Using ddply to aggregate over irregular time periods in longitudinal data - r

Question

I'm looking for help adapting two existing scripts.
I am working with a longitudinal dataset, and aggregating a key variable over time periods. I have a variable for both weeks and months. I'm able to aggregate over both weeks and months - but my goal is to aggregate over weeks for the first six weeks, and then move over to aggregating by months after 6 weeks+.
Aggregating by weeks and months is easy enough...
df.summary_week <- ddply(df, .(weeks), summarise,
var.mean = mean(var,na.rm=T))
Which yields something like:
weeks var.mean
1 3.99
2 5.44
3 6.7
4 8.100
5 2.765
6 2.765
7 3.765
8 4.765
9 1.765
10 4.765
11 1.765
And then aggregating by month would yield something similar:
df.summary_months <- ddply(df, .(months), summarise,
var.mean = mean(var,na.rm=T))
months var.mean
1 5.00
2 3.001
3 4.7
4 7.100
My initial idea was to simply subset the two datasets with cut points and then bind them together, but I don't know how to do that when the 1-month aggregation starts at 6 weeks rather than 8.
Thoughts, R wizards?

Answer 1

Basic example data.
dat <- data.frame(var=1:24,weeks=1:24,months=rep(1:6,each=4))
Means for first 6 grps should be just 1:6, then means will be values
for subsequent 4 week periods. E.g. (mean(7:10) = 8.5 etc).
Make a suitable group identifier going from weeks to months:
dat$grp <- findInterval(dat$weeks,seq(7,max(dat$weeks),4)) + 6
dat$grp <- ifelse(dat$grp==6,dat$weeks,dat$grp)
#[1] 1 2 3 4 5 6 7 7 7 7 8 8 8 8 9 9 9 9 10 10 10 10 11 11
Group the data:
ddply(dat, .(grp), summarise, var.mean = mean(var,na.rm=T))
grp var.mean
1 1 1.0
2 2 2.0
3 3 3.0
4 4 4.0
5 5 5.0
6 6 6.0
7 7 8.5
8 8 12.5
9 9 16.5
10 10 20.5
11 11 23.5

Answer 2

How about just creating a new grouping column?
set.seed(1618)
dat <- data.frame(week = sample(1:26, 200, replace = TRUE),
value = rpois(200, 2))
dat <- within(dat, {
idx <- cut(week, c(0, 6, seq(10, max(week), by = 4)))
})
# head(dat)
# week value idx
# 1 6 1 (0,6]
# 2 16 2 (14,18]
# 3 9 1 (6,10]
# 4 13 2 (10,14]
# 5 8 2 (6,10]
# 6 16 2 (14,18]
library(plyr)
ddply(dat, .(idx), summarise,
mean = mean(value, na.rm = TRUE))
# idx mean
# 1 (0,6] 1.870968
# 2 (6,10] 2.259259
# 3 (10,14] 2.171429
# 4 (14,18] 1.931034
# 5 (18,22] 1.560000
# 6 (22,26] 1.954545
# checking a couple values
mean(dat[dat$week %in% 1:6, 'value'])
# [1] 1.870968
mean(dat[dat$week %in% 7:10, 'value'])
# [1] 2.259259
mean(dat[dat$week %in% 23:26, 'value'])
# [1] 1.954545