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    <p>You can use <code>reshape()</code> for the first step, but then you need to do some more work. Also, <code>reshape()</code> needs a <code>data.frame()</code> as its input, but your sample data is a matrix.</p> <p>Here's how to proceed:</p> <ol> <li><p><code>reshape()</code> your data from wide to long:</p> <pre><code>dat2 &lt;- reshape(data.frame(dat), direction = "long", idvar = c("id", "cohort"), varying = 3:ncol(dat), sep = "") dat2 # id cohort time s dur # 1.1.1 1 1 1 3 4 # 2.0.1 2 0 1 1 4 # 1.1.2 1 1 2 2 5 # 2.0.2 2 0 2 4 3 </code></pre></li> <li><p>"Expand" the resulting <code>data.frame</code> using <code>rep()</code></p> <pre><code>dat3 &lt;- dat2[rep(seq_len(nrow(dat2)), dat2$dur), c("id", "cohort", "s")] dat3[order(dat3$id), ] # id cohort s # 1.1.1 1 1 3 # 1.1.1.1 1 1 3 # 1.1.1.2 1 1 3 # 1.1.1.3 1 1 3 # 1.1.2 1 1 2 # 1.1.2.1 1 1 2 # 1.1.2.2 1 1 2 # 1.1.2.3 1 1 2 # 1.1.2.4 1 1 2 # 2.0.1 2 0 1 # 2.0.1.1 2 0 1 # 2.0.1.2 2 0 1 # 2.0.1.3 2 0 1 # 2.0.2 2 0 4 # 2.0.2.1 2 0 4 # 2.0.2.2 2 0 4 </code></pre></li> </ol> <p>You can get rid of the funky row names too by using <code>rownames(dat3) &lt;- NULL</code>.</p> <h2>Update: Retaining the ability to revert to the original form</h2> <p>In the example above, since we dropped the "time" and "dur" variables, it isn't possible to directly revert to the original dataset. If you feel this is something you would need to do, I suggest keeping those columns in and creating another <code>data.frame</code> with the subset of the columns that you need if required.</p> <p>Here's how:</p> <p>Use <code>aggregate()</code> to get back to "dat2":</p> <pre><code>aggregate(cbind(s, dur) ~ ., dat3, unique) # id cohort time s dur # 1 2 0 1 1 4 # 2 1 1 1 3 4 # 3 2 0 2 4 3 # 4 1 1 2 2 5 </code></pre> <p>Wrap <code>reshape()</code> around that to get back to "dat1". Here, in one step:</p> <pre><code>reshape(aggregate(cbind(s, dur) ~ ., dat3, unique), direction = "wide", idvar = c("id", "cohort")) # id cohort s.1 dur.1 s.2 dur.2 # 1 2 0 1 4 4 3 # 2 1 1 3 4 2 5 </code></pre>
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    1. POReshaping from wide to long and vice versa (multistate/survival analysis dataset)
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    1. COVery neat answer, thank you very much! May I ask you how to carry out the "reverse" operation? (i.e. getting `dat` starting from an initial `dat3` matrix).
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    2. CO@Stezzo, see my update. It basically involves using `aggregate()` and `reshape()`, but you would need to not drop any columns when creating "dat3".
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    3. COGreat! This helps a lot!
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