "D", "D", "D", "D", "A", "D", "D", "D", 
"D", "A", "D", "D", "D", "D", "D", "D", "D", "D", "D", "D", "D", 
"D", "D", "D", "D", "D", "D", "C", "D", "D", "D", "D", "D", "D", 
"A", "D", "D", "D", "D", "D", "D", "A,D", "A", "D", "D", "A", 
"D", "D", "D", "A", "A,D", "A", "D", "D", "A", "A", "D", "D", 
"D", "A", "D", "D", "D", "D", "A,D", "D", "D", "D", "D", "D", 
"D", "A", "D", "A,D", "A,D", "D", "D", "B", "B", "D", "D", "D", 
"D", "D", "D", "D", "C", "D", "D", "D", "B", "D", "A", "D", "D", 
"C", "D", "D", "D", "D", "B", "D", "D", "D", "B", "B", "D", "A,D", 
"D", "A,D", "D", "D", "D", "D", "B", "D", "D", "D", "D", "D", 
"D", "D", "C", "D", "D", "D", "D", "E", "D", "D", "B", "D", "D", 
"D", "D", "D", "D", "A", "D", "A", "D", "D", "D", "D", "D", "D", 
"D", "D", "B", "A,D", "D", "D", "D", "D", "D", "B", "A,D", "D", 
"D", "A", "D", "A", "A,E", "A", "B", "D", "D", "D", "D", "A", 
"D", "A", "D", "D", "D", "D", "A,D", "D", "A", "D", "D", "B", 
"B", "D", "D", "B", "D", "D", "D", "D", "D", "D", "D", "D", "D", 
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"D", "D", "D", "D", "D", "D", "D", "D", "D", "D", "D", "D", "D", 
"B", "D", "D", "D", "D", "B", "A", "D", "D", "C", "D", "E", "B", 
"A,D", "C", "D", "D", "D", "D", "B", "D", "D", "D", "D", "A", 
"A", "D", "B", "D", "D", "A,D", "A,D", "D", "C", "D", "A", "D", 
"D", "D", "D", "D", "B", "B", "B", "B", "C", "B", "A,D", "C", 
"A", "D", "A", "A", "D", "D", "D", "B", "D", "D", "A,D", "B", 
"B", "D", "D", "D", "D", "D", "A", "A,D", "D", "D", "A", "A", 
"D", "D", "D", "D", "D", "C", "D", "C", "D", "C", "D", "A", "D", 
"D", "D", "B", "D", "D", "E", "E", "D", "D", "D", "A", "D", "B", 
"D", "D", "D", "D", "D", "D", "D", "D", "A", "D", "D", "D", "D", 
"D", "D", "A", "D", "A,D", "A,D", "D", "D", "D", "D", "D", "B", 
"D", "E", "D", "B", "A", "D", "D", "D", "D", "D", "D", "D", "D", 
"D", "D", "C", "D", "C", "D", "C", "D", "D", "D", "D", "B", "E", 
"D", "B", "D", "D", "D", "D", "D", "D", "D", "D", "A,D", "D", 
"E", "D", "A,D", "A,D", "D", "D", "D", "D", "D", "D", "D", "A,D", 
"D", "D", "E", "A", "D", "E", "A", "D", "D", "D", "D", "A", "B", 
"A", "D", "D", "D", "C", "A", "E", "D", "A,B", "D", "E", "A", 
"D", "A", "D", "C", "E", "A,D", "D", "A", "D", "D", "D", "D", 
"D", "A", "D", "D", "A", "D", "D", "D", "D", "A", "C", "D", "C", 
"D", "A,D", "D", "D", "D", "A", "D", "A,D", "A", "C", "D", "D", 
"D", "D", "D", "D", "B", "C")), .Names = c("Id", "Product1", 
"Product2"), row.names = c(2L, 6L, 8L, 10L, 11L, 13L, 15L, 16L, 
17L, 20L, 21L, 23L, 24L, 31L, 33L, 38L, 39L, 40L, 50L, 52L, 53L, 
54L, 55L, 56L, 57L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 
68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 
81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 
94L, 95L, 96L, 97L, 98L, 99L, 100L, 101L, 102L, 103L, 104L, 105L, 
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254L, 255L, 256L, 257L, 258L, 259L, 260L, 261L, 262L, 263L, 264L, 
265L, 266L, 267L, 268L, 269L, 270L, 271L, 272L, 273L, 274L, 275L, 
276L, 277L, 278L, 279L, 280L, 281L, 282L, 283L, 284L, 285L, 287L, 
289L, 291L, 292L, 293L, 294L, 295L, 296L, 297L, 298L, 299L, 300L, 
301L, 302L, 303L, 304L, 305L, 306L, 308L, 309L, 310L, 311L, 312L, 
313L, 314L, 315L, 316L, 317L, 318L, 319L, 320L, 321L, 322L, 323L, 
324L, 325L, 326L, 327L, 328L, 329L, 330L, 334L, 335L, 336L, 337L, 
338L, 339L, 340L, 342L, 343L, 344L, 345L, 346L, 347L, 348L, 349L, 
350L, 351L, 352L, 353L, 354L, 355L, 356L, 357L, 358L, 359L, 360L, 
361L, 362L, 363L, 364L, 365L, 366L, 367L, 368L, 369L, 370L, 371L, 
372L, 373L, 374L, 376L, 377L, 378L, 379L, 380L, 381L, 382L, 383L, 
384L, 385L, 386L, 387L, 388L, 389L, 391L, 392L, 393L, 395L, 396L, 
397L, 398L, 399L, 400L, 401L, 402L, 403L, 404L, 405L, 406L, 407L, 
408L, 409L, 410L, 411L, 412L, 413L, 414L, 415L, 416L, 417L, 418L, 
419L, 420L, 421L, 422L, 423L, 424L, 425L, 426L, 427L, 429L, 430L, 
431L, 432L, 433L, 435L, 436L, 437L, 438L, 439L, 440L, 441L, 442L, 
443L, 444L, 445L, 446L, 447L, 448L, 449L, 450L, 451L, 452L, 453L, 
454L, 455L, 456L, 457L, 458L, 459L, 460L, 461L, 462L, 463L, 464L, 
465L, 466L, 467L, 468L, 469L, 470L, 471L, 472L, 473L, 474L, 475L, 
476L, 477L, 478L, 479L, 480L, 481L, 482L, 483L, 484L, 485L, 486L, 
487L, 488L, 489L, 490L, 491L, 492L, 493L, 494L, 495L, 496L, 497L, 
498L, 499L, 500L, 501L, 502L, 503L, 504L, 505L, 506L, 507L, 508L, 
509L, 510L, 511L, 512L, 513L, 514L, 515L, 516L, 517L, 518L, 519L, 
520L, 521L, 523L, 524L, 525L, 526L, 527L, 528L, 529L, 530L, 531L, 
532L, 533L, 534L, 535L, 536L, 538L, 539L, 540L, 541L, 542L, 543L, 
544L, 545L), class = "data.frame")

geekoverdose · Accepted Answer · 2016-06-06 10:36:56Z

2

I derive that you don't yet know which relations to analyse. You could for example use some clustering to identify relations worth investigating further:

> # remove constants
> data <- data.frame(data[,-which(apply(data, 2, sd)==0)], check.names = T)

> # clusters 
> k <- kmeans(x = data[,-1], centers = 3, iter.max = 10000) # you likely need more than 3 clusters
> table(k$cluster)

 1  2  3 
17  9  4 

> k$center

          X5        X3        X1         D   B         A         E   C
1 0.05882353 0.9411765 0.0000000 0.8529412 0.0 0.1470588 0.0000000 0.0
2 0.11111111 0.5000000 0.3888889 0.1666667 0.0 0.7222222 0.1111111 0.0
3 0.00000000 1.0000000 0.0000000 0.0000000 0.5 0.0000000 0.0000000 0.5

# visualize cluster centers
> library(lattice)
> levelplot(t(k$center), col.regions=gray(100:0/100), ylab='cluster nr.')

Those clusters give you an idea of what some trends worth looking at might be. You could use those to split your data into more homogeneous groups, which you can then analyse/model further:

> # look at samples of one example cluster (nr. 1: people that buy 3 seem to buy D and/or A)
> table(data[data$X3 > 0.9,][,c('D', 'A')])

     A
D      0 0.5  1
  0    4   0  3
  0.5  0   5  0
  1   11   0  0

edited Jun 6, 2016 at 10:36

answered Jun 6, 2016 at 10:28

geekoverdose

3,8912 gold badges16 silver badges28 bronze badges

$\begingroup$ Thanks a lot for your help, # remove constants because 2 and 4 = 0 ? it was just a dput(head(data,30)), so I've value in all column, I' should delete this line ? (please see the edit to see the complete data). Thanks again. $\endgroup$
– ranell
Jun 6, 2016 at 12:04
$\begingroup$ Yes, I removed constants because they a) don't add information - so removing them doesn't hurt, and b) they cause problems with certain analysis (e.g. correlation). If they are actually constants in your data you should remove them too - otherwise just skip this step. $\endgroup$
– geekoverdose
Jun 6, 2016 at 12:08
$\begingroup$ ok thanks, but I still have a problem. I expected to see dark cluster between D and 1 because through my cross tabulation "table()" i've value that indicate that there is strong relation between D and 3. Plese see EDIT 2. Thanks again! $\endgroup$
– ranell
Jun 6, 2016 at 12:13
$\begingroup$ In your example data, D and 3 are not very strongly correlated: corrplot(cor(data[,c('D', '3')])), so getting clusters with that property is unlikely. Try varying the nr. of clusters: I could imagine you likely have more than 3 groups or clusters of buying behaviours for those 2 products. $\endgroup$
– geekoverdose
Jun 6, 2016 at 12:21
$\begingroup$ Nice, didn't know about the corrplot packages. Thanks. I've 492 customer for 2 products and each product 5 versions. The question is what is the perfect nb of cluster that I must choose, and why the plot of cluster changing for each run ?? Thanks again it is very interesting ! $\endgroup$
– ranell
Jun 6, 2016 at 12:32

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