在GGPlot2直方图中为X值以上的任何值创建一个bin

Using ggplot2, I want to create a histogram where anything above X is grouped into the final bin. For example, if most of my distribution was between 100 and 200, and I wanted to bin by 10, I would want anything above 200 to be binned in "200+".

使用ggplot2,我想创建一个直方图,其中X以上的任何内容都被分组到最终的bin中。例如,如果我的大多数发行版都在100到200之间,并且我希望以10分为单位,那么我希望将200以上的任何内容分成“200+”。

# create some fake data    
id <- sample(1:100000, 10000, rep=T)
visits <- sample(1:1200,10000, rep=T)

#merge to create a dataframe
df <- data.frame(cbind(id,visits))

#plot the data
hist <- ggplot(df, aes(x=visits)) + geom_histogram(binwidth=50)

How can I limit the X axis, while still representing the data I want limit?

如何限制X轴,同时仍然表示我想要限制的数据?

2 个解决方案

#1

Perhaps you're looking for the breaks argument for geom_histogram:

也许你正在寻找geom_histogram的break参数:

# create some fake data    
id <- sample(1:100000, 10000, rep=T)
visits <- sample(1:1200,10000, rep=T)

#merge to create a dataframe
df <- data.frame(cbind(id,visits))

#plot the data
require(ggplot2)
ggplot(df, aes(x=visits)) +
  geom_histogram(breaks=c(seq(0, 200, by=10), max(visits)), position = "identity") +
  coord_cartesian(xlim=c(0,210))

This would look like this (with the caveats that the fake data looks pretty bad here and the axis need to be adjusted as well to match the breaks):

这看起来像这样(警告说这里假数据看起来很糟糕,轴也需要调整以匹配断点):

Edit:

Maybe someone else can weigh in here:

也许其他人可以在这里权衡:

# create breaks and labels
brks <- c(seq(0, 200, by=10), max(visits))
lbls <- c(as.character(seq(0, 190, by=10)), "200+", "")
# true
length(brks)==length(lbls)

# hmmm
ggplot(df, aes(x=visits)) +
  geom_histogram(breaks=brks, position = "identity") +
  coord_cartesian(xlim=c(0,220)) +
  scale_x_continuous(labels=lbls)

The plot errors with:

情节错误:

Error in scale_labels.continuous(scale) : 
  Breaks and labels are different lengths

Which looks like this but that was fixed 8 months ago.

这看起来像这样但是8个月前修复了。

#2

If you want to fudge it a little to get around the issues of bin labelling then just subset your data and create the binned values in a new sacrificial data-frame:

如果你想稍微捏一下它来解决bin标签的问题,那么只需将数据子集化并在新的牺牲数据框中创建分箱值:

id <- sample(1:100000, 10000, rep=T)
visits <- sample(1:1200,10000, rep=T)

#merge to create a dataframe
df <- data.frame(cbind(id,visits))
#create sacrificical data frame
dfsac <- df
dfsac$visits[dfsac$visits > 200 ] <- 200

Then use the breaks command in scale_x_continuous to define your bin labels easily:

然后使用scale_x_continuous中的breaks命令轻松定义bin标签:

ggplot(data=dfsac, aes(dfsac$visits)) + 
  geom_histogram(breaks=c(seq(0, 200, by=10)), 
                 col="black", 
                 fill="red") +
  labs(x="Visits", y="Count")+
  scale_x_continuous(limits=c(0, 200), breaks=c(seq(0, 200, by=10)), labels=c(seq(0,190, by=10), "200+"))