blocks|key|3957226|text|通过加载tidyr包，可以对数据进行整形以支持预期输出。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3957227|library(ggplot2)
library(tidyr)|code-block|syntax|javascript|3957228|在读取具有列的正确类的数据之后(+Channel和Region的系数，而其余六个字段是数值)，检查数据的正确性。|3957229|df+<-+read.csv(file+=+url('https://archive.ics.uci.edu/ml/machine-learning-databases/00292/Wholesale%2520customers%2520data.csv'),+colClasses+=+c('factor','factor','numeric','numeric','numeric','numeric','numeric','numeric'))|3957230|str(df)|3957231|'data.frame':+++440+obs.+of++8+variables:
+$+Channel+++++++++:+Factor+w/+2+levels+"1","2":+2+2+2+1+2+2+2+2+1+2+...
+$+Region++++++++++:+Factor+w/+3+levels+"1","2","3":+3+3+3+3+3+3+3+3+3+3+...
+$+Fresh+++++++++++:+num++12669+7057+6353+13265+22615+...
+$+Milk++++++++++++:+num++9656+9810+8808+1196+5410+...
+$+Grocery+++++++++:+num++7561+9568+7684+4221+7198+...
+$+Frozen++++++++++:+num++214+1762+2405+6404+3915+...
+$+Detergents_Paper:+num++2674+3293+3516+507+1777+...
+$+Delicassen++++++:+num++1338+1776+7844+1788+5185+...|3957232|head(df)|3957233|++Channel+Region+Fresh+Milk+Grocery+Frozen+Detergents_Paper+Delicassen
1+++++++2++++++3+12669+9656++++7561++++214+++++++++++++2674+++++++1338
2+++++++2++++++3++7057+9810++++9568+++1762+++++++++++++3293+++++++1776
3+++++++2++++++3++6353+8808++++7684+++2405+++++++++++++3516+++++++7844
4+++++++1++++++3+13265+1196++++4221+++6404++++++++++++++507+++++++1788
5+++++++2++++++3+22615+5410++++7198+++3915+++++++++++++1777+++++++5185
6+++++++2++++++3++9413+8259++++5126++++666+++++++++++++1795+++++++1451|3957234|数据似乎已正确导入。|3957235|接下来，我们结合使用tidyr::gather和ggplot2::ggplot来生成所需的条形图(而不是直方图)。|3957236|df+%25>%25
++tidyr::gather(Type,+Amount,+-c(Channel,+Region))+%25>%25
++ggplot(aes(x=Type,+y=Amount,+fill=Channel,+group=Channel))+%2B
++++geom_col(position+=+position_dodge())|3957237|​|3957238|📷|atomic|offset|length|3957239|3957240|tidyr::gather(Type,+Amount,+-c(Channel,+Region))将从以下位置重塑数据集：|style|CODE|3957241|++Channel+Region+Fresh+Milk+Grocery+Frozen+Detergents_Paper+Delicassen
1+++++++2++++++3+12669+9656++++7561++++214+++++++++++++2674+++++++1338
2+++++++2++++++3++7057+9810++++9568+++1762+++++++++++++3293+++++++1776
3+++++++2++++++3++6353+8808++++7684+++2405+++++++++++++3516+++++++7844
4+++++++1++++++3+13265+1196++++4221+++6404++++++++++++++507+++++++1788
5+++++++2++++++3+22615+5410++++7198+++3915+++++++++++++1777+++++++5185
6+++++++2++++++3++9413+8259++++5126++++666+++++++++++++1795+++++++1451|3957242|到一个“较长”的数据集，它现在的product类型为rows：|3957243|++Channel+Region++Type+Amount
1+++++++2++++++3+Fresh++12669
2+++++++2++++++3+Fresh+++7057
3+++++++2++++++3+Fresh+++6353
4+++++++1++++++3+Fresh++13265
5+++++++2++++++3+Fresh++22615
6+++++++2++++++3+Fresh+++9413|3957244|这将使用ggplot2::ggplot准备要绘制的数据，其中x输入可以映射到新的Type变量，y变量可以映射到Amount。一定要使用Group=Channel和position=position_dodge()，这样ggplot就会知道你想让条形图并排出现。|3957245|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/dfe4de21a89388f94000d811591d3872.png|imageAlt^0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|1C|0|0|0|0|1V|D|29|P|0^^$0|@$1|2|3|4|5|6|7|1R|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|1S|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|1T|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|1U|8|@]|9|@]|A|$E|F]]|$1|K|3|L|5|D|7|1V|8|@]|9|@]|A|$E|F]]|$1|M|3|N|5|D|7|1W|8|@]|9|@]|A|$E|F]]|$1|O|3|P|5|D|7|1X|8|@]|9|@]|A|$E|F]]|$1|Q|3|R|5|D|7|1Y|8|@]|9|@]|A|$E|F]]|$1|S|3|T|5|6|7|1Z|8|@]|9|@]|A|$]]|$1|U|3|V|5|6|7|20|8|@]|9|@]|A|$]]|$1|W|3|X|5|D|7|21|8|@]|9|@]|A|$E|F]]|$1|Y|3|Z|5|6|7|22|8|@]|9|@]|A|$]]|$1|10|3|11|5|12|7|23|8|@]|9|@$13|24|14|25|1|26]]|A|$]]|$1|15|3|Z|5|6|7|27|8|@]|9|@]|A|$]]|$1|16|3|17|5|6|7|28|8|@$13|29|14|2A|18|19]]|9|@]|A|$]]|$1|1A|3|1B|5|D|7|2B|8|@]|9|@]|A|$E|F]]|$1|1C|3|1D|5|6|7|2C|8|@]|9|@]|A|$]]|$1|1E|3|1F|5|D|7|2D|8|@]|9|@]|A|$E|F]]|$1|1G|3|1H|5|6|7|2E|8|@$13|2F|14|2G|18|19]|$13|2H|14|2I|18|19]]|9|@]|A|$]]|$1|1I|3|-4|5|6|7|2J|8|@]|9|@]|A|$]]]|1J|$1K|$5|1L|1M|1N|A|$1O|1P|1Q|-4]]]]

By loading the tidyr package, the data can be reshaped to support the expected output.

<pre class="lang-r prettyprint-override"><code>library(ggplot2)
library(tidyr)
</code></pre>

After reading in the data with the correct classes for the columns (factors for Channel and Region while the remaining six fields are numeric), check the data for correctness.

<pre class="lang-r prettyprint-override"><code>df &lt;- read.csv(file = url('https://archive.ics.uci.edu/ml/machine-learning-databases/00292/Wholesale%20customers%20data.csv'), colClasses = c('factor','factor','numeric','numeric','numeric','numeric','numeric','numeric'))
</code></pre>

<pre class="lang-r prettyprint-override"><code>str(df)
</code></pre>

<pre class="lang-r prettyprint-override"><code>'data.frame': 440 obs. of 8 variables:
 $ Channel : Factor w/ 2 levels "1","2": 2 2 2 1 2 2 2 2 1 2 ...
 $ Region : Factor w/ 3 levels "1","2","3": 3 3 3 3 3 3 3 3 3 3 ...
 $ Fresh : num 12669 7057 6353 13265 22615 ...
 $ Milk : num 9656 9810 8808 1196 5410 ...
 $ Grocery : num 7561 9568 7684 4221 7198 ...
 $ Frozen : num 214 1762 2405 6404 3915 ...
 $ Detergents_Paper: num 2674 3293 3516 507 1777 ...
 $ Delicassen : num 1338 1776 7844 1788 5185 ...
</code></pre>

<pre class="lang-r prettyprint-override"><code>head(df)
</code></pre>

<pre class="lang-r prettyprint-override"><code> Channel Region Fresh Milk Grocery Frozen Detergents_Paper Delicassen
1 2 3 12669 9656 7561 214 2674 1338
2 2 3 7057 9810 9568 1762 3293 1776
3 2 3 6353 8808 7684 2405 3516 7844
4 1 3 13265 1196 4221 6404 507 1788
5 2 3 22615 5410 7198 3915 1777 5185
6 2 3 9413 8259 5126 666 1795 1451
</code></pre>

The data appear to have imported correctly.

Next we use a combination of tidyr::gather and ggplot2::ggplot to produce the desired bar plot (not histogram).

<pre class="lang-r prettyprint-override"><code>df %&gt;%
 tidyr::gather(Type, Amount, -c(Channel, Region)) %&gt;%
 ggplot(aes(x=Type, y=Amount, fill=Channel, group=Channel)) +
 geom_col(position = position_dodge())
</code></pre>

<a href="https://i.stack.imgur.com/HM7xs.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/HM7xs.png" alt="enter image description here"></a>

<code>tidyr::gather(Type, Amount, -c(Channel, Region))</code> will reshape the data set from this:

<pre class="lang-r prettyprint-override"><code> Channel Region Fresh Milk Grocery Frozen Detergents_Paper Delicassen
1 2 3 12669 9656 7561 214 2674 1338
2 2 3 7057 9810 9568 1762 3293 1776
3 2 3 6353 8808 7684 2405 3516 7844
4 1 3 13265 1196 4221 6404 507 1788
5 2 3 22615 5410 7198 3915 1777 5185
6 2 3 9413 8259 5126 666 1795 1451
</code></pre>

To a "longer" data set which now has the product type as rows:

<pre class="lang-r prettyprint-override"><code> Channel Region Type Amount
1 2 3 Fresh 12669
2 2 3 Fresh 7057
3 2 3 Fresh 6353
4 1 3 Fresh 13265
5 2 3 Fresh 22615
6 2 3 Fresh 9413
</code></pre>

This prepares the data to be plotted using ggplot2::ggplot where the x input can be mapped to the new Type variable and the y variable to Amount.
Be sure to use <code>Group=Channel</code> and <code>position=position_dodge()</code> so that ggplot will know that you want the bars side-by-side.

I'm working with this dataset: <a href="https://archive.ics.uci.edu/ml/datasets/Wholesale+customers" rel="nofollow noreferrer">https://archive.ics.uci.edu/ml/datasets/Wholesale+customers</a>

I want to plot a histogram with all the variables that have the amount spent (everything but region and channel). I'd like them to be plotted by channel, which has 2 levels. I got the following code from an example on a website, but put in my variables: 

<pre><code>category=c(rep("Fresh",2),rep("Grocery",2),rep("Milk",2),rep("Frozen",2),
 rep("Detergents_Paper",2),rep("Delicassen",2))
condition=rep(c("Food Service", "Retail"))
value=abs(rnorm(12 , 0 , 15))
data=data.frame(category,condition,value)

ggplot(data, aes(fill=condition, y=value, x=category)) + 
 geom_bar(position="dodge", stat="identity")
</code></pre>

This produces what I want, but it doesn't use my data. Here's the graph I get, but the values don't mean anything since they're basically random. 

<img src="https://i.stack.imgur.com/baGnc.png" alt="Multivariate Histogram">

How do I get my data to plot like this?

Plot multiple variables on one barplot?

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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我正在使用这个数据集：我想要绘制一个直方图，其中包含所有已花费的变量(除了区域和渠道之外的所有变量)。我希望它们是按通道绘制的，它有2个级别。我从一个网站的示例中获得了以下代码，但将其放入了我的变量中：category=c(rep("Fresh",2),rep("Grocery",2),rep("Milk",2),re...

在一个柱状图上绘制多个变量？-腾讯云开发者社区-腾讯云

问在一个柱状图上绘制多个变量？
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