EVOLUTION-MANAGER
Edit File: geom_point.html
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><title>R: Points</title> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> <link rel="stylesheet" type="text/css" href="R.css" /> </head><body> <table width="100%" summary="page for geom_point {ggplot2}"><tr><td>geom_point {ggplot2}</td><td style="text-align: right;">R Documentation</td></tr></table> <h2>Points</h2> <h3>Description</h3> <p>The point geom is used to create scatterplots. The scatterplot is most useful for displaying the relationship between two continuous variables. It can be used to compare one continuous and one categorical variable, or two categorical variables, but a variation like <code><a href="geom_jitter.html">geom_jitter()</a></code>, <code><a href="geom_count.html">geom_count()</a></code>, or <code><a href="geom_bin2d.html">geom_bin2d()</a></code> is usually more appropriate. A <em>bubblechart</em> is a scatterplot with a third variable mapped to the size of points. </p> <h3>Usage</h3> <pre> geom_point( mapping = NULL, data = NULL, stat = "identity", position = "identity", ..., na.rm = FALSE, show.legend = NA, inherit.aes = TRUE ) </pre> <h3>Arguments</h3> <table summary="R argblock"> <tr valign="top"><td><code>mapping</code></td> <td> <p>Set of aesthetic mappings created by <code><a href="aes.html">aes()</a></code> or <code><a href="aes_.html">aes_()</a></code>. If specified and <code>inherit.aes = TRUE</code> (the default), it is combined with the default mapping at the top level of the plot. You must supply <code>mapping</code> if there is no plot mapping.</p> </td></tr> <tr valign="top"><td><code>data</code></td> <td> <p>The data to be displayed in this layer. There are three options: </p> <p>If <code>NULL</code>, the default, the data is inherited from the plot data as specified in the call to <code><a href="ggplot.html">ggplot()</a></code>. </p> <p>A <code>data.frame</code>, or other object, will override the plot data. All objects will be fortified to produce a data frame. See <code><a href="fortify.html">fortify()</a></code> for which variables will be created. </p> <p>A <code>function</code> will be called with a single argument, the plot data. The return value must be a <code>data.frame</code>, and will be used as the layer data. A <code>function</code> can be created from a <code>formula</code> (e.g. <code>~ head(.x, 10)</code>).</p> </td></tr> <tr valign="top"><td><code>stat</code></td> <td> <p>The statistical transformation to use on the data for this layer, as a string.</p> </td></tr> <tr valign="top"><td><code>position</code></td> <td> <p>Position adjustment, either as a string, or the result of a call to a position adjustment function.</p> </td></tr> <tr valign="top"><td><code>...</code></td> <td> <p>Other arguments passed on to <code><a href="layer.html">layer()</a></code>. These are often aesthetics, used to set an aesthetic to a fixed value, like <code>colour = "red"</code> or <code>size = 3</code>. They may also be parameters to the paired geom/stat.</p> </td></tr> <tr valign="top"><td><code>na.rm</code></td> <td> <p>If <code>FALSE</code>, the default, missing values are removed with a warning. If <code>TRUE</code>, missing values are silently removed.</p> </td></tr> <tr valign="top"><td><code>show.legend</code></td> <td> <p>logical. Should this layer be included in the legends? <code>NA</code>, the default, includes if any aesthetics are mapped. <code>FALSE</code> never includes, and <code>TRUE</code> always includes. It can also be a named logical vector to finely select the aesthetics to display.</p> </td></tr> <tr valign="top"><td><code>inherit.aes</code></td> <td> <p>If <code>FALSE</code>, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. <code><a href="borders.html">borders()</a></code>.</p> </td></tr> </table> <h3>Overplotting</h3> <p>The biggest potential problem with a scatterplot is overplotting: whenever you have more than a few points, points may be plotted on top of one another. This can severely distort the visual appearance of the plot. There is no one solution to this problem, but there are some techniques that can help. You can add additional information with <code><a href="geom_smooth.html">geom_smooth()</a></code>, <code><a href="geom_quantile.html">geom_quantile()</a></code> or <code><a href="geom_density_2d.html">geom_density_2d()</a></code>. If you have few unique <code>x</code> values, <code><a href="geom_boxplot.html">geom_boxplot()</a></code> may also be useful. </p> <p>Alternatively, you can summarise the number of points at each location and display that in some way, using <code><a href="geom_count.html">geom_count()</a></code>, <code><a href="geom_hex.html">geom_hex()</a></code>, or <code><a href="geom_density_2d.html">geom_density2d()</a></code>. </p> <p>Another technique is to make the points transparent (e.g. <code>geom_point(alpha = 0.05)</code>) or very small (e.g. <code>geom_point(shape = ".")</code>). </p> <h3>Aesthetics</h3> <p><code>geom_point()</code> understands the following aesthetics (required aesthetics are in bold): </p> <ul> <li> <p><strong><code>x</code></strong> </p> </li> <li> <p><strong><code>y</code></strong> </p> </li> <li> <p><code>alpha</code> </p> </li> <li> <p><code>colour</code> </p> </li> <li> <p><code>fill</code> </p> </li> <li> <p><code>group</code> </p> </li> <li> <p><code>shape</code> </p> </li> <li> <p><code>size</code> </p> </li> <li> <p><code>stroke</code> </p> </li></ul> <p>Learn more about setting these aesthetics in <code>vignette("ggplot2-specs")</code>. </p> <h3>Examples</h3> <pre> p <- ggplot(mtcars, aes(wt, mpg)) p + geom_point() # Add aesthetic mappings p + geom_point(aes(colour = factor(cyl))) p + geom_point(aes(shape = factor(cyl))) # A "bubblechart": p + geom_point(aes(size = qsec)) # Set aesthetics to fixed value ggplot(mtcars, aes(wt, mpg)) + geom_point(colour = "red", size = 3) # Varying alpha is useful for large datasets d <- ggplot(diamonds, aes(carat, price)) d + geom_point(alpha = 1/10) d + geom_point(alpha = 1/20) d + geom_point(alpha = 1/100) # For shapes that have a border (like 21), you can colour the inside and # outside separately. Use the stroke aesthetic to modify the width of the # border ggplot(mtcars, aes(wt, mpg)) + geom_point(shape = 21, colour = "black", fill = "white", size = 5, stroke = 5) # You can create interesting shapes by layering multiple points of # different sizes p <- ggplot(mtcars, aes(mpg, wt, shape = factor(cyl))) p + geom_point(aes(colour = factor(cyl)), size = 4) + geom_point(colour = "grey90", size = 1.5) p + geom_point(colour = "black", size = 4.5) + geom_point(colour = "pink", size = 4) + geom_point(aes(shape = factor(cyl))) # geom_point warns when missing values have been dropped from the data set # and not plotted, you can turn this off by setting na.rm = TRUE mtcars2 <- transform(mtcars, mpg = ifelse(runif(32) < 0.2, NA, mpg)) ggplot(mtcars2, aes(wt, mpg)) + geom_point() ggplot(mtcars2, aes(wt, mpg)) + geom_point(na.rm = TRUE) </pre> <hr /><div style="text-align: center;">[Package <em>ggplot2</em> version 3.3.2 <a href="00Index.html">Index</a>]</div> </body></html>