(这跟ggplot2 loess Q之后我得到了一个很好的答案) - 导致这个情节:
我的R知识非常有限(对不起!)
我使用表data1中的数据绘制散点图。
data1<-NaRV.omit(data[,c(2,3,7,10)]) #(2=start, 3=end, 7=value, 10=type)
ylabs='E / A - ratio'
p1<-ggplot(data1, aes(x=start, y=value)) +
ylim(0,5) +
geom_point(shape=points, col=pointcol1, na.rm=T) +
geom_hline(aes(yintercept=1, col=linecol)) +
geom_smooth(method="loess", span=spanv, fullrange=F, se=T, na.rm=T) +
#
xlab(xlabs) +
ylab(ylabs)
某些地区没有数据(包括中间的一个大区域,但也包括较小的离散区域),我想在y = 0处绘制彩色区段来说明这一事实
我将两种数据类型合并到一个表中,标签列为#10 ='type'(散射数据的内容='cnv'和no-data ='nregion')。 nregions在值列中有0。
如何只为散点图获取“cnv”数据,并且只绘制“nregion”数据来绘制线段;两个都在同一个情节?
我找到了geom_segment:
+ geom_segment(aes(x=data1$start, y=0, xend=data1$end, yend=0))
但是我找不到每种ggplot子图的子集方法。
由于
####跟进@gauden解决方案你好@gauden 我尝试了你的方法,它部分工作。 我的问题是我不能像你使用的那样很好地划分我的数据] -1; 0]因为我的nregions是分散的(由图中的蓝点和线表示),并且每个新图都不同,如下图所示:
因此,黄土像以前一样经过大的nregion。如何防止nregions中的黄土?
#############################
## plot settings (edit below)
spanv<-0.1
pointcol1="#E69F00"
pointcol2="#56B4E9"
pointcol3="#009E73"
points=20
onecol="green"
colnreg="blue"
xlabs=paste(onechr, " position", " (loess-span=", spanv, ")", sep="")
##### end edit ##############
########################################################
## using the center coordinate of each segment and points
## prepare plot #1
# plot E / A - ratio
## draw loess average for cnv
## draw line for nregion
ylabs='E / A - ratio'
p1<-ggplot(chrdata, aes(x=start+1000, y=E.R, group=type, label=type)) +
ylim(0,5) +
geom_hline(aes(yintercept=1, col=onecol)) +
geom_point(data = chrdata[chrdata$type != 'nregion',], shape=points, col=pointcol2) +
geom_smooth(data = chrdata[chrdata$type != 'nregion',], method="loess", span=spanv) +
geom_point(data = chrdata[chrdata$type == 'nregion',], col=colnreg) +
geom_segment(data = chrdata[chrdata$type == 'nregion',], aes(x=start, y=E.R, xend=end, yend=E.R), colour=colnreg, linetype=1, size=1) +
xlab(xlabs) +
ylab(ylabs)
答案 0 :(得分:7)
编辑:完整修订以允许澄清请求
这是我的目标情节:
以下是产生它的代码:
library("ggplot2")
# CREATE DATA FRAME
# This is the sort of data that I understand you to have
start <- rnorm(200)
value <- rnorm(200)
df <- data.frame( cbind(start, value) )
df[ df$start > -0.6 & df$start <= 0, "value"] <- 0
df[ df$start > -1.6 & df$start <= -1.3, "value"] <- 0
df[ df$start > 0.9 & df$start <= 1.2, "value"] <- 0
df$type <- rep('cnv', 200)
df[ df$value == 0, "type"] <- 'nregion'
df[ df$value != 0, "type"] <- 'cnv'
# SORT the data frame by value so that the 'cnv' and
# 'nregion' chunks become contiguous
df <- df[order(start),]
# See note below.
r <- rle(df$type)
df$label <- rep(seq(from=0, length=length(r$lengths)), times=r$lengths)
# set up plot with colour aesthetic to distinguish the three regions
# playing around with colour and group produces different effects
p <- ggplot(df, aes(x = start,
y= value,
colour=type,
group = label)
)
p <- p + theme_bw()
# draw points outside the 'nregion'
p <- p + geom_point( data = df[df$type != 'nregion',] )
# draw smoothed lines outside the 'nregion'
p <- p + geom_smooth( data = df[df$type != 'nregion',] )
# plot zero points inside the 'nregion'
p <- p + geom_smooth( data = df[df$type == 'nregion',], size = 2 )
p
的回答中进一步解释了rle
的使用