blocks|key|759914|text|解决这个问题的一种方法是使用alpha混合，它使每个点都稍微透明一些。因此，绘制了更多点的区域看起来更暗。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|759915|这在ggplot2中很容易做到|offset|length|style|CODE|759916|df+<-+data.frame(x+=+rnorm(5000),y=rnorm(5000))
ggplot(df,aes(x=x,y=y))+%2B+geom_point(alpha+=+0.3)|code-block|syntax|javascript|759917|​|759918|📷|atomic|759919|759920|处理这个问题的另一种方便的方法是(可能更适合于您拥有的点数)是六边形装箱：|759921|ggplot(df,aes(x=x,y=y))+%2B+stat_binhex()|759922|759923|759924|759925|还有常规的旧矩形装箱(省略图像)，它更像你的传统热图：|759926|ggplot(df,aes(x=x,y=y))+%2B+geom_bin2d()|759927|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/7acac33fd122b933445a2f68d2c0ad46.png|imageAlt|1|https://ask.qcloudimg.com/http-save/yehe-900000/28f21009ff9942eb5f8a791756f7cd37.png^0|0|2|7|0|0|0|0|1|0|0|0|0|0|0|0|1|1|0|0|0|0^^$0|@$1|2|3|4|5|6|7|1E|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|1F|8|@$D|1G|E|1H|F|G]]|9|@]|A|$]]|$1|H|3|I|5|J|7|1I|8|@]|9|@]|A|$K|L]]|$1|M|3|N|5|6|7|1J|8|@]|9|@]|A|$]]|$1|O|3|P|5|Q|7|1K|8|@]|9|@$D|1L|E|1M|1|1N]]|A|$]]|$1|R|3|N|5|6|7|1O|8|@]|9|@]|A|$]]|$1|S|3|T|5|6|7|1P|8|@]|9|@]|A|$]]|$1|U|3|V|5|J|7|1Q|8|@]|9|@]|A|$K|L]]|$1|W|3|N|5|6|7|1R|8|@]|9|@]|A|$]]|$1|X|3|P|5|Q|7|1S|8|@]|9|@$D|1T|E|1U|1|1V]]|A|$]]|$1|Y|3|N|5|6|7|1W|8|@]|9|@]|A|$]]|$1|Z|3|10|5|6|7|1X|8|@]|9|@]|A|$]]|$1|11|3|12|5|J|7|1Y|8|@]|9|@]|A|$K|L]]|$1|13|3|-4|5|6|7|1Z|8|@]|9|@]|A|$]]]|14|$15|$5|16|17|18|A|$19|1A|1B|-4]]|1C|$5|16|17|18|A|$19|1D|1B|-4]]]]

<p>One way to deal with this is with alpha blending, which makes each point slightly transparent. So regions appear darker that have more point plotted on them.</p>

<p>This is easy to do in <code>ggplot2</code>:</p>

<pre><code>df &lt;- data.frame(x = rnorm(5000),y=rnorm(5000))
ggplot(df,aes(x=x,y=y)) + geom_point(alpha = 0.3)
</code></pre>

<p><img src="https://i.stack.imgur.com/PJbMn.png" alt="enter image description here"></p>

<p>Another convenient way to deal with this is (and probably more appropriate for the number of points you have) is hexagonal binning:</p>

<pre><code>ggplot(df,aes(x=x,y=y)) + stat_binhex()
</code></pre>

<p><img src="https://i.stack.imgur.com/XyWw1.png" alt="enter image description here"></p>

<p>And there is also regular old rectangular binning (image omitted), which is more like your traditional heatmap:</p>

<pre><code>ggplot(df,aes(x=x,y=y)) + geom_bin2d()
</code></pre>


blocks|key|543420|text|还可以使用密度等高线(ggplot2)：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|543421|df+<-+data.frame(x+=+rnorm(15000),y=rnorm(15000))
ggplot(df,aes(x=x,y=y))+%2B+geom_point()+%2B+geom_density2d()|code-block|syntax|javascript|543422|​|543423|📷|atomic|543424|543425|或者将密度轮廓与alpha混合相结合：|543426|ggplot(df,aes(x=x,y=y))+%2B+
++++geom_point(colour="blue",+alpha=0.2)+%2B+
++++geom_density2d(colour="black")|543427|543428|543429|543430|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/191e0f37a321d5432d5423f7edbd88eb.png|imageAlt|1|https://ask.qcloudimg.com/http-save/yehe-900000/03ba99a9f1fbd4239bf30d198803f96b.png^0|B|7|0|0|0|0|1|0|0|0|0|0|0|0|1|1|0|0^^$0|@$1|2|3|4|5|6|7|18|8|@$9|19|A|1A|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|1B|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|1C|8|@]|D|@]|E|$]]|$1|M|3|N|5|O|7|1D|8|@]|D|@$9|1E|A|1F|1|1G]]|E|$]]|$1|P|3|L|5|6|7|1H|8|@]|D|@]|E|$]]|$1|Q|3|R|5|6|7|1I|8|@]|D|@]|E|$]]|$1|S|3|T|5|H|7|1J|8|@]|D|@]|E|$I|J]]|$1|U|3|L|5|6|7|1K|8|@]|D|@]|E|$]]|$1|V|3|N|5|O|7|1L|8|@]|D|@$9|1M|A|1N|1|1O]]|E|$]]|$1|W|3|L|5|6|7|1P|8|@]|D|@]|E|$]]|$1|X|3|-4|5|6|7|1Q|8|@]|D|@]|E|$]]]|Y|$Z|$5|10|11|12|E|$13|14|15|-4]]|16|$5|10|11|12|E|$13|17|15|-4]]]]

<p>You can also use density contour lines (<code>ggplot2</code>):</p>

<pre><code>df &lt;- data.frame(x = rnorm(15000),y=rnorm(15000))
ggplot(df,aes(x=x,y=y)) + geom_point() + geom_density2d()
</code></pre>

<p><img src="https://i.stack.imgur.com/TMpNp.png" alt="enter image description here"></p>

<p>Or combine density contours with alpha blending:</p>

<pre><code>ggplot(df,aes(x=x,y=y)) + 
    geom_point(colour="blue", alpha=0.2) + 
    geom_density2d(colour="black")
</code></pre>

<p><img src="https://i.stack.imgur.com/HIEtd.png" alt="enter image description here"></p>


blocks|key|760089|text|您可能会发现hexbin包很有用。从hexbinplot的帮助页面|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|760090|library(hexbin)
mixdata+<-+data.frame(x+=+c(rnorm(5000),rnorm(5000,4,1.5)),
++++++++++++++++++++++y+=+c(rnorm(5000),rnorm(5000,2,3)),
++++++++++++++++++++++a+=+gl(2,+5000))
hexbinplot(y+~+x+%7C+a,+mixdata)|code-block|syntax|javascript|760091|​|760092|📷|atomic|760093|760094|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/782a36de93fb4d13314f77186a48c99c.png|imageAlt^0|6|6|I|A|0|0|0|0|1|0|0|0^^$0|@$1|2|3|4|5|6|7|Z|8|@$9|10|A|11|B|C]|$9|12|A|13|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|14|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|15|8|@]|D|@]|E|$]]|$1|M|3|N|5|O|7|16|8|@]|D|@$9|17|A|18|1|19]]|E|$]]|$1|P|3|L|5|6|7|1A|8|@]|D|@]|E|$]]|$1|Q|3|-4|5|6|7|1B|8|@]|D|@]|E|$]]]|R|$S|$5|T|U|V|E|$W|X|Y|-4]]]]

<p>You may find useful the <code>hexbin</code> package. From the help page of <code>hexbinplot</code>:</p>

<pre><code>library(hexbin)
mixdata &lt;- data.frame(x = c(rnorm(5000),rnorm(5000,4,1.5)),
                      y = c(rnorm(5000),rnorm(5000,2,3)),
                      a = gl(2, 5000))
hexbinplot(y ~ x | a, mixdata)
</code></pre>

<p><img src="https://i.stack.imgur.com/v4o3W.png" alt="hexbinplot"></p>


blocks|key|543611|text|Alpha混合也很容易处理基础图形。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|543612|df+<-+data.frame(x+=+rnorm(5000),y=rnorm(5000))
with(df,+plot(x,+y,+col="#00000033"))|code-block|syntax|javascript|543613|#之后的前六个数字是RGB十六进制的颜色，后两个是不透明度，同样是十六进制，所以33+~3/16不透明。|offset|length|style|CODE|543614|​|543615|📷|atomic|543616|543617|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/9036885979338b0d9c4c2941b0e51ff7.png|imageAlt^0|0|0|0|1|0|0|0|1|0|0|0^^$0|@$1|2|3|4|5|6|7|11|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|12|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|13|8|@$I|14|J|15|K|L]]|9|@]|A|$]]|$1|M|3|N|5|6|7|16|8|@]|9|@]|A|$]]|$1|O|3|P|5|Q|7|17|8|@]|9|@$I|18|J|19|1|1A]]|A|$]]|$1|R|3|N|5|6|7|1B|8|@]|9|@]|A|$]]|$1|S|3|-4|5|6|7|1C|8|@]|9|@]|A|$]]]|T|$U|$5|V|W|X|A|$Y|Z|10|-4]]]]

<p>Alpha blending is easy to do with base graphics as well.</p>

<pre><code>df &lt;- data.frame(x = rnorm(5000),y=rnorm(5000))
with(df, plot(x, y, col="#00000033"))
</code></pre>

<p>The first six numbers after the <code>#</code> are the color in RGB hex and the last two are the opacity, again in hex, so 33 ~ 3/16th opaque.</p>

<p><img src="https://i.stack.imgur.com/5G4sa.png" alt="enter image description here"></p>


blocks|key|543806|text|您还可以查看ggsubplot包。这个包实现了Hadley+Wickham在2011年提出的特性(http://blog.revolutionanalytics.com/2011/10/ggplot2-for-big-data.html)。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|543807|(在下面，为了说明的目的，我包含了"points"-layer。)|543808|library(ggplot2)
library(ggsubplot)

#+Make+up+some+data
set.seed(955)
dat+<-+data.frame(cond+=+rep(c("A",+"B"),+each=5000),
++++++++++++++++++xvar+=+c(rep(1:20,250)+%2B+rnorm(5000,sd=5),rep(16:35,250)+%2B+rnorm(5000,sd=5)),
++++++++++++++++++yvar+=+c(rep(1:20,250)+%2B+rnorm(5000,sd=5),rep(16:35,250)+%2B+rnorm(5000,sd=5)))


#+Scatterplot+with+subplots+(simple)
ggplot(dat,+aes(x=xvar,+y=yvar))+%2B
++geom_point(shape=1)+%2B
++geom_subplot2d(aes(xvar,+yvar,
+++++++++++++++++++++subplot+=+geom_bar(aes(rep("dummy",+length(xvar)),+..count..))),+bins+=+c(15,15),+ref+=+NULL,+width+=+rel(0.8),+ply.aes+=+FALSE)|code-block|syntax|javascript|543809|​|543810|📷|atomic|543811|543812|然而，如果你有第三个变量需要控制，这个特性就很重要。|543813|#+Scatterplot+with+subplots+(including+a+third+variable)+

ggplot(dat,+aes(x=xvar,+y=yvar))+%2B
++geom_point(shape=1,+aes(color+=+factor(cond)))+%2B
++geom_subplot2d(aes(xvar,+yvar,
+++++++++++++++++++++subplot+=+geom_bar(aes(cond,+..count..,+fill+=+cond))),
+++++++++++++++++bins+=+c(15,15),+ref+=+NULL,+width+=+rel(0.8),+ply.aes+=+FALSE)++|543814|543815|543816|543817|或者，另一种方法是使用smoothScatter()|543818|smoothScatter(dat[2:3])|543819|543820|543821|543822|entityMap|0|LINK|mutability|MUTABLE|url|http://blog.revolutionanalytics.com/2011/10/ggplot2-for-big-data.html|1|IMAGE|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/62e1b6f4671c87f09893f28dac4c1f36.png|imageAlt|2|https://ask.qcloudimg.com/http-save/yehe-900000/912cbfb14516a0f00c66d374e1fed2f7.png|3|https://ask.qcloudimg.com/http-save/yehe-900000/18fb428789ad917f7e42c08aeb97c347.png^0|6|9|1D|1X|0|0|0|0|0|0|1|1|0|0|0|0|0|0|1|2|0|0|B|F|0|0|0|0|1|3|0|0^^$0|@$1|2|3|4|5|6|7|1O|8|@$9|1P|A|1Q|B|C]]|D|@$9|1R|A|1S|1|1T]]|E|$]]|$1|F|3|G|5|6|7|1U|8|@]|D|@]|E|$]]|$1|H|3|I|5|J|7|1V|8|@]|D|@]|E|$K|L]]|$1|M|3|N|5|6|7|1W|8|@]|D|@]|E|$]]|$1|O|3|P|5|Q|7|1X|8|@]|D|@$9|1Y|A|1Z|1|20]]|E|$]]|$1|R|3|N|5|6|7|21|8|@]|D|@]|E|$]]|$1|S|3|T|5|6|7|22|8|@]|D|@]|E|$]]|$1|U|3|V|5|J|7|23|8|@]|D|@]|E|$K|L]]|$1|W|3|N|5|6|7|24|8|@]|D|@]|E|$]]|$1|X|3|P|5|Q|7|25|8|@]|D|@$9|26|A|27|1|28]]|E|$]]|$1|Y|3|N|5|6|7|29|8|@]|D|@]|E|$]]|$1|Z|3|10|5|6|7|2A|8|@$9|2B|A|2C|B|C]]|D|@]|E|$]]|$1|11|3|12|5|J|7|2D|8|@]|D|@]|E|$K|L]]|$1|13|3|N|5|6|7|2E|8|@]|D|@]|E|$]]|$1|14|3|P|5|Q|7|2F|8|@]|D|@$9|2G|A|2H|1|2I]]|E|$]]|$1|15|3|N|5|6|7|2J|8|@]|D|@]|E|$]]|$1|16|3|-4|5|6|7|2K|8|@]|D|@]|E|$]]]|17|$18|$5|19|1A|1B|E|$1C|1D]]|1E|$5|1F|1A|1G|E|$1H|1I|1J|-4]]|1K|$5|1F|1A|1G|E|$1H|1L|1J|-4]]|1M|$5|1F|1A|1G|E|$1H|1N|1J|-4]]]]

<p>You can also have a look at the <code>ggsubplot</code> package. This package implements features which were presented by Hadley Wickham back in 2011 (<a href="http://blog.revolutionanalytics.com/2011/10/ggplot2-for-big-data.html" rel="noreferrer">http://blog.revolutionanalytics.com/2011/10/ggplot2-for-big-data.html</a>).</p>

<p>(In the following, I include the "points"-layer for illustration purposes.)</p>

<pre><code>library(ggplot2)
library(ggsubplot)

# Make up some data
set.seed(955)
dat &lt;- data.frame(cond = rep(c("A", "B"), each=5000),
                  xvar = c(rep(1:20,250) + rnorm(5000,sd=5),rep(16:35,250) + rnorm(5000,sd=5)),
                  yvar = c(rep(1:20,250) + rnorm(5000,sd=5),rep(16:35,250) + rnorm(5000,sd=5)))


# Scatterplot with subplots (simple)
ggplot(dat, aes(x=xvar, y=yvar)) +
  geom_point(shape=1) +
  geom_subplot2d(aes(xvar, yvar,
                     subplot = geom_bar(aes(rep("dummy", length(xvar)), ..count..))), bins = c(15,15), ref = NULL, width = rel(0.8), ply.aes = FALSE)
</code></pre>

<p><img src="https://i.stack.imgur.com/t7yKA.png" alt="enter image description here"></p>

<p>However, this features rocks if you have a third variable to control for.</p>

<pre><code># Scatterplot with subplots (including a third variable) 

ggplot(dat, aes(x=xvar, y=yvar)) +
  geom_point(shape=1, aes(color = factor(cond))) +
  geom_subplot2d(aes(xvar, yvar,
                     subplot = geom_bar(aes(cond, ..count.., fill = cond))),
                 bins = c(15,15), ref = NULL, width = rel(0.8), ply.aes = FALSE)  
</code></pre>

<p><img src="https://i.stack.imgur.com/9YKym.png" alt="enter image description here"></p>

<p>Or another approach would be to use <code>smoothScatter()</code>:</p>

<pre><code>smoothScatter(dat[2:3])
</code></pre>

<p><img src="https://i.stack.imgur.com/ntfa9.png" alt="enter image description here"></p>


blocks|key|543909|text|ggplot2中几个很好的选项概述|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|543910|library(ggplot2)
x+<-+rnorm(n+=+10000)
y+<-+rnorm(n+=+10000,+sd=2)+%2B+x
df+<-+data.frame(x,+y)|code-block|syntax|javascript|543911|选项A:透明点|543912|o1+<-+ggplot(df,+aes(x,+y))+%2B
++geom_point(alpha+=+0.05)|543913|选项B:添加密度等值线|543914|o2+<-+ggplot(df,+aes(x,+y))+%2B
++geom_point(alpha+=+0.05)+%2B
++geom_density_2d()|543915|选项C:添加填充密度等值线|543916|o3+<-+ggplot(df,+aes(x,+y))+%2B
++stat_density_2d(aes(fill+=+stat(level)),+geom+=+'polygon')+%2B
++scale_fill_viridis_c(name+=+"density")+%2B
++geom_point(shape+=+'.')|543917|选项D:密度热图|543918|o4+<-+ggplot(df,+aes(x,+y))+%2B
++stat_density_2d(aes(fill+=+stat(density)),+geom+=+'raster',+contour+=+FALSE)+%2B+++++++
++scale_fill_viridis_c()+%2B
++coord_cartesian(expand+=+FALSE)+%2B
++geom_point(shape+=+'.',+col+=+'white')|543919|选项E:六进制数|543920|o5+<-+ggplot(df,+aes(x,+y))+%2B
++geom_hex()+%2B
++scale_fill_viridis_c()+%2B
++geom_point(shape+=+'.',+col+=+'white')|543921|选项F:地毯|543922|o6+<-+ggplot(df,+aes(x,+y))+%2B
++geom_point(alpha+=+0.1)+%2B
++geom_rug(alpha+=+0.01)|543923|合并成一个图形：|543924|cowplot::plot_grid(
++o1,+o2,+o3,+o4,+o5,+o6,
++ncol+=+2,+labels+=+'AUTO',+align+=+'v',+axis+=+'lr'
)|543925|​|543926|📷|atomic|543927|543928|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/e892f383b30f169e3303b2159f25f66b.jpeg|imageAlt^0|0|7|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0^^$0|@$1|2|3|4|5|6|7|1R|8|@$9|1S|A|1T|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|1U|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|1V|8|@]|D|@]|E|$]]|$1|M|3|N|5|H|7|1W|8|@]|D|@]|E|$I|J]]|$1|O|3|P|5|6|7|1X|8|@]|D|@]|E|$]]|$1|Q|3|R|5|H|7|1Y|8|@]|D|@]|E|$I|J]]|$1|S|3|T|5|6|7|1Z|8|@]|D|@]|E|$]]|$1|U|3|V|5|H|7|20|8|@]|D|@]|E|$I|J]]|$1|W|3|X|5|6|7|21|8|@]|D|@]|E|$]]|$1|Y|3|Z|5|H|7|22|8|@]|D|@]|E|$I|J]]|$1|10|3|11|5|6|7|23|8|@]|D|@]|E|$]]|$1|12|3|13|5|H|7|24|8|@]|D|@]|E|$I|J]]|$1|14|3|15|5|6|7|25|8|@]|D|@]|E|$]]|$1|16|3|17|5|H|7|26|8|@]|D|@]|E|$I|J]]|$1|18|3|19|5|6|7|27|8|@]|D|@]|E|$]]|$1|1A|3|1B|5|H|7|28|8|@]|D|@]|E|$I|J]]|$1|1C|3|1D|5|6|7|29|8|@]|D|@]|E|$]]|$1|1E|3|1F|5|1G|7|2A|8|@]|D|@$9|2B|A|2C|1|2D]]|E|$]]|$1|1H|3|1D|5|6|7|2E|8|@]|D|@]|E|$]]|$1|1I|3|-4|5|6|7|2F|8|@]|D|@]|E|$]]]|1J|$1K|$5|1L|1M|1N|E|$1O|1P|1Q|-4]]]]

<p>An overview of several good options in <code>ggplot2</code>:</p>

<pre><code>library(ggplot2)
x &lt;- rnorm(n = 10000)
y &lt;- rnorm(n = 10000, sd=2) + x
df &lt;- data.frame(x, y)
</code></pre>

<h3>Option A: transparent points</h3>

<pre><code>o1 &lt;- ggplot(df, aes(x, y)) +
  geom_point(alpha = 0.05)
</code></pre>

<h3>Option B: add density contours</h3>

<pre><code>o2 &lt;- ggplot(df, aes(x, y)) +
  geom_point(alpha = 0.05) +
  geom_density_2d()
</code></pre>

<h3>Option C: add filled density contours</h3>

<pre><code>o3 &lt;- ggplot(df, aes(x, y)) +
  stat_density_2d(aes(fill = stat(level)), geom = 'polygon') +
  scale_fill_viridis_c(name = "density") +
  geom_point(shape = '.')
</code></pre>

<h3>Option D: density heatmap</h3>

<pre><code>o4 &lt;- ggplot(df, aes(x, y)) +
  stat_density_2d(aes(fill = stat(density)), geom = 'raster', contour = FALSE) +       
  scale_fill_viridis_c() +
  coord_cartesian(expand = FALSE) +
  geom_point(shape = '.', col = 'white')
</code></pre>

<h3>Option E: hexbins</h3>

<pre><code>o5 &lt;- ggplot(df, aes(x, y)) +
  geom_hex() +
  scale_fill_viridis_c() +
  geom_point(shape = '.', col = 'white')
</code></pre>

<h3>Option F: rugs</h3>

<pre><code>o6 &lt;- ggplot(df, aes(x, y)) +
  geom_point(alpha = 0.1) +
  geom_rug(alpha = 0.01)
</code></pre>

<p>Combine in one figure:</p>

<pre><code>cowplot::plot_grid(
  o1, o2, o3, o4, o5, o6,
  ncol = 2, labels = 'AUTO', align = 'v', axis = 'lr'
)
</code></pre>

<p><a href="https://i.stack.imgur.com/AY8jM.jpg" rel="noreferrer"><img src="https://i.stack.imgur.com/AY8jM.jpg" alt="enter image description here"></a></p>


blocks|key|760535|text|来自ggpointdensity+package的geom_pointdenisty+(最近由Lukas+Kremer和Simon+Anders+(2019)开发)允许您同时可视化密度和单个数据点：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|760536|library(ggplot2)
#+install.packages("ggpointdensity")
library(ggpointdensity)

df+<-+data.frame(x+=+rnorm(5000),+y+=+rnorm(5000))
ggplot(df,+aes(x=x,+y=y))+%2B+geom_pointdensity()+%2B+scale_color_viridis_c()|code-block|syntax|javascript|760537|​|760538|📷|atomic|760539|760540|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/LKremer/ggpointdensity|1|IMAGE|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/4099b4f93cdfe5753906cfe9ce2089f9.png|imageAlt^0|2|E|P|H|2|M|0|0|0|0|0|1|1|0|0^^$0|@$1|2|3|4|5|6|7|14|8|@$9|15|A|16|B|C]|$9|17|A|18|B|C]]|D|@$9|19|A|1A|1|1B]]|E|$]]|$1|F|3|G|5|H|7|1C|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|1D|8|@]|D|@]|E|$]]|$1|M|3|N|5|O|7|1E|8|@]|D|@$9|1F|A|1G|1|1H]]|E|$]]|$1|P|3|L|5|6|7|1I|8|@]|D|@]|E|$]]|$1|Q|3|-4|5|6|7|1J|8|@]|D|@]|E|$]]]|R|$S|$5|T|U|V|E|$W|X]]|Y|$5|Z|U|10|E|$11|12|13|-4]]]]

<p><code>geom_pointdenisty</code> from the <a href="https://github.com/LKremer/ggpointdensity" rel="noreferrer"><code>ggpointdensity</code> package</a> (recently developed by Lukas Kremer and Simon Anders (2019)) allows you visualize density and individual data points at the same time:</p>

<pre class="lang-r prettyprint-override"><code>library(ggplot2)
# install.packages("ggpointdensity")
library(ggpointdensity)

df &lt;- data.frame(x = rnorm(5000), y = rnorm(5000))
ggplot(df, aes(x=x, y=y)) + geom_pointdensity() + scale_color_viridis_c()
</code></pre>

<p><img src="https://i.imgur.com/myVWdsT.png" alt=""></p>


<p>I am trying to plot two variables where N=700K. The problem is that there is too much overlap, so that the plot becomes mostly a solid block of black. Is there any way of having a grayscale "cloud" where the darkness of the plot is a function of the number of points in an region? In other words, instead of showing individual points, I want the plot to be a "cloud", with the more the number of points in a region, the darker that region.</p>


Scatterplot with too many points

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我正在尝试绘制两个变量，其中N=700K。问题是有太多的重叠，因此绘图主要是一个实心的黑色区块。有没有办法得到一个灰度“云”，其中绘图的暗度是一个区域中点数的函数？换句话说，我不想显示单独的点，而是想要一个“云”，一个区域中的点数越多，该区域就越暗。

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