blocks|key|1722695|text|您可以通过修改scale_x_continuous和/或scale_y_continuous来覆盖ggplots的默认比例。例如：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1722696|library(ggplot2)
dat+<-+data.frame(x+=+rnorm(100),+y+=+rnorm(100))

ggplot(dat,+aes(x,y))+%2B
++geom_point()|code-block|syntax|javascript|1722697|为您提供以下内容：|1722698|​|1722699|📷|atomic|1722700|1722701|覆盖天平可能会给你带来这样的结果：|1722702|ggplot(dat,+aes(x,y))+%2B
++geom_point()+%2B
++scale_x_continuous(breaks+=+round(seq(min(dat$x),+max(dat$x),+by+=+0.5),1))+%2B
++scale_y_continuous(breaks+=+round(seq(min(dat$y),+max(dat$y),+by+=+0.5),1))|1722703|1722704|1722705|1722706|如果您想简单地“放大”绘图的特定部分，请分别查看xlim()和ylim()。也可以在here中找到好的洞察力来理解其他论点。|1722707|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/787303e4eea8a45a11c12b25c18f9cba.jpeg|imageAlt|1|https://ask.qcloudimg.com/http-save/yehe-900000/3ead38c1d1ad2b62cc31bfd7125dc917.jpeg|2|LINK|MUTABLE|url|http://had.co.nz/ggplot2/scale_continuous.html^0|7|I|S|I|0|0|0|0|0|1|0|0|0|0|0|0|0|1|1|0|0|O|6|V|6|16|4|2|0^^$0|@$1|2|3|4|5|6|7|1H|8|@$9|1I|A|1J|B|C]|$9|1K|A|1L|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|1M|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|1N|8|@]|D|@]|E|$]]|$1|M|3|N|5|6|7|1O|8|@]|D|@]|E|$]]|$1|O|3|P|5|Q|7|1P|8|@]|D|@$9|1Q|A|1R|1|1S]]|E|$]]|$1|R|3|N|5|6|7|1T|8|@]|D|@]|E|$]]|$1|S|3|T|5|6|7|1U|8|@]|D|@]|E|$]]|$1|U|3|V|5|H|7|1V|8|@]|D|@]|E|$I|J]]|$1|W|3|N|5|6|7|1W|8|@]|D|@]|E|$]]|$1|X|3|P|5|Q|7|1X|8|@]|D|@$9|1Y|A|1Z|1|20]]|E|$]]|$1|Y|3|N|5|6|7|21|8|@]|D|@]|E|$]]|$1|Z|3|10|5|6|7|22|8|@$9|23|A|24|B|C]|$9|25|A|26|B|C]]|D|@$9|27|A|28|1|29]]|E|$]]|$1|11|3|-4|5|6|7|2A|8|@]|D|@]|E|$]]]|12|$13|$5|14|15|16|E|$17|18|19|-4]]|1A|$5|14|15|16|E|$17|1B|19|-4]]|1C|$5|1D|15|1E|E|$1F|1G]]]]

You can override ggplots default scales by modifying <code>scale_x_continuous</code> and/or <code>scale_y_continuous</code>. For example:

<pre><code>library(ggplot2)
dat &lt;- data.frame(x = rnorm(100), y = rnorm(100))

ggplot(dat, aes(x,y)) +
 geom_point()
</code></pre>

Gives you this:

<img src="https://i.stack.imgur.com/d5jFT.jpg" alt="enter image description here">

And overriding the scales can give you something like this:

<pre><code>ggplot(dat, aes(x,y)) +
 geom_point() +
 scale_x_continuous(breaks = round(seq(min(dat$x), max(dat$x), by = 0.5),1)) +
 scale_y_continuous(breaks = round(seq(min(dat$y), max(dat$y), by = 0.5),1))
</code></pre>

<img src="https://i.stack.imgur.com/brQ5z.jpg" alt="enter image description here">

If you want to simply "zoom" in on a specific part of a plot, look at <code>xlim()</code> and <code>ylim()</code> respectively. Good insight can also be found <a href="http://had.co.nz/ggplot2/scale_continuous.html" rel="noreferrer">here</a> to understand the other arguments as well.

blocks|key|1722726|text|您可以向scale提供一个函数参数，ggplot将使用该函数来计算刻度位置。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1722727|library(ggplot2)
dat+<-+data.frame(x+=+rnorm(100),+y+=+rnorm(100))
number_ticks+<-+function(n)+{function(limits)+pretty(limits,+n)}

ggplot(dat,+aes(x,y))+%2B
++geom_point()+%2B
++scale_x_continuous(breaks=number_ticks(10))+%2B
++scale_y_continuous(breaks=number_ticks(10))|code-block|syntax|javascript|1722728|entityMap^0|4|5|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|P|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|Q|8|@]|D|@]|E|$]]]|L|$]]

You can supply a function argument to <code>scale</code>, and ggplot will use
that function to calculate the tick locations.

<pre><code>library(ggplot2)
dat &lt;- data.frame(x = rnorm(100), y = rnorm(100))
number_ticks &lt;- function(n) {function(limits) pretty(limits, n)}

ggplot(dat, aes(x,y)) +
 geom_point() +
 scale_x_continuous(breaks=number_ticks(10)) +
 scale_y_continuous(breaks=number_ticks(10))
</code></pre>

blocks|key|1936374|text|基于Daniel+Krizian's+comment，您还可以使用自动导入的scales库中的pretty_breaks函数：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1936375|ggplot(dat,+aes(x,y))+%2B+geom_point()+%2B
scale_x_continuous(breaks+=+scales::pretty_breaks(n+=+10))+%2B
scale_y_continuous(breaks+=+scales::pretty_breaks(n+=+10))|code-block|syntax|javascript|1936376|您所要做的就是插入n所需的刻度数。|1936377|1936378|一个稍微不太有用的解决方案(因为您必须再次指定数据变量)，您可以使用内置的pretty函数：|1936379|ggplot(dat,+aes(x,y))+%2B+geom_point()+%2B
scale_x_continuous(breaks+=+pretty(dat$x,+n+=+10))+%2B
scale_y_continuous(breaks+=+pretty(dat$y,+n+=+10))|1936380|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/questions/11335836/increase-number-of-axis-ticks-in-ggplot2#comment32214370_17257422|1|https://www.rdocumentation.org/packages/scales/versions/0.4.1/topics/pretty_breaks^0|12|6|1B|D|2|O|0|1B|D|1|0|0|9|1|0|0|11|6|0|0^^$0|@$1|2|3|4|5|6|7|11|8|@$9|12|A|13|B|C]|$9|14|A|15|B|C]]|D|@$9|16|A|17|1|18]|$9|19|A|1A|1|1B]]|E|$]]|$1|F|3|G|5|H|7|1C|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|1D|8|@$9|1E|A|1F|B|C]]|D|@]|E|$]]|$1|M|3|-4|5|6|7|1G|8|@]|D|@]|E|$]]|$1|N|3|O|5|6|7|1H|8|@$9|1I|A|1J|B|C]]|D|@]|E|$]]|$1|P|3|Q|5|H|7|1K|8|@]|D|@]|E|$I|J]]|$1|R|3|-4|5|6|7|1L|8|@]|D|@]|E|$]]]|S|$T|$5|U|V|W|E|$X|Y]]|Z|$5|U|V|W|E|$X|10]]]]

Based on <a href="https://stackoverflow.com/questions/11335836/increase-number-of-axis-ticks-in-ggplot2#comment32214370_17257422">Daniel Krizian's comment</a>, you can also use the <a href="https://www.rdocumentation.org/packages/scales/versions/0.4.1/topics/pretty_breaks" rel="noreferrer"><code>pretty_breaks</code></a> function from the <code>scales</code> library, which is imported automatically:

<pre><code>ggplot(dat, aes(x,y)) + geom_point() +
scale_x_continuous(breaks = scales::pretty_breaks(n = 10)) +
scale_y_continuous(breaks = scales::pretty_breaks(n = 10))
</code></pre>

All you have to do is insert the number of ticks wanted for <code>n</code>.

<hr>

A slightly less useful solution (since you have to specify the data variable again), you can use the built-in <code>pretty</code> function:

<pre><code>ggplot(dat, aes(x,y)) + geom_point() +
scale_x_continuous(breaks = pretty(dat$x, n = 10)) +
scale_y_continuous(breaks = pretty(dat$y, n = 10))
</code></pre>

blocks|key|1936422|text|另外，|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1936423|ggplot(dat,+aes(x,y))+%2B
geom_point()+%2B
scale_x_continuous(breaks+=+seq(min(dat$x),+max(dat$x),+by+=+0.05))|code-block|syntax|javascript|1936424|适用于二进制或离散缩放的x轴数据(即，不需要舍入)。|1936425|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

Additionally,

<pre><code>ggplot(dat, aes(x,y)) +
geom_point() +
scale_x_continuous(breaks = seq(min(dat$x), max(dat$x), by = 0.05))
</code></pre>

Works for binned or discrete scaled x-axis data (I.e., rounding not necessary).

blocks|key|1723260|text|从v3.3.0开始，ggplot2具有一个选项n.breaks，可自动为scale_x_continuous和scale_y_continuous生成中断|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1723261|++++library(ggplot2)

++++plt+<-+ggplot(mtcars,+aes(x+=+mpg,+y+=+disp))+%2B
++++++geom_point()

++++plt+%2B+
++++++scale_x_continuous(n.breaks+=+5)|code-block|syntax|javascript|1723262|​|1723263|📷|atomic|1723264|1723265|++++plt+%2B+
++++++scale_x_continuous(n.breaks+=+10)+%2B
++++++scale_y_continuous(n.breaks+=+10)|1723266|1723267|1723268|1723269|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/2f3d95520f4032d7fabb7fca5a244437.png|imageAlt|1|https://ask.qcloudimg.com/http-save/yehe-900000/e1ee686eeb723d90deec0f3ff80d87bb.png^0|A|7|N|8|10|I|1J|I|0|0|0|0|1|0|0|0|0|0|0|1|1|0|0^^$0|@$1|2|3|4|5|6|7|16|8|@$9|17|A|18|B|C]|$9|19|A|1A|B|C]|$9|1B|A|1C|B|C]|$9|1D|A|1E|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|1F|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|1G|8|@]|D|@]|E|$]]|$1|M|3|N|5|O|7|1H|8|@]|D|@$9|1I|A|1J|1|1K]]|E|$]]|$1|P|3|L|5|6|7|1L|8|@]|D|@]|E|$]]|$1|Q|3|R|5|H|7|1M|8|@]|D|@]|E|$I|J]]|$1|S|3|L|5|6|7|1N|8|@]|D|@]|E|$]]|$1|T|3|N|5|O|7|1O|8|@]|D|@$9|1P|A|1Q|1|1R]]|E|$]]|$1|U|3|L|5|6|7|1S|8|@]|D|@]|E|$]]|$1|V|3|-4|5|6|7|1T|8|@]|D|@]|E|$]]]|W|$X|$5|Y|Z|10|E|$11|12|13|-4]]|14|$5|Y|Z|10|E|$11|15|13|-4]]]]

Starting from v3.3.0, <code>ggplot2</code> has an option <code>n.breaks</code> to automatically generate breaks for <code>scale_x_continuous</code> and <code>scale_y_continuous</code>
<pre><code> library(ggplot2)

 plt &lt;- ggplot(mtcars, aes(x = mpg, y = disp)) +
 geom_point()

 plt + 
 scale_x_continuous(n.breaks = 5)
</code></pre>
<a href="https://i.stack.imgur.com/RUxXg.png" rel="noreferrer"><img src="https://i.stack.imgur.com/RUxXg.png" alt="enter image description here" /></a>
<pre><code> plt + 
 scale_x_continuous(n.breaks = 10) +
 scale_y_continuous(n.breaks = 10)
</code></pre>
<a href="https://i.stack.imgur.com/pyTrw.png" rel="noreferrer"><img src="https://i.stack.imgur.com/pyTrw.png" alt="enter image description here" /></a>

blocks|key|1936881|text|对这个问题的回答和How+set+labels+on+the+X+and+Y+axises+by+equal+intervals+in+R+ggplot?|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1936882|mtcars+%25>%25+
++ggplot(aes(mpg,+disp))+%2B
++geom_point()+%2B
++geom_smooth()+%2B+
++scale_y_continuous(limits+=+c(0,+500),
+++++++++++++++++++++breaks+=+seq(0,500,50))++%2B
++scale_x_continuous(limits+=+c(0,40),
+++++++++++++++++++++breaks+=+seq(0,40,5))|code-block|syntax|javascript|1936883|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/questions/69410603/how-set-labels-on-the-x-and-y-axises-by-equal-intervals-in-r-ggplot^0|9|1W|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@$A|R|B|S|1|T]]|C|$]]|$1|D|3|E|5|F|7|U|8|@]|9|@]|C|$G|H]]|$1|I|3|-4|5|6|7|V|8|@]|9|@]|C|$]]]|J|$K|$5|L|M|N|C|$O|P]]]]

A reply to this question and <a href="https://stackoverflow.com/questions/69410603/how-set-labels-on-the-x-and-y-axises-by-equal-intervals-in-r-ggplot">How set labels on the X and Y axises by equal intervals in R ggplot?</a>
<pre><code>mtcars %&gt;% 
 ggplot(aes(mpg, disp)) +
 geom_point() +
 geom_smooth() + 
 scale_y_continuous(limits = c(0, 500),
 breaks = seq(0,500,50)) +
 scale_x_continuous(limits = c(0,40),
 breaks = seq(0,40,5))
</code></pre>

I'm generating plots for some data, but the number of ticks is too small, I need more precision on the reading.

Is there some way to increase the number of axis ticks in ggplot2?

I know I can tell ggplot to use a vector as axis ticks, but what I want is to increase the number of ticks, for all data. In other words, I want the tick number to be calculated from the data.

Possibly ggplot do this internally with some algorithm, but I couldn't find how it does it, to change according to what I want.

Increase number of axis ticks

我正在为一些数据生成曲线图，但刻度数太少，我需要更精确的读数。有什么方法可以增加ggplot2中轴刻度的数量吗？我知道我可以告诉ggplot使用一个向量作为轴刻度，但我想要的是增加所有数据的刻度数。换句话说，我希望从数据中计算出刻度数。也许ggplot在内部使用了一些算法，但我找不到它是如何做到这一点的，根据我想要的改...

问增加轴刻度数
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