Using dplyr to average time series groups with individuals of different lengths

user_11714836 0
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Consider dat created here:

set.seed(123)
ID = factor(letters[seq(6)])
time = c(100, 102, 120, 105, 109, 130)
dat <- data.frame(ID = rep(ID,time), Time = sequence(time))
dat$group <- rep(c("GroupA","GroupB"), c(322,344))

dat$values <- sample(100, nrow(dat), TRUE)

We have time series data for 6 individuals (6 IDs), which belong to 2 groups (GroupA and GroupB). We want to make a line plot that shows the "average" time series of both groups (so there will be two lines). Since the individuals all have different lengths, we need to do dat%>%group_by(group), and shave off values after the shortest ID within both groups. In other words, ID == a is the shortest in group 1, so the "average" line for GroupA will only be 100 values long on the x-axis; likewise ID == d is the shortest for GroupB so the "average" time series of GroupB will be 105 values long on the x axis (time). How can we do this (preferably through a dplyr pipe) and send the data to ggplot()?

line-plot dplyr time-series ggplot2 r
2 Answers

You could try:

library(ggplot2)
library(dplyr)

dat %>% 
  group_by(ID) %>%
  mutate(maxtime = max(Time)) %>%
  group_by(group) %>%
  mutate(maxtime = min(maxtime)) %>%
  group_by(group, Time) %>%
  summarize(values = mean(values)) %>%
  ggplot(aes(Time, values, colour = group)) + geom_line()

user_125003150

We could do

library(dplyr)
dat %>% 
   add_count(group, ID) %>%
   group_by(group) %>%
   mutate(n = min(n)) %>%
   group_by(group, ID) %>% 
   summarise(values = mean(values[seq_len(first(n))]))
user_37322710
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