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    copied!<p>If you use dcast from the reshape2 package, you'll get a matrix that you can then do a pairwise correlation function on:</p> <pre><code>data &lt;- read.table(h=T, text="Order.ID Product + 193505 'Pineapple Cheddar Burger' + 193505 'Onion Rings' + 193564 'Pineapple Cheddar Burger' + 193623 'Hamburger' + 193623 'French Fries' + 193623 'Fountain Soda' + 193623 'Hot Dog' + 193631 'Hamburger' + 193631 'French Fries' + 193631 'Milkshake' + 193644 'Daily Special' + 193726 'Hamburger' + 193726 'French Fries' + 193726 'Fountain Soda' + 193728 'Hamburger' + 193728 'French Fries' + 193728 'Fountain Soda' + 193738 'Hamburger' + 193738 'French Fries' + 193762 'Hamburger' + 193762 'French Fries' + 193762 'Fountain Soda' + 193762 'Hamburger' + 193762 'French Fries' + 193762 'Fountain Soda'") library(reshape2) df &lt;- dcast(data, Order.ID ~ Product) cor(df[,-1], use = "pairwise") Daily Special Fountain Soda French Fries Hamburger Hot Dog Daily Special 1.0000 -0.2868 -0.4375 -0.4375 -0.1250 Fountain Soda -0.2868 1.0000 0.8030 0.8030 0.2294 French Fries -0.4375 0.8030 1.0000 1.0000 0.1250 Hamburger -0.4375 0.8030 1.0000 1.0000 0.1250 Hot Dog -0.1250 0.2294 0.1250 0.1250 1.0000 Milkshake -0.1250 -0.2868 0.1250 0.1250 -0.1250 Onion Rings -0.1250 -0.2868 -0.4375 -0.4375 -0.1250 Pineapple Cheddar Burger -0.1890 -0.4336 -0.6614 -0.6614 -0.1890 Milkshake Onion Rings Pineapple Cheddar Burger Daily Special -0.1250 -0.1250 -0.1890 Fountain Soda -0.2868 -0.2868 -0.4336 French Fries 0.1250 -0.4375 -0.6614 Hamburger 0.1250 -0.4375 -0.6614 Hot Dog -0.1250 -0.1250 -0.1890 Milkshake 1.0000 -0.1250 -0.1890 Onion Rings -0.1250 1.0000 0.6614 Pineapple Cheddar Burger -0.1890 0.6614 1.0000 </code></pre>
 

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