用R[复制]做直方图中的y轴对数

时间：2021-05-23 15:00:19

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Hi I'm making histogram using R, but the number of Y axis is so large that I need to turn it into logarithmic.See below my script:

你好，我用R做直方图，但是Y轴的数目太大了，我需要把它变成对数。见下面我的脚本:

hplot<-read.table("libl")
hplot
pdf("first_end")
hist(hplot$V1, breaks=24, xlim=c(0,250000000), ylim=c(0,2000000),main="first end mapping", xlab="Coordinates")
dev.off()

So how should I change my script? thx

那么，我该如何修改我的脚本呢?谢谢

4 个解决方案

#1

18  

You can save the histogram data to tweak it before plotting:

您可以保存直方图数据，在绘制之前对其进行调整:

set.seed(12345)
x = rnorm(1000)

hist.data = hist(x, plot=F)
hist.data$counts = log10(hist.data$counts)

dev.new(width=4, height=4)
hist(x)

dev.new(width=4, height=4)
plot(hist.data, ylab='log10(Frequency)')

用R[复制]做直方图中的y轴对数

用R[复制]做直方图中的y轴对数

#2

4  

A histogram with the y-axis on the log scale will be a rather odd histogram. Technically it will still fit the definition, but it could look rather misleading: the peaks will be flattened relative to the rest of the distribution.

在对数尺度上的y轴的直方图将是一个相当奇怪的直方图。从技术上讲，它仍然符合这个定义，但它看起来很有误导性:相对于分布的其余部分，峰值将被压平。

Instead of using a log transformation, have you considered:

您是否考虑过使用日志转换?

  • Dividing the counts by 1 million:

    将总数除以100万:

    h <- hist(hplot$V1, plot=FALSE)

    h < -嘘(hplot $ V1,情节= FALSE)

    h$counts <- h$counts/1e6

    h数< - h数/ 1美元e6

    plot(h)

    情节(h)

  • Plotting the histogram as a density estimate:

    将直方图绘制成密度估计:

    hist(hplot$V1, freq=FALSE)

    嘘(hplot $ V1,频率= FALSE)

#3

1  

Another option would be to use plot(density(hplot$V1), log="y").

另一个选项是使用plot(密度(hplot$V1)， log="y")。

It's not a histogram, but it shows just about the same information, and it avoids the illogical part where a bin with zero counts is not well-defined in log-space.

它不是直方图，但它只显示了相同的信息，并且它避免了逻辑部分，在日志空间中，没有定义为零的bin。

Of course, this is only relevant when your data is continuous and not when it's really categorical or ordinal.

当然，只有当你的数据是连续的，而不是真正的分类或序数时，这才有意义。

#4

1  

You can log your y-values for the plot and add a custom log y-axis afterwards.

您可以为该绘图记录y值，然后添加自定义的日志y轴。

Here is an example for a table object of random normal distribution numbers:

下面是一个关于随机正态分布的表对象的例子:

# data
count = table(round(rnorm(10000)*2))
# plot
plot(log(count) ,type="h",  yaxt="n", xlab="position", ylab="log(count)")
# axis labels
yAxis = c(0,1,10,100,1000)
# draw axis labels
axis(2, at=log(yAxis),labels=yAxis, las=2)

用R[复制]做直方图中的y轴对数

#1

18  

You can save the histogram data to tweak it before plotting:

您可以保存直方图数据，在绘制之前对其进行调整:

set.seed(12345)
x = rnorm(1000)

hist.data = hist(x, plot=F)
hist.data$counts = log10(hist.data$counts)

dev.new(width=4, height=4)
hist(x)

dev.new(width=4, height=4)
plot(hist.data, ylab='log10(Frequency)')

用R[复制]做直方图中的y轴对数

用R[复制]做直方图中的y轴对数

#2

4  

A histogram with the y-axis on the log scale will be a rather odd histogram. Technically it will still fit the definition, but it could look rather misleading: the peaks will be flattened relative to the rest of the distribution.

在对数尺度上的y轴的直方图将是一个相当奇怪的直方图。从技术上讲，它仍然符合这个定义，但它看起来很有误导性:相对于分布的其余部分，峰值将被压平。

Instead of using a log transformation, have you considered:

您是否考虑过使用日志转换?

  • Dividing the counts by 1 million:

    将总数除以100万:

    h <- hist(hplot$V1, plot=FALSE)

    h < -嘘(hplot $ V1,情节= FALSE)

    h$counts <- h$counts/1e6

    h数< - h数/ 1美元e6

    plot(h)

    情节(h)

  • Plotting the histogram as a density estimate:

    将直方图绘制成密度估计:

    hist(hplot$V1, freq=FALSE)

    嘘(hplot $ V1,频率= FALSE)

#3

1  

Another option would be to use plot(density(hplot$V1), log="y").

另一个选项是使用plot(密度(hplot$V1)， log="y")。

It's not a histogram, but it shows just about the same information, and it avoids the illogical part where a bin with zero counts is not well-defined in log-space.

它不是直方图，但它只显示了相同的信息，并且它避免了逻辑部分，在日志空间中，没有定义为零的bin。

Of course, this is only relevant when your data is continuous and not when it's really categorical or ordinal.

当然，只有当你的数据是连续的，而不是真正的分类或序数时，这才有意义。

#4

1  

You can log your y-values for the plot and add a custom log y-axis afterwards.

您可以为该绘图记录y值，然后添加自定义的日志y轴。

Here is an example for a table object of random normal distribution numbers:

下面是一个关于随机正态分布的表对象的例子:

# data
count = table(round(rnorm(10000)*2))
# plot
plot(log(count) ,type="h",  yaxt="n", xlab="position", ylab="log(count)")
# axis labels
yAxis = c(0,1,10,100,1000)
# draw axis labels
axis(2, at=log(yAxis),labels=yAxis, las=2)

用R[复制]做直方图中的y轴对数

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