EVOLUTION-MANAGER

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<table width="100%" summary="page for cast {reshape2}"><tr><td>cast {reshape2}</td><td style="text-align: right;">R Documentation</td></tr></table>

<h2>Cast functions
Cast a molten data frame into an array or data frame.</h2>

<h3>Description</h3>

<p>Use <code>acast</code> or <code>dcast</code> depending on whether you want
vector/matrix/array output or data frame output.  Data frames can have at
most two dimensions.
</p>

<h3>Usage</h3>

<pre>
dcast(
  data,
  formula,
  fun.aggregate = NULL,
  ...,
  margins = NULL,
  subset = NULL,
  fill = NULL,
  drop = TRUE,
  value.var = guess_value(data)
)

acast(
  data,
  formula,
  fun.aggregate = NULL,
  ...,
  margins = NULL,
  subset = NULL,
  fill = NULL,
  drop = TRUE,
  value.var = guess_value(data)
)
</pre>

<h3>Arguments</h3>

<table summary="R argblock">
<tr valign="top"><td><code>data</code></td>
<td>
<p>molten data frame, see <code><a href="melt.html">melt</a></code>.</p>
</td></tr>
<tr valign="top"><td><code>formula</code></td>
<td>
<p>casting formula, see details for specifics.</p>
</td></tr>
<tr valign="top"><td><code>fun.aggregate</code></td>
<td>
<p>aggregation function needed if variables do not
identify a single observation for each output cell.  Defaults to length
(with a message) if needed but not specified.</p>
</td></tr>
<tr valign="top"><td><code>...</code></td>
<td>
<p>further arguments are passed to aggregating function</p>
</td></tr>
<tr valign="top"><td><code>margins</code></td>
<td>
<p>vector of variable names (can include &quot;grand\_col&quot; and
&quot;grand\_row&quot;) to compute margins for, or TRUE to compute all margins .
Any variables that can not be margined over will be silently dropped.</p>
</td></tr>
<tr valign="top"><td><code>subset</code></td>
<td>
<p>quoted expression used to subset data prior to reshaping,
e.g. <code>subset = .(variable=="length")</code>.</p>
</td></tr>
<tr valign="top"><td><code>fill</code></td>
<td>
<p>value with which to fill in structural missings, defaults to
value from applying <code>fun.aggregate</code> to 0 length vector</p>
</td></tr>
<tr valign="top"><td><code>drop</code></td>
<td>
<p>should missing combinations dropped or kept?</p>
</td></tr>
<tr valign="top"><td><code>value.var</code></td>
<td>
<p>name of column which stores values, see
<code><a href="guess_value.html">guess_value</a></code> for default strategies to figure this out.</p>
</td></tr>
</table>

<h3>Details</h3>

<p>The cast formula has the following format:
<code>x_variable + x_2 ~ y_variable + y_2 ~ z_variable ~  ... </code>
The order of the variables makes a difference.  The first varies slowest,
and the last fastest.  There are a couple of special variables: &quot;...&quot;
represents all other variables not used in the formula and &quot;.&quot; represents
no variable, so you can do <code>formula = var1 ~ .</code>.
</p>
<p>Alternatively, you can supply a list of quoted expressions, in the form
<code>list(.(x_variable, x_2), .(y_variable, y_2), .(z))</code>.  The advantage
of this form is that you can cast based on transformations of the
variables: <code>list(.(a + b), (c = round(c)))</code>.  See the documentation
for <code><a href="../../plyr/html/..html">.</a></code> for more details and alternative formats.
</p>
<p>If the combination of variables you supply does not uniquely identify one
row in the original data set, you will need to supply an aggregating
function, <code>fun.aggregate</code>. This function should take a vector of
numbers and return a single summary statistic.
</p>

<p><code><a href="melt.html">melt</a></code>,  <a href="http://had.co.nz/reshape/">http://had.co.nz/reshape/</a>
</p>

<h3>Examples</h3>

<pre>
#Air quality example
names(airquality) &lt;- tolower(names(airquality))
aqm &lt;- melt(airquality, id=c("month", "day"), na.rm=TRUE)

acast(aqm, day ~ month ~ variable)
acast(aqm, month ~ variable, mean)
acast(aqm, month ~ variable, mean, margins = TRUE)
dcast(aqm, month ~ variable, mean, margins = c("month", "variable"))

library(plyr) # needed to access . function
acast(aqm, variable ~ month, mean, subset = .(variable == "ozone"))
acast(aqm, variable ~ month, mean, subset = .(month == 5))

#Chick weight example
names(ChickWeight) &lt;- tolower(names(ChickWeight))
chick_m &lt;- melt(ChickWeight, id=2:4, na.rm=TRUE)

dcast(chick_m, time ~ variable, mean) # average effect of time
dcast(chick_m, diet ~ variable, mean) # average effect of diet
acast(chick_m, diet ~ time, mean) # average effect of diet &amp; time

# How many chicks at each time? - checking for balance
acast(chick_m, time ~ diet, length)
acast(chick_m, chick ~ time, mean)
acast(chick_m, chick ~ time, mean, subset = .(time &lt; 10 &amp; chick &lt; 20))

acast(chick_m, time ~ diet, length)

dcast(chick_m, diet + chick ~ time)
acast(chick_m, diet + chick ~ time)
acast(chick_m, chick ~ time ~ diet)
acast(chick_m, diet + chick ~ time, length, margins="diet")
acast(chick_m, diet + chick ~ time, length, drop = FALSE)

#Tips example
dcast(melt(tips), sex ~ smoker, mean, subset = .(variable == "total_bill"))

ff_d &lt;- melt(french_fries, id=1:4, na.rm=TRUE)
acast(ff_d, subject ~ time, length)
acast(ff_d, subject ~ time, length, fill=0)
dcast(ff_d, treatment ~ variable, mean, margins = TRUE)
dcast(ff_d, treatment + subject ~ variable, mean, margins="treatment")
if (require("lattice")) {
 lattice::xyplot(`1` ~ `2` | variable, dcast(ff_d, ... ~ rep), aspect="iso")
}
</pre>

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