In this post, I’ll continue my discussion of working with regularly sampled interval data using R. (See my previous post for some insight regarding minute data.) The discussion here is focused more so on function design.
Daily Data When I’ve worked with daily data, I’ve found that the .csv files tend to be much larger than those for data sampled on a minute basis (as a consequence of each file holding data for sub-daily intervals).
In my job, I often work with data sampled at regular intervals. Samples may range from 5-minute intervals to daily intervals, depending on the specific task. While working with this kind of data is straightforward when its in a database (and I can use SQL), I have been in a couple of situations where the data is spread across .csv files. In these cases, I lean on R to scrape and compile the data.