Plot In R with Multiple Lines Based On A Particular Variable? - r

Question

I have this accelerometer dataset and, let's say that I have some n number of observations for each subject (30 subjects total) for body-acceleration x time.
I want to make a plot so that it plots these body acceleration x time points for each subject in a different color on the y axis and the x axis is just an index. I tried this:
ggplot(data = filtered_data_walk, aes(x = seq_along(filtered_data_walk$'body-acceleration-mean-y-time'), y = filtered_data_walk$'body-acceleration-mean-y-time')) +
geom_line(aes(color = filtered_data_walk$subject))
But, the problem is that it doesn't superimpose the 30 lines, instead, they run along side each other. In other words, I end up with n1 + n2 + n3 + ... + n30 x index points, instead of max{n1, n2, ..., n30}. This is my first time posting, so I hope this makes sense (I know my formatting is bad).
One solution I thought of was to create a new variable which gives a value of 1 to n for all the observations of each subject. So, for example, if I had 6 observations for subject1, 4 observations for subject2, and 9 observations for subject3, this new variable would be sequenced like:
1 2 3 4 5 6 1 2 3 4 1 2 3 4 5 6 7 8 9
Is there an easy way to do this? Please help, ty.

Answer 1

Assuming your data is formatted as a data.frame or matrix, for a toy dataset like
x <- data.frame(replicate(5, rnorm(10)))
x
# X1 X2 X3 X4 X5
# 1 -1.36452272 -1.46446475 2.0444381 0.001585876 -1.1085990
# 2 -1.41303046 -0.14690269 1.6179084 -0.310162018 -1.5528733
# 3 -0.15319554 -0.18779791 -0.3005058 0.351619212 1.6282955
# 4 -0.38712167 -0.14867239 -1.0776359 0.106694311 -0.7065382
# 5 -0.50711166 -0.95992916 1.3522922 1.437085757 -0.7921355
# 6 -0.82377208 0.50423328 -0.5366513 -1.315263679 1.0604499
# 7 -0.01462037 -1.15213287 0.9910678 0.372623508 1.9002438
# 8 1.49721113 -0.84914197 0.2422053 0.337141898 1.2405208
# 9 1.95914245 -1.43041783 0.2190829 -1.797396822 0.4970690
# 10 -1.75726827 -0.04123615 -0.1660454 -1.071688768 -0.3331887
...you might be able to get there with something like
plot(x[,1], type='l', xlim=c(1, nrow(x)), ylim=c(min(x), max(x)))
for(i in 2:ncol(x)) lines(x[,i], col=i)
You could play with formatting some more, of course, do things with lty= and lwd= and maybe a color ramp of your own choosing, etc.
If your data is in the format below...
x <- data.frame(id=c("A","A","A","B","B","B","B","C","C"), acc=rnorm(9))
x
# id acc
# 1 A 0.1796964
# 2 A 0.8770237
# 3 A -2.4413527
# 4 B 0.9379746
# 5 B -0.3416141
# 6 B -0.2921062
# 7 B 0.1440221
# 8 C -0.3248310
# 9 C -0.1058267
...you could get there with
maxn <- max(with(x, tapply(acc, id, length)))
ids <- sort(unique(x$id))
plot(x$acc[x$id==ids[1]], type='l', xlim=c(1,maxn), ylim=c(min(x$acc),max(x$acc)))
for(i in 2:length(ids)) lines(x$acc[x$id==ids[i]], col=i)
Hope this helps, and that I interpreted your problem right--

Answer 2

That's pretty quick to do if you are OK with using dplyr. group_by to enforce a separate counter for each subject, mutate to add the actual counter, and your ggplot should work. Example with iris dataset:
group_by(iris, Species) %>%
mutate(index = seq_along(Petal.Length)) %>%
ggplot() + geom_line(aes(x=index, y=Petal.Length, color=Species))