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    <p>Using <code>reshape2</code>:</p> <pre><code># Thanks to Ista for helping with direct naming using "variable.name" df.m &lt;- melt(df, id.var = c("ID", "Test", "Year"), variable.name = "Time") df.m &lt;- transform(df.m, Test = paste0("Test", Test)) dcast(df.m, ID + Year + Time ~ Test, value.var = "value") </code></pre> <hr> <h3>Update: Using data.table melt/cast from versions >= 1.9.0:</h3> <p><code>data.table</code> from versions 1.9.0 imports <code>reshape2</code> package and implements fast <code>melt</code> and <code>dcast</code> methods in C for data.tables. A comparison of speed on bigger data is shown below.</p> <p>For more info regarding NEWS, go <a href="https://stackoverflow.com/a/6913151/559784"><strong>here</strong></a>.</p> <pre><code>require(data.table) ## ver. &gt;=1.9.0 require(reshape2) dt &lt;- as.data.table(df, key=c("ID", "Test", "Year")) dt.m &lt;- melt(dt, id.var = c("ID", "Test", "Year"), variable.name = "Time") dt.m[, Test := paste0("Test", Test)] dcast.data.table(dt.m, ID + Year + Time ~ Test, value.var = "value") </code></pre> <p>At the moment, you'll have to write <code>dcast.data.table</code> explicitly as it's not a S3 generic in <code>reshape2</code> yet.</p> <hr> <h3>Benchmarking on bigger data:</h3> <pre><code># generate data: set.seed(45L) DT &lt;- data.table(ID = sample(1e2, 1e7, TRUE), Test = sample(1e3, 1e7, TRUE), Year = sample(2008:2014, 1e7,TRUE), Fall = sample(50, 1e7, TRUE), Spring = sample(50, 1e7,TRUE), Winter = sample(50, 1e7, TRUE)) DF &lt;- as.data.frame(DT) </code></pre> <h3>reshape2 timings:</h3> <pre><code>reshape2_melt &lt;- function(df) { df.m &lt;- melt(df, id.var = c("ID", "Test", "Year"), variable.name = "Time") } # min. of three consecutive runs system.time(df.m &lt;- reshape2_melt(DF)) # user system elapsed # 43.319 4.909 48.932 df.m &lt;- transform(df.m, Test = paste0("Test", Test)) reshape2_cast &lt;- function(df) { dcast(df.m, ID + Year + Time ~ Test, value.var = "value") } # min. of three consecutive runs system.time(reshape2_cast(df.m)) # user system elapsed # 57.728 9.712 69.573 </code></pre> <h3>data.table timings:</h3> <pre><code>DT_melt &lt;- function(dt) { dt.m &lt;- melt(dt, id.var = c("ID", "Test", "Year"), variable.name = "Time") } # min. of three consecutive runs system.time(dt.m &lt;- reshape2_melt(DT)) # user system elapsed # 0.276 0.001 0.279 dt.m[, Test := paste0("Test", Test)] DT_cast &lt;- function(dt) { dcast.data.table(dt.m, ID + Year + Time ~ Test, value.var = "value") } # min. of three consecutive runs system.time(DT_cast(dt.m)) # user system elapsed # 12.732 0.825 14.006 </code></pre> <p><code>melt.data.table</code> is <strong>~175x faster</strong> than <code>reshape2:::melt</code> and <code>dcast.data.table</code> is <strong>~5x</strong> than <code>reshape2:::dcast</code>.</p>
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    1. COWow, ... didn't know you could d that with data.table.
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