Cycle Identification #2
Description
This package is awesome!!! Is there an ability to apply an identifying factor on the cycles in the data set? I didn't see anything digging through what you have.
Below illustrates what I am looking for and how I was able to rig together a solution for my application with time series data. This is a small sample of my dataset df.txt which represents about 3 days of a dataset that covers a few years.
An example of the data looks like:
| Time | Value |
|---|---|
| 2018-08-10 06:00:00 | 760.6917 |
| 2018-08-10 06:01:00 | 761.0291 |
| 2018-08-10 06:02:00 | 760.9166 |
| 2018-08-10 06:03:00 | 761.2542 |
| 2018-08-10 06:04:00 | 761.1416 |
| 2018-08-10 06:05:00 | 762.5291 |
I used the indices from the FindPeaks() function and converted those to matching timestamps from my dataset and then used row_number() to apply a quick and dirty identifying factor.
library(lubridate)
library(dplyr)
p <- FindPeaks(df$Value, R=1.05, smooth = FALSE)
cycleID <- tibble(Time = c(df$Time[1], df$Time[1] + lubridate::dminutes(p$indices[-1]))) %>%
dplyr::mutate(Cycle = row_number()) %>%
dplyr::select(Cycle, Time)
df_out <- left_join(df, cycleID, by = "Time") %>%
tidyr::fill(Cycle)
The output looks like this with the new ID variable.
| Time | Value | Cycle |
|---|---|---|
| 2018-08-10 06:00:00 | 760.6917 | 1 |
| 2018-08-10 06:01:00 | 761.0291 | 1 |
| 2018-08-10 06:02:00 | 760.9166 | 1 |
| 2018-08-10 06:03:00 | 761.2542 | 1 |
| 2018-08-10 06:04:00 | 761.1416 | 1 |
| 2018-08-10 06:05:00 | 762.5291 | 1 |