best method for downsampling? #2
Description
if you have functions measured over a rich grid, you might want to downsample (e.g. go from minute-level wearable device data to 5 minute or one-hour increments). if that's your goal, you might prefer to average over bins -- but i don't think there's a good way to do that right now, is there?
tf_evaluate lets you evaluate on a new domain, but uses interpolation rather than averaging. and tf_integrate could work, sort of, in that you get average value between lower and upper -- but it produces a scalar, and you'd have to do some kind of loop.
for what it's worth, my current work around is to unnest, aggregate, then nest and re-merge. something like:
hour_data =
activity_df %>%
select(id, activity) %>%
tf_unnest(activity) %>%
mutate(hour = floor((activity_arg -1) / 60)) %>%
group_by(id, hour) %>%
summarize(act_hour = mean(activity_value)) %>%
tf_nest(.id = id, .arg = hour)