R ggplot2对数切割，在x轴上具有负值和正值，在y轴上每个仓位均值

Question

我正在寻找一种方法来利用ggplot2中更复杂的函数来绘制一个变量的平均值分布在另一个变量（具有正值和负值）的log2值的区间中。 我认为我正在使这一过程变得很复杂，并且它可能在ggplot2精致选项中进行了硬编码，但是我无法正确实现，因此在回到基础知识之前，我认为我可以尝试学习如何在此处应用这些功能。

value <- rnorm(1000,0,20)
dist = c(rep(0, 15), sample(1:490), sample(-1:-495))
data = data.frame(value=value, dist=dist)

data$log=log2(abs(data$dist)+1)
# re-lable the x-axis: 
data$Labels=2^(abs(data$log))-1

data$bins=cut(data$log, breaks=10)
# Try to recover the negative log after transformation
data$sign=ifelse(data$dist==0, 0, ifelse(data$dist>0, "+", "-"))

# find the average expression of value per each bin
data=with(data, aggregate(data$value, by = list(bins, sign), FUN =    function(x) c(mn =mean(x), n=length(x) )))
data= as.data.frame(as.list(data))
names(data)=c("bins", "sign", "mean", "length")

# I am doing this in a very contorted way to try to achieve what I would like which is something like this:

bin_num = do.call("rbind", lapply(strsplit(sapply(as.character(data$bins), function(x) substr(x, 2, nchar(x)-1)), ","), as.numeric))
data$bin_num=bin_num[,1]
data$bin_num=ifelse(data$sign==0, 0, ifelse(data$sign=="-", -data$bin_num, data$bin_num))
data = data[order(data$bin_num),]

data <- transform(data, x2 = factor(paste(sign, bins)))
data <- transform(data, x2 = reorder(x2, rank(bin_num)))

# Line plot to show the distribution of the means across the bins of log2 of x:
ggplot(data, aes(y = mean, x = bin_num, group=1)) +  geom_point() + geom_line()

＃然后，我尝试通过添加标签在此处重新标记对数转换，但是当然它不起作用：

ggplot(data, aes(y = mean, x = bin_num, group=1)) +  geom_point() + geom_line() + scale_x_discrete(labels=data$dist, breaks=data$bin_num)

我看到ggplot2具有直接计算均值的功能，因此我什至不需要以前的命令。 我试过了：

ggplot(data, aes(x = bins, y = mean)) + stat_summary(fun.y = "mean") +     geom_line() + scale_x_continuous(breaks = labels)

但是当然这是行不通的...我还看到了ggplo2具有自动对数标记的功能，而不是我在这里使用的功能，但是当要记录负值时，我看不到如何执行此操作。 有一个从另外一个问题一个很好的功能， 在这里 ，其将该两个值，但我没有看到它在这个阶段是有用的。 非常感谢您提出建议，非常感谢！

Answer 1

答案的第一个版本，使用data.table实现更快的速度和更好的可读性：

该代码使用更短和更快的代码来再现问题

library(data.table)

# function that returns the lower bound of a cut
lower.bound <- function(x, n) {
  c <- cut(x, n)
  tmp <- substr(x = c, start = 2, stop = regexpr(",", c) - 1)
  return(as.numeric(tmp))
}

nbin <- 10
set.seed(123)
dat <- data.table(value = rnorm(1000,0, 20),
                  dist = c(rep(0, 15), sample(1:490), sample(-1:-495)))

dat[, log := log2(abs(dist) + 1)]
dat[, labels := 2^(abs(log))]
dat[, sign := ifelse(dist == 0, 
                     0,
                     ifelse(dist > 0, "+", "-"))]

dat[, bin := ifelse(sign == 0, 
                    0,
                    ifelse(sign == "+", 
                           lower.bound(log, nbin),
                           -lower.bound(log, nbin)))]

sumdat <- dat[, .(mvalue = mean(value),
                  nvalue = .N,
                  ylab = mean(dist)), 
                 by = .(bin, sign)][order(bin)]

ggplot(sumdat, aes(x = ylab, y = mvalue)) + geom_line()