如何绘制多个个体的平均时间序列

Question

I have time series data where measurements of 7 variables ( Var1:Var7 ) were taken on 15 individuals (denoted by a unique ID ). These individuals were sampled from 3 different Location s. Note that the number of observations is different for each individual. I believe the individuals within each Location will be more similar to each other than individuals in other Location s, both in value and trend. For each Variable within each Location , I want to plot the average time series (to get an idea of what the group looks like as a whole) up to the point where Time is the same for each individual (so the length of the x-axis will only be as long as the shortest individual). How can I do this and add error bars for each Time point to see how much variation exists between individuals? Here is some sample data:

set.seed(123)
ID = factor(letters[seq(15)])
Time = c(1000,1200,1234,980,1300,1020,1180,1908,1303,
        1045,1373,1111,1097,1167,1423)
df <- data.frame(ID = rep(ID, Time), Time = sequence(Time))
df$Location = rep(c("NY","WA","MA"), c(5714,7829,4798))
df[paste0('Var', c(1:7))] <- rnorm(sum(Time))

Answer 1

The values of all your variables are the same, so I did the following to make it more random:

for(i in 1:7) df[paste0('Var', i)] <- rnorm(sum(Time))

Then the following code gives a time-series plot for each of the 7 variables averaged over the three locations.

df %>%
  pivot_longer(cols = Var1:Var7, names_to="Variable") %>%
  group_by(Location, Variable, Time) %>%
  summarise(mval=mean(value)) %>%
  ggplot(aes(y=mval, x=Time, color=Variable)) +
  geom_line() +
  facet_grid(~Location) # , scales="free" # ?

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I'm not sure if this is what you had in mind though.