blocks|key|270086|text|一种想法是在两遍过程中对数据进行：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|270087|270088|运行第一遍，了解界限是什么，例如，给定百分位数的截取，或平均值之上的N个标准差，或者...|ordered-list-item|270089|在第二遍中，将超出给定界限的值设置为该界限|270090|的值|270091|我应该强调的是，这是一种老式的方法，应该由更现代的健壮技术主导，但您仍然经常遇到它。|270092|entityMap^0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|N|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|O|8|@]|9|@]|A|$]]|$1|C|3|D|5|E|7|P|8|@]|9|@]|A|$]]|$1|F|3|G|5|E|7|Q|8|@]|9|@]|A|$]]|$1|H|3|I|5|6|7|R|8|@]|9|@]|A|$]]|$1|J|3|K|5|6|7|S|8|@]|9|@]|A|$]]|$1|L|3|-4|5|6|7|T|8|@]|9|@]|A|$]]]|M|$]]

One idea would be to <a href="http://en.wikipedia.org/wiki/Winsorising" rel="noreferrer">winsorize</a> the data in a two-pass procedure:

<ol>
<li>run a first pass, learn what the bounds are, e.g. cut of at given percentile, or N standard deviation above the mean, or ...</li>
<li>in a second pass, set the values beyond the given bound to the value of that bound</li>
</ol>

I should stress that this is an old-fashioned method which ought to be dominated by more modern robust techniques but you still come across it a lot.

blocks|key|2973521|text|使用geom_boxplot(outlier.shape+=+NA)不显示异常值，使用scale_y_continuous(limits+=+c(lower,+upper))更改轴限制。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|2973522|举个例子。|2973523|n+<-+1e4L
dfr+<-+data.frame(
++y+=+exp(rlnorm(n)),++#really+right-skewed+variable
++f+=+gl(2,+n+/+2)
)

p+<-+ggplot(dfr,+aes(f,+y))+%2B+
++geom_boxplot()
p+++#+big+outlier+causes+quartiles+to+look+too+slim

p2+<-+ggplot(dfr,+aes(f,+y))+%2B+
++geom_boxplot(outlier.shape+=+NA)+%2B
++scale_y_continuous(limits+=+quantile(dfr$y,+c(0.1,+0.9)))
p2++#+no+outliers+plotted,+range+shifted|code-block|syntax|javascript|2973524|实际上，正如Ramnath在他的答案中(+Andrie也在评论中)所显示的那样，在计算完统计数据后，通过coord_cartesian来裁剪比例更有意义。|2973525|coord_cartesian(ylim+=+quantile(dfr$y,+c(0.1,+0.9)))|2973526|(您可能仍然需要使用scale_y_continuous来修复轴中断。)|2973527|entityMap|0|LINK|mutability|MUTABLE|url|https://www.rdocumentation.org/packages/ggplot2/topics/geom_boxplot|1|https://www.rdocumentation.org/packages/ggplot2/topics/scale_continuous|2|https://www.rdocumentation.org/packages/ggplot2/topics/coord_cartesian^0|2|W|17|18|2|W|0|17|18|1|0|0|0|1G|F|1G|F|2|0|0|A|I|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]|$9|1C|A|1D|1|1E]]|E|$]]|$1|F|3|G|5|6|7|1F|8|@]|D|@]|E|$]]|$1|H|3|I|5|J|7|1G|8|@]|D|@]|E|$K|L]]|$1|M|3|N|5|6|7|1H|8|@$9|1I|A|1J|B|C]]|D|@$9|1K|A|1L|1|1M]]|E|$]]|$1|O|3|P|5|J|7|1N|8|@]|D|@]|E|$K|L]]|$1|Q|3|R|5|6|7|1O|8|@$9|1P|A|1Q|B|C]]|D|@]|E|$]]|$1|S|3|-4|5|6|7|1R|8|@]|D|@]|E|$]]]|T|$U|$5|V|W|X|E|$Y|Z]]|10|$5|V|W|X|E|$Y|11]]|12|$5|V|W|X|E|$Y|13]]]]

Use <a href="https://www.rdocumentation.org/packages/ggplot2/topics/geom_boxplot" rel="noreferrer"><code>geom_boxplot(outlier.shape = NA)</code></a> to not display the outliers and <a href="https://www.rdocumentation.org/packages/ggplot2/topics/scale_continuous" rel="noreferrer"><code>scale_y_continuous(limits = c(lower, upper))</code></a> to change the axis limits.

An example.

<pre><code>n &lt;- 1e4L
dfr &lt;- data.frame(
 y = exp(rlnorm(n)), #really right-skewed variable
 f = gl(2, n / 2)
)

p &lt;- ggplot(dfr, aes(f, y)) + 
 geom_boxplot()
p # big outlier causes quartiles to look too slim

p2 &lt;- ggplot(dfr, aes(f, y)) + 
 geom_boxplot(outlier.shape = NA) +
 scale_y_continuous(limits = quantile(dfr$y, c(0.1, 0.9)))
p2 # no outliers plotted, range shifted
</code></pre>

Actually, as Ramnath showed in his answer (and Andrie too in the comments), it makes more sense to crop the scales after you calculate the statistic, via <a href="https://www.rdocumentation.org/packages/ggplot2/topics/coord_cartesian" rel="noreferrer"><code>coord_cartesian</code></a>.

<pre><code>coord_cartesian(ylim = quantile(dfr$y, c(0.1, 0.9)))
</code></pre>

(You'll probably still need to use <code>scale_y_continuous</code> to fix the axis breaks.)

blocks|key|2973532|text|这是一个使用boxplot.stats的解决方案|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2973533|#+create+a+dummy+data+frame+with+outliers
df+=+data.frame(y+=+c(-100,+rnorm(100),+100))

#+create+boxplot+that+includes+outliers
p0+=+ggplot(df,+aes(y+=+y))+%2B+geom_boxplot(aes(x+=+factor(1)))


#+compute+lower+and+upper+whiskers
ylim1+=+boxplot.stats(df$y)$stats[c(1,+5)]

#+scale+y+limits+based+on+ylim1
p1+=+p0+%2B+coord_cartesian(ylim+=+ylim1*1.05)|code-block|syntax|javascript|2973534|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

Here is a solution using boxplot.stats

<pre><code># create a dummy data frame with outliers
df = data.frame(y = c(-100, rnorm(100), 100))

# create boxplot that includes outliers
p0 = ggplot(df, aes(y = y)) + geom_boxplot(aes(x = factor(1)))


# compute lower and upper whiskers
ylim1 = boxplot.stats(df$y)$stats[c(1, 5)]

# scale y limits based on ylim1
p1 = p0 + coord_cartesian(ylim = ylim1*1.05)
</code></pre>

blocks|key|3187356|text|我也有同样的问题，我使用boxplot.stats预先计算了Q1，Q2，median，ymin，ymax的值|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|3187357|#+Load+package+and+generate+data
library(ggplot2)
data+<-+rnorm(100)

#+Compute+boxplot+statistics
stats+<-+boxplot.stats(data)$stats
df+<-+data.frame(x="label1",+ymin=stats[1],+lower=stats[2],+middle=stats[3],+
+++++++++++++++++upper=stats[4],+ymax=stats[5])

#+Create+plot
p+<-+ggplot(df,+aes(x=x,+lower=lower,+upper=upper,+middle=middle,+ymin=ymin,+
++++++++++++++++++++ymax=ymax))+%2B+
++++geom_boxplot(stat="identity")
p|code-block|syntax|javascript|3187358|结果是一个没有异常值的箱线图。|3187359|📷|atomic|3187360|​|3187361|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/5acb47a675d94cc9a55cfef717330aeb.png|imageAlt^0|C|D|0|0|0|0|1|0|0|0^^$0|@$1|2|3|4|5|6|7|10|8|@$9|11|A|12|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|13|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|14|8|@]|D|@]|E|$]]|$1|M|3|N|5|O|7|15|8|@]|D|@$9|16|A|17|1|18]]|E|$]]|$1|P|3|Q|5|6|7|19|8|@]|D|@]|E|$]]|$1|R|3|-4|5|6|7|1A|8|@]|D|@]|E|$]]]|S|$T|$5|U|V|W|E|$X|Y|Z|-4]]]]

I had the same problem and precomputed the values for Q1, Q2, median, ymin, ymax using <code>boxplot.stats</code>:

<pre><code># Load package and generate data
library(ggplot2)
data &lt;- rnorm(100)

# Compute boxplot statistics
stats &lt;- boxplot.stats(data)$stats
df &lt;- data.frame(x="label1", ymin=stats[1], lower=stats[2], middle=stats[3], 
 upper=stats[4], ymax=stats[5])

# Create plot
p &lt;- ggplot(df, aes(x=x, lower=lower, upper=upper, middle=middle, ymin=ymin, 
 ymax=ymax)) + 
 geom_boxplot(stat="identity")
p
</code></pre>

The result is a boxplot without outliers.
<a href="https://i.stack.imgur.com/vQwX4.png" rel="noreferrer"><img src="https://i.stack.imgur.com/vQwX4.png" alt="enter image description here"></a>

blocks|key|2973612|text|geom_boxplot函数的"coef“选项允许根据四分位数范围更改异常值截止值。此选项在函数stat_boxplot中有说明。要停用离群值(换句话说，它们被视为常规数据)，可以指定一个非常高的截止值，而不是使用默认值1.5：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2973613|library(ggplot2)
#+generate+data+with+outliers:
df+=+data.frame(x=1,+y+=+c(-10,+rnorm(100),+10))+
#+generate+plot+with+increased+cutoff+for+outliers:
ggplot(df,+aes(x,+y))+%2B+geom_boxplot(coef=1e30)|code-block|syntax|javascript|2973614|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

The "coef" option of the geom_boxplot function allows to change the outlier cutoff in terms of interquartile ranges. This option is documented for the function stat_boxplot. To deactivate outliers (in other words they are treated as regular data), one can instead of using the default value of 1.5 specify a very high cutoff value:

<pre><code>library(ggplot2)
# generate data with outliers:
df = data.frame(x=1, y = c(-10, rnorm(100), 10)) 
# generate plot with increased cutoff for outliers:
ggplot(df, aes(x, y)) + geom_boxplot(coef=1e30)
</code></pre>

blocks|key|3187388|text|如果要强制胡须延伸到最大值和最小值，可以调整coef参数。coef的默认值为1.5+(即胡须的默认长度是IQR的1.5倍)。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|3187389|#+Load+package+and+create+a+dummy+data+frame+with+outliers+
#(using+example+from+Ramnath's+answer+above)
library(ggplot2)
df+=+data.frame(y+=+c(-100,+rnorm(100),+100))

#+create+boxplot+that+includes+outliers
p0+=+ggplot(df,+aes(y+=+y))+%2B+geom_boxplot(aes(x+=+factor(1)))

#+create+boxplot+where+whiskers+extend+to+max+and+min+values
p1+=+ggplot(df,+aes(y+=+y))+%2B+geom_boxplot(aes(x+=+factor(1)),+coef+=+500)|code-block|syntax|javascript|3187390|​|3187391|📷|atomic|3187392|3187393|3187394|3187395|3187396|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/b3c87f775ab35f9d458482c2384f1a37.png|imageAlt|1|https://ask.qcloudimg.com/http-save/yehe-900000/a4a56461f34c205752c9b872e2218abb.png^0|M|4|T|4|0|0|0|0|1|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]|$9|17|A|18|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|19|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|1A|8|@]|D|@]|E|$]]|$1|M|3|N|5|O|7|1B|8|@]|D|@$9|1C|A|1D|1|1E]]|E|$]]|$1|P|3|L|5|6|7|1F|8|@]|D|@]|E|$]]|$1|Q|3|L|5|6|7|1G|8|@]|D|@]|E|$]]|$1|R|3|N|5|O|7|1H|8|@]|D|@$9|1I|A|1J|1|1K]]|E|$]]|$1|S|3|L|5|6|7|1L|8|@]|D|@]|E|$]]|$1|T|3|-4|5|6|7|1M|8|@]|D|@]|E|$]]]|U|$V|$5|W|X|Y|E|$Z|10|11|-4]]|12|$5|W|X|Y|E|$Z|13|11|-4]]]]

If you want to force the whiskers to extend to the max and min values, you can tweak the <code>coef</code> argument. Default value for <code>coef</code> is 1.5 (i.e. default length of the whiskers is 1.5 times the IQR).

<pre><code># Load package and create a dummy data frame with outliers 
#(using example from Ramnath's answer above)
library(ggplot2)
df = data.frame(y = c(-100, rnorm(100), 100))

# create boxplot that includes outliers
p0 = ggplot(df, aes(y = y)) + geom_boxplot(aes(x = factor(1)))

# create boxplot where whiskers extend to max and min values
p1 = ggplot(df, aes(y = y)) + geom_boxplot(aes(x = factor(1)), coef = 500)
</code></pre>

<a href="https://i.stack.imgur.com/PTCJp.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/PTCJp.png" alt="image of p0"></a>

<a href="https://i.stack.imgur.com/pjgdb.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/pjgdb.png" alt="image of p1"></a>

blocks|key|275675|text|Ipaper::geom_boxplot2正是您想要的。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|275676|#+devtools::install_github('kongdd/Ipaper')
library(Ipaper)
library(ggplot2)
p+<-+ggplot(mpg,+aes(class,+hwy))
p+%2B+geom_boxplot2(width+=+0.8,+width.errorbar+=+0.5)|code-block|syntax|javascript|275677|​|275678|📷|atomic|offset|length|275679|275680|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/3013a5da1768a57eb62b4c69153fd8ff.png|imageAlt^0|0|0|0|0|1|0|0|0^^$0|@$1|2|3|4|5|6|7|X|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|Y|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Z|8|@]|9|@]|A|$]]|$1|I|3|J|5|K|7|10|8|@]|9|@$L|11|M|12|1|13]]|A|$]]|$1|N|3|H|5|6|7|14|8|@]|9|@]|A|$]]|$1|O|3|-4|5|6|7|15|8|@]|9|@]|A|$]]]|P|$Q|$5|R|S|T|A|$U|V|W|-4]]]]

Ipaper::geom_boxplot2 is just what you want.

<pre class="lang-r prettyprint-override"><code># devtools::install_github('kongdd/Ipaper')
library(Ipaper)
library(ggplot2)
p &lt;- ggplot(mpg, aes(class, hwy))
p + geom_boxplot2(width = 0.8, width.errorbar = 0.5)
</code></pre>

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

blocks|key|3187438|text|简单，肮脏和有效。geom_boxplot(outlier.alpha+=+0)|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3187439|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

Simple, dirty and effective.
geom_boxplot(outlier.alpha = 0)

How would I ignore outliers in ggplot2 boxplot? I don't simply want them to disappear (i.e. outlier.size=0), but I want them to be ignored such that the y axis scales to show 1st/3rd percentile. My outliers are causing the "box" to shrink so small its practically a line. Are there some techniques to deal with this?

Edit
Here's an example:

<pre><code>y = c(.01, .02, .03, .04, .05, .06, .07, .08, .09, .5, -.6)
qplot(1, y, geom="boxplot")
</code></pre>

<a href="https://i.stack.imgur.com/mb3In.png" rel="noreferrer"><img src="https://i.stack.imgur.com/mb3In.png" alt="enter image description here"></a>

Ignore outliers in ggplot2 boxplot

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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如何忽略ggplot2箱线图中的异常值？我不是简单地希望它们消失(即outlier.size=0)，而是希望它们被忽略，以便y轴缩放到显示第一/第三个百分位数。我的异常值导致“盒子”缩小到几乎成了一条线。有什么技术可以解决这个问题吗？编辑示例如下：y = c(.01, .02, .03, .04, .05, .06, ...

问忽略ggplot2箱线图中的异常值
EN

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问忽略ggplot2箱线图中的异常值EN