jitter geom_line() - r

Question

Is there a way to jitter the lines in geom_line()? I know it kinda defies the purpose of this plot, but if you have a plot with few lines and would like them all to show it could be handy. Maybe some other solution to this visibility problem.
Please see below for code,
A <- c(1,2,3,5,1)
B <- c(3,4,1,2,3)
id <- 1:5
df <- data.frame(id, A, B)
# install.packages(reshape2)
require(reshape2) # for melt
dfm <- melt(df, id=c("id"))
# install.packages(ggplot2)
require(ggplot2)
p1 <- ggplot(data = dfm, aes(x = variable, y = value, group = id,
color= as.factor(id))) + geom_line() + labs(x = "id # 1 is hardly
visible as it is covered by id # 5") + scale_colour_manual(values =
c('red','blue', 'green', 'yellow', 'black'))
p2 <- ggplot(subset(dfm, id != 5), aes(x = variable, y = value,
group = id, color= as.factor(id))) + geom_line() + labs(x = "id # 
5 removed, id # 1 is visible") + scale_colour_manual(values =
c('red','blue', 'green', 'yellow', 'black'))
# install.packages(RODBC)
require(gridExtra)
grid.arrange(p1, p2)

Answer 1

You can try
geom_line(position=position_jitter(w=0.02, h=0))
and see if that works well.

Answer 2

If you just want to prevent two lines from overlapping exactly, there is now a better way: position_dodge(), which "adjusts position by dodging overlaps to the side". This is nicer than adding jitter to any line, even when it's not needed.
Avoid ggplot2 lines overlapping exactly using position_dodge()
Code example:
df<-data.frame(x=1:10,y=1:10,z=1:10);
df.m <- melt(df, id.vars = "x");
ggplot(df.m, aes(x=x,y=value,group=variable,colour=variable))
+ geom_line(position=position_dodge(width=0.2));
Thanks to position_dodge(), we can now see that there are two lines in the plot, which just happen to co-incide exactly:

Answer 3

I tend to use different linestyles, so that, say, a solid blue line "peeks through" a dashed red line on top of it.
Then again, it does depend on what you want to impart to the reader. Keep in mind first and foremost that data should be points and theory lines unless this makes things cluttered. Unless the y and x values are identical, it'll be easier to see the points. (or you could apply the existing jitter function to the x-values)
Next, if you just want to show which runs are in the "bundle" and which are outliers, overlap doesn't matter because it's very unlikely that two outliers will be near-equal.
If you want to show a bunch of near-equal runs, you may prefer (which is to say, your readers will understand better) to plot the deltas against a mean rather than the actual values.

Answer 4

I would like to suggest a solution to a different problem than described, in which the Y axis is a factor, so position_dodge does nothing.
code:
library(tidyverse)
time_raw <- tibble(year=1900:1909,
person_A=c(rep("Rome",2),rep("Jerusalem",8)),
person_B=c(rep("Jerusalem",5),rep("Rome",5)))
achievements <- tribble(~year,~who,~what,
1900,"person_A","born",
1900,"person_B","born",
1909,"person_A","died",
1909,"person_B","died",
1905,"person_A","super star",
1905,"person_B","super star")
SCALE=0.5
jitter_locations <- time_raw %>%
pivot_longer(-year,names_to="who",values_to="place") %>%
distinct(place)%>%
filter(!is.na(place)) %>%
mutate(y_place=seq_along(place))
jitter_lines <- time_raw %>%
pivot_longer(-year,names_to="who",values_to="place") %>%
distinct(who) %>%
mutate(y_jitter=scale(seq_along(who))*0.015)
data_for_plot <- time_raw %>%
pivot_longer(-year,names_to="who",values_to="place") %>%
filter(!is.na(place)) %>%
left_join(achievements) %>%
left_join(jitter_locations) %>%
left_join(jitter_lines)
data_for_plot %>%
ggplot(aes(x=year,y=y_place+y_jitter,color=who,group=who))+
geom_line(size=2)+
geom_hline(aes(yintercept=y_place),size=50,alpha=0.1)+
geom_point(data = . %>% filter(!is.na(what)),size=5)+
geom_label(aes(label=what),size=3,nudge_y = -0.025)+
theme_bw()+
coord_cartesian(ylim = c(min(jitter_locations$y_place)-0.5*SCALE,
max(jitter_locations$y_place)+0.5*SCALE))+
scale_y_continuous(breaks =
min(jitter_locations$y_place):max(jitter_locations$y_place),
labels = jitter_locations$place)+
scale_x_continuous(breaks =
min(data_for_plot$year):max(data_for_plot$year))+
ylab("Place")