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copied!<p>The "database approach" is to keep a separate table (a data.frame) for your product keys definitions. It makes even more sense since you say your product keys translate into not only a brand, but also a size:</p> <pre><code>product.keys <- read.table(textConnection(" product brand size 1 Tylenol small 2 Tylenol medium 3 Tylenol large 4 Advil small 5 Advil medium 6 Advil large 7 Bayer small 8 Bayer medium 9 Bayer large 10 Generic small 11 Generic medium 12 Generic large "), header = TRUE) </code></pre> <p>Then, you can join your data using <code>merge</code>:</p> <pre><code>merge(dat, product.keys, by = "product") # product brand size # 1 1 Tylenol small # 2 3 Tylenol large # 3 4 Advil small # 4 5 Advil medium # 5 5 Advil medium # 6 5 Advil medium # 7 6 Advil large # 8 7 Bayer small # 9 7 Bayer small # 10 8 Bayer medium # 11 9 Bayer large # 12 9 Bayer large # 13 9 Bayer large # 14 10 Generic small # 15 10 Generic small # 16 11 Generic medium # 17 11 Generic medium # 18 11 Generic medium # 19 11 Generic medium # 20 11 Generic medium </code></pre> <p>As you notice, the order of the rows is not preserved by <code>merge</code>. If this is a problem, the <code>plyr</code> package has a <code>join</code> function that does preserve the order:</p> <pre><code>library(plyr) join(dat, product.keys, by = "product") # product brand size # 1 11 Generic medium # 2 11 Generic medium # 3 9 Bayer large # 4 9 Bayer large # 5 6 Advil large # 6 1 Tylenol small # 7 11 Generic medium # 8 5 Advil medium # 9 7 Bayer small # 10 11 Generic medium # 11 5 Advil medium # 12 11 Generic medium # 13 4 Advil small # 14 3 Tylenol large # 15 10 Generic small # 16 7 Bayer small # 17 10 Generic small # 18 5 Advil medium # 19 9 Bayer large # 20 8 Bayer medium </code></pre> <p>Finally, if your tables are large and speed is an issue, consider using data.tables (from the <code>data.table</code> package) instead of data.frames.</p>

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