R，ggplot：更改系列中的线型

我正在使用ggplot geom_smooth来绘制上一年度相对于本年度的客户组营业额数据（基于日历周）。由于上周未完成，因此我想在上周使用虚线。但是，我不知道如何做到这一点。我可以更改整个图或整个系列的线型，但是不能在系列中更改线型（取决于x的值）：

为简单起见，我们只使用以下示例：

set.seed(42)
frame <- data.frame(series = rep(c('a','b'),50),x = 1:100, y = runif(100))

ggplot(frame,aes(x = x,y = y, group = series, color=series)) + 
geom_smooth(size=1.5, se=FALSE)

我将如何更改它以得到x> = 75的虚线？

目标将是这样的：

非常感谢您的帮助！

编辑，2016-03-05

Of course I fail when trying to use this method on the original plot. The Problem lies with the ribbon, which is calculated using stat_summary and a predefined function. I tried to use use stat_summary on the original data (mdf), and geom_line on the smooth_data. Even when I comment out everything else, I still get "Error: Continuous value supplied to discrete scale". I believe the problem comes from the fact that the original x value (Kalenderwoche) was discrete, whereas the new, smoothed x is continuous. Do I have to somehow transform one into the other? What else could I do?

Here is what I tried (condensed to the essential lines):

quartiles <- function(x) {  
  x <- na.omit(x) # remove NULL
  median <- median(x)
  q1 <- quantile(x,0.25)
  q3 <- quantile(x,0.75)
  data.frame(y = median, ymin = median, ymax = q3)
}

g <- ggplot(mdf, aes(x=Kalenderwoche, y=value, group=variable, colour=variable,fill=variable))+
geom_smooth(size=1.5, method="auto", se=FALSE)

# Take out the data for smooth line
smooth_data <- ggplot_build(g)$data[[1]]

ggplot(mdf, aes(x=Kalenderwoche, y=value, group=variable, colour=variable,fill=variable))+
  stat_summary(fun.data = quartiles,geom="ribbon", colour="NA", alpha=0.25)+
  geom_line(data=smooth_data, aes(x=x, y=y, group=group, colour=group, fill=group))  

mdf looks like this:

str(mdf)
'data.frame':   280086 obs. of  5 variables:
 $ konto_id     : int  1 1 1 1 1 1 1 1 1 1 ...
 $ Kalenderwoche: Factor w/ 14 levels "2015-48","2015-49",..: 4 12 1 3 7 13 10 6 5 9 ...
 $ variable     : Factor w/ 2 levels "Umsatz","Umsatz Vorjahr": 1 1 1 1 1 1 1 1 1 1 ...
 $ value        : num  0 428.3 97.8 76 793.1 ...

There are many accounts (konto_id), and for each account and calendar week (Kalenderwoche), there is a current turnover value (Umsatz) and a turnover value from last year (Umsatz Vorjahr). I can provide a smaller version of the data.frame and the entire code, if required.

Thx very much for any help!

P.S. I am a total novice in R, so my code probably looks rather stupid to pros, sorry for that :(

Edit, 2016-03-06

I have uploaded a subset of the data (mdf): mdf

The full code of the original graph is the following (looking somewhat weird with so little data, but that's not the point ;)

library(dtw)
library(reshape2)
library(ggplot2)
library(RODBC)
library(Cairo)

# custom breaks for X axis
breaks.custom <- unique(mdf$Kalenderwoche)[c(TRUE,rep(FALSE,0))] 

# function called by stat_summary
quartiles <- function(x) {  
  x <- na.omit(x)
  median <- median(x)
  q1 <- quantile(x,0.25)
  q3 <- quantile(x,0.75)
  data.frame(y = median, ymin = median, ymax = q3)
}

# Positions for guidelines and labels
horizontal.center <- (length(unique(mdf$Kalenderwoche))+1)/2
kw.horizontal.center <- as.vector(sort(unique(mdf$Kalenderwoche))[c(horizontal.center-0.5,horizontal.center+0.5)])
vpos.P75.label <- max(quantile(mdf$value[mdf$Kalenderwoche==kw.horizontal.center[1]],0.75)
                      ,quantile(mdf$value[mdf$Kalenderwoche==kw.horizontal.center[2]],0.75))+10
# use the higher P75 value of the two weeks around the center
vpos.mean.label <- min(mean(mdf$value[mdf$Kalenderwoche==kw.horizontal.center[1]])
                       ,mean(mdf$value[mdf$Kalenderwoche==kw.horizontal.center[2]]))-10
vpos.median.label <- min(median(mdf$value[mdf$Kalenderwoche==kw.horizontal.center[1]])
                         ,median(mdf$value[mdf$Kalenderwoche==kw.horizontal.center[2]]))-10

hpos.vline <- which(as.vector(sort(unique(mdf$Kalenderwoche))=="2016-03"))

# custom colour palette (2 colors)
cbPaletteLine <- c("#DA2626", "#2626DA")
cbPaletteFill <- c("#F0A8A8", "#7C7CE9")


# ggplot
ggplot(mdf, aes(x=Kalenderwoche, y=value, group=variable, colour=variable,fill=variable))+
  geom_smooth(size=1.5, method="auto", se=FALSE)+ 
  # SE=FALSE to suppress drawing of the SE of the fit.SE of the data shall be used instead:
  stat_summary(fun.data = quartiles,geom="ribbon", colour="NA", alpha=0.25)+
  scale_x_discrete(breaks=breaks.custom)+
  scale_colour_manual(values=cbPaletteLine)+
  scale_fill_manual(values=cbPaletteFill)+
  #coord_cartesian(ylim = c(0, 250)) +
  theme(legend.title = element_blank(), title = element_text(face="bold", size=12))+
  #scale_color_brewer(palette="Dark2")+
  labs(title = "Tranche 1", x =  "Kalenderwoche", y = "Konto-Umsatz [CHF]")+
  geom_vline(xintercept = hpos.vline, linetype=2)+
  annotate("text", x=horizontal.center, y=vpos.median.label, label = "Median", size=4)+
  annotate("text", x=horizontal.center, y=vpos.mean.label, label= "Mean", size=4)+  
  annotate("text", x=horizontal.center, y=vpos.P75.label, label = "P75%", size=4)+
  theme(axis.text.x=element_text(angle = 90, hjust = 0.5, vjust = 0.5))

Edit, 2016-03-06

The final plot now looks like this (thx, Jason!!)