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<table width="100%" summary="page for Hitters"><tr><td>Hitters</td><td align="right">R Documentation</td></tr></table>

<h2>Hitters Data</h2>

<h3>Description</h3>


<p>This data set is deduced from the <code>Baseball</code> fielding data
set: fielding performance basically includes the numbers of Errors,
Putouts and Assists made by each player.  In order to reduce the
number of observations, the was compressed by calculating the mean
number of errors, putouts and assists for each team and for only 6
positions (1B, 2B, 3B, C, OF, SS and UT).  In addition, each of these
three variables was scaled to a common range by dividing each variable
by the maximum of the variable.
</p>


<h3>Usage</h3>

<pre>data("Hitters")</pre>


<h3>Format</h3>


<p>A data frame with 154 observations and 4 variables.
</p>

<dl>
<dt>Positions</dt><dd><p>factor indicating the field position (1B=first
baseman, 2B=second baseman, 3B=third baseman, C=catcher,
OF=outfielder, SS=Short Stop, UT=Utility Players).</p>
</dd>
<dt>Putouts</dt><dd><p>occur when a fielder causes an opposing player to be
tagged or forced out.</p>
</dd>
<dt>Assists</dt><dd><p>are credited to other fielders involved in making
that putout.</p>
</dd>
<dt>Errors</dt><dd><p>count the errors made by a player.</p>
</dd>
</dl>



<h3>Source</h3>


<p>SAS System for Statistical Graphics, First Edition, Page A2.3
</p>


<h3>References</h3>


<p>M. Friendly (2000),
<EM>Visualizing Categorical Data</EM>.
SAS Institute, Cary, NC.
</p>


<h3>Examples</h3>

<pre>
data("Hitters")
attach(Hitters)

colors &lt;- c("black","red","green","blue","red","black","blue")
pch &lt;- substr(levels(Positions), 1, 1)
ternaryplot(Hitters[,2:4],
  pch = as.character(Positions),
  col = colors[as.numeric(Positions)],
  main = "Baseball Hitters Data")
grid_legend(0.8, 0.9, pch, colors, levels(Positions),
  title = "POSITION(S)")

detach(Hitters)
</pre>


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