R中ggplot的条件stat_summary

Question

如果数据大于某个值，我想在图表中写一些条件统计数据。

在Jack Ryan（Cut data and access groups to draw percentile lines）的帮助下，我可以创建以下脚本，将数据分组为小时并绘制结果：

# Read example data
A <- read.csv(url('http://people.ee.ethz.ch/~hoferr/download/data-20130812.csv'))

# Libraries
library(doBy)
library(ggplot2)
library(plyr)
library(reshape2)
library(MASS)
library(scales)

# Sample size function
give.n <- function(x){
       return(c(y = min(x) - 0.2, label = length(x)))
}

# Calculate gaps
gaps <- rep(NA, length(A$Timestamp))
times <- A$Timestamp
loss <- A$pingLoss
gap.start <- 1
gap.end <- 1
for(i in 2:length(A$Timestamp))
{ #For all rows
    if(is.na(A$pingRTT.ms.[i]))
    { #Currently no connection
        if(!is.na(A$pingRTT.ms.[i-1]))
        { #Connection lost now
            gap.start <- i
        }
        if(!is.na(A$pingRTT.ms.[i+1]))
        { # Connection restores next time
            gap.end <- i+1
            gaps[gap.start] <- as.numeric(A$Timestamp[gap.end]-A$Timestamp[gap.start], units="secs")
            loss[gap.start] <- gap.end - gap.start
        } 
    }       
}              
H <- data.frame(times, gaps, loss)
H <- H[complete.cases(H),]
C <- H      
C$dates <- strptime(C$times, "%Y-%m-%d %H:%M:%S")
C$h1 <- C$dates$hour

# Calculate percentiles
cuts <- c(1, .75, .5, .25, 0)
c <- ddply(C, .(h1), function (x) { summarise(x, y = quantile(x$gaps, cuts)) } )
c$cuts <- cuts
c <- dcast(c, h1 ~ cuts, value.var = "y")
c.melt <- melt(c, id.vars = "h1")

p <- ggplot(c.h1.melt, aes(x = h1, y = value, color = variable)) +
geom_point(size = 4) +
stat_summary(fun.data = max.n, geom = "text", fun.y = max, colour = "red", angle = 90, size=4) +
scale_colour_brewer(palette="RdYlBu", name="Percentile", guide = guide_legend(reverse=TRUE)) +
scale_x_continuous(breaks=0:23, limits = c(0,23)) +
annotation_logticks(sides = "lr") +
theme_bw() +
scale_y_log10(breaks=c(1e0,1e1,1e2,1e3,1e4), labels = trans_format("log10", math_format(10^.x)), limits=c(1e0,1e4)) +
xlab("Hour of day") + ylab("Ping gaps [s]")
p

p <- ggplot(c.m1.melt, aes(x = m1/60, y = value, color = variable)) +
geom_point(size = 1) +
stat_summary(fun.data = give.n, geom = "text", fun.y = median, angle = 90, size=4) +
stat_summary(fun.data = max.n, geom = "text", fun.y = max, colour = "red", angle = 90, size=4) +
scale_colour_brewer(palette="RdYlBu", name="Percentile", guide = guide_legend(reverse=TRUE)) +
scale_x_continuous(breaks=0:23, limits = c(0,24)) +
annotation_logticks(sides = "lr") +
theme_bw() +
scale_y_log10(breaks=c(1e0,1e1,1e2,1e3,1e4), labels = trans_format("log10", math_format(10^.x)), limits=c(1e0,1e4)) +
xlab("Time of day") + ylab("Ping gaps [s]")
p

这会创建一个每小时分组的间隙图，其中最长间隙的长度写在数据点的旁边：

下面是精细分组的情节。这个数字是不可读的，为什么我想添加条件统计数据，如果差距超过5分钟，或者仅仅是十个最长的差距或类似的东西。

我试图将stat函数更改为

max.n.filt <- function(x){
    filter = 300
    if ( x > filter ) {
      return(c(y = max(x) + 0.4, label = round(max(10^x),2)))
    } else {
        return(c(y=x, label = ""))
    }
}

并将其用于精细分组的绘图。但我得到了这个错误：

Error in list_to_dataframe(res, attr(.data, "split_labels")) : 
  Results do not have equal lengths
In addition: There were 50 or more warnings (use warnings() to see the first 50)
Error in if (nrow(layer_data) == 0) return() : argument is of length zero
Calls: print ... print.ggplot -> ggplot_gtable -> Map -> mapply -> <Anonymous>
In addition: Warning message:
Removed 6 rows containing missing values (geom_point).

另外，在小时图中，我想在间隙长度旁边写下每小时的样本数。我想我可以在c数据框中添加一个新列，但遗憾的是我无法找到这样做的方法。

非常感谢任何帮助。

Answer 1

参见？stat_summary。

  

fun.data：完整的摘要功能。应将数据帧作为输入   并返回数据框作为输出

您的函数max.n.filt使用if()语句尝试评估条件x > filter。但是当length(x) > 1时，if()语句仅评估x的第一个值的条件。当在数据框上使用时，这将返回从原始输入x拼凑在一起的列表以及if()语句返回的任何标签。

> max.n.filt(data.frame(x=c(10,15,400)))
$y.x
[1]  10  15 400

$label
[1] ""

尝试使用ifelse()代替的功能：

max.n.filt2 <- function(x){
    filter = 300                  # whatever threshold
    y = ifelse( x > filter, max(x) + 1, x[,1] )
    label = ifelse( x > filter, round(max(x),2), NA )
    return(data.frame(y=y[,1], label=label[,1]))
}

> max.n.filt2(data.frame(x=c(10,15,400)))
    y label
1  10    NA
2  15    NA
3 401   400

或者，您可能会发现使用geom_text()更容易。我无法重现您的示例，但这是一个模拟数据集：

set.seed(101)
sim_data <- expand.grid(m1=1:1440, variable=factor(c(0,0.25,0.5,0.75,1)))
sim_data$sample_size <- sapply(1:1440, function(.) sample(1:25, 1, replace=T))
sim_data$value = t(sapply(1:1440, function(.) quantile(rgamma(sim_data$sample_size, 0.9, 0.5),c(0,0.25,0.5,0.75,1))))[1:(1440*5)]

只需使用subset中的geom_text()参数来选择您想要标记的那些点：

ggplot(sim_data, aes(x = m1/60, y = value, color = variable)) +
geom_point(size = 4) + geom_text(aes(label=round(value)), subset = .(variable == 1 & value > 25), angle = 90, size = 4, colour = "red", hjust = -0.5)

如果您有一列样本量，可以将label与paste()合并到一起：

ggplot(sim_data, aes(x = m1/60, y = value, color = variable)) +
geom_point(size = 4) + geom_text(aes(label=paste(round(value),", N=",sample_size)), subset = .(variable == 1 & value > 25), angle = 90, size = 4, colour = "red", hjust = -0.25)

（或在您的数据中使用您想要的任何标签创建单独的列。）如果您询问 如何检索样本量，您可以修改对ddply()的调用像这样：

...
c2 <- ddply(C, .(h1), function (x) { cbind(summarise(x, y = quantile(x$gaps, cuts)), n=nrow(x)) } )
c2$cuts <- cuts
c2 <- dcast(c2, h1 + n ~ cuts, value.var = "y")
c2.h1.melt <- melt(c2, id.vars = c("h1","n"))
...