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    <p>I know you asked for a "plyr"-specific solution, but for the sake of sharing, here is an alternative approach in base R. In my opinion, I find the base R approach just as "readable". And, at least in this particular case, it's a lot faster!</p> <pre><code>output &lt;- within(df1, { yoy &lt;- ave(ab, team, lg, FUN = function(x) c(NA, diff(x))) }) head(output) # year lg team ab yoy # 1 1884 UA ALT 108 NA # 2 1997 AL ANA 1703 NA # 3 1998 AL ANA 1502 -201 # 4 1999 AL ANA 660 -842 # 5 2000 AL ANA 85 -575 # 6 2001 AL ANA 219 134 library(rbenchmark) benchmark(DDPLY = { ddply(df1, .(team, lg), mutate , yoy = c(NA, diff(ab))) }, WITHIN = { within(df1, { yoy &lt;- ave(ab, team, lg, FUN = function(x) c(NA, diff(x))) }) }, columns = c("test", "replications", "elapsed", "relative", "user.self")) # test replications elapsed relative user.self # 1 DDPLY 100 10.675 4.974 10.609 # 2 WITHIN 100 2.146 1.000 2.128 </code></pre> <h2>Update: <code>data.table</code></h2> <p>If your data are very large, check out <code>data.table</code>. Even with this example, you'll find a good speedup in relative terms. Plus the syntax is super compact and, in my opinion, easily readable.</p> <pre><code>library(plyr) df1 &lt;- aggregate(ab~year+lg+team, FUN=sum, data=baseball) library(data.table) DT &lt;- data.table(df1) DT # year lg team ab # 1: 1884 UA ALT 108 # 2: 1997 AL ANA 1703 # 3: 1998 AL ANA 1502 # 4: 1999 AL ANA 660 # 5: 2000 AL ANA 85 # --- # 2523: 1895 NL WSN 839 # 2524: 1896 NL WSN 982 # 2525: 1897 NL WSN 1426 # 2526: 1898 NL WSN 1736 # 2527: 1899 NL WSN 787 </code></pre> <p>Now, look at this concise solution:</p> <pre><code>DT[, yoy := c(NA, diff(ab)), by = "team,lg"] DT # year lg team ab yoy # 1: 1884 UA ALT 108 NA # 2: 1997 AL ANA 1703 NA # 3: 1998 AL ANA 1502 -201 # 4: 1999 AL ANA 660 -842 # 5: 2000 AL ANA 85 -575 # --- # 2523: 1895 NL WSN 839 290 # 2524: 1896 NL WSN 982 143 # 2525: 1897 NL WSN 1426 444 # 2526: 1898 NL WSN 1736 310 # 2527: 1899 NL WSN 787 -949 </code></pre>
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    1. CO+1 for ave(). :)
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    2. COthanks Amanda, great idea. I'm pretty new to R and am trying to learn Plyr but in no way wed to using it so I appreciate your suggestion. Is there anyone from that Plyr camp that offer any reasons why Plyr might be a better solution?
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    3. CO@MikeTP, I'm afraid that if someone from the "data.table" camp comes along with a `data.table` solution (which would be very easy), you'd be changing accepted answers again! I think it really comes down to preference and consistency of syntax. `plyr` and `data.table` both have their own syntax that might be a bit different from a base R approach, and for some, the constructs might just seem more natural.
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