I have a dataset from an accelerometer, which consists in a time variable and measurements in the 3 axis. I have 50 measurements per second, and my ultimate goal is to subset those measurements, i.e. to select randomly 30 of them (per second).
The dataset looks like this:
> head(ACCL_20220211_190222_)
# A tibble: 6 × 4
time x y z
<dttm> <dbl> <dbl> <dbl>
1 2022-02-11 19:03:31 14336 6720 -6080
2 2022-02-11 19:03:31 14272 6656 -6144
3 2022-02-11 19:03:31 14208 6720 -5952
4 2022-02-11 19:03:31 14272 6656 -5952
5 2022-02-11 19:03:31 14272 6592 -6080
6 2022-02-11 19:03:31 14208 6528 -6016
I first tried this:
ACCL_20220211_190222_s <- ACCL_20220211_190222_ %>% group_by(time) %>% dplyr::slice_sample(n=30)
Which didn't work out, and I think it is because the time values are not really unique, even though it looks like they are. Because when I use the unique() function, I get the 50 values per "unique" time, and not just one (also, see the output of dput() below).
So I thought I could create a column grouping the time variable, but it is not working out, and I think it is because of the same issue.
This code below creates a column grouping_variable with a single value, 1.
ACCL_20220211_190222_s <- ACCL_20220211_190222_ %>%
group_by(time) %>%
mutate(grouping_var = match(time, sort(unique(time))))
So, how can I group this column? Or, if there's a direct way of subsampling the values, from 50 per second to 30, even better!
Here's an example of the dataset (it has dates/times for 10 seconds, so it should create 10 groups):
> dput(ACCL_20220211_190222_)
structure(list(time = structure(c(1644606211.007, 1644606211.027,
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-6144, -6144, -6208, -6336, -6080, -5888, -6016, -6144, -6080,
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-6144, -6144, -6016, -6144, -6080, -6144, -6144, -6272, -6144,
-6336, -6272, -6208, -6016, -6080, -6656, -6272, -6016, -6016,
-6208, -6208, -6272, -6144, -6208, -6272, -6144, -6272, -6144,
-6144, -6336, -6208, -6144, -6208, -6080, -6464, -6464, -6144,
-6208, -6144, -6080, -6208, -6208, -6208, -6336, -6208, -6336,
-6272, -6208, -6272, -6208, -6144, -6080, -6144, -6336, -6144,
-6208, -6272, -6272, -6208, -6336, -6400, -6080, -6208, -6272,
-6208, -6080, -6144, -6144, -6144, -6208, -6272, -6336, -6144,
-6336, -6208, -6208, -6144, -6208, -6208, -6208, -6208, -6272,
-6144, -6336, -6400, -6272, -6016, -6144, -6400, -6400, -6080,
-6208, -6208, -6272, -6336, -6208, -6144, -6208, -6080, -6144,
-6272, -6144, -6080, -6080, -6272, -6336, -6144, -6464, -6080,
-6528, -5888, -6464, -6144, -6016, -6208, -6272, -6080, -6400,
-6272, -6336, -6592, -6400, -6528, -6464, -6208, -6272, -6272,
-6336, -6464, -6784, -6272, -6336, -6144, -6272, -6400, -6656,
-6336, -6464, -6272, -6208, -6144, -6272, -6272, -6400, -6400,
-6272, -6400, -6400, -6464, -6400, -6400, -6272, -6336, -6336,
-6400, -6336, -6400, -5952, -6336, -6272, -6528, -6464, -6272,
-5952, -6272, -6336, -6400, -6272, -6272, -6272, -6336, -6272,
-6208, -6400, -6400, -6208, -6208, -6336, -6208, -6208, -6144,
-6208, -6208, -6208, -6080, -6272, -6528, -6464, -6272, -6208,
-6464, -6272, -6144, -6080, -6144, -6528, -6592, -6464, -6144,
-6464, -6272, -6336, -6272, -6272, -6144, -6272, -6336, -6208,
-6336, -6336, -6464, -6400, -6336, -6144, -6336, -6400, -6080,
-6080, -6208, -6208, -6400, -6528, -6400, -6272, -6208, -6208,
-6336, -6272, -6144, -6208, -6272, -6208, -6208, -6208, -6336,
-6400, -6144, -6208, -6208, -6464, -6272, -6336, -6336, -6272,
-6016, -6272, -6400, -6272, -6464, -6080, -6208, -6400, -6208,
-6144, -6272, -6400, -6400, -6336, -6336, -6080, -6528, -6400,
-6080, -6144, -6208, -6400, -6336, -6144, -6272, -6208, -6400,
-6400, -6400, -6400, -6272, -6208, -6400, -6272, -6400, -6144,
-6144, -6272, -6336, -6336, -6528, -6400, -6400, -6528, -6144,
-6208, -6272, -6336, -6272, -6208, -6272, -6464, -6400, -6400,
-6464, -6400, -6400, -6336, -6336, -6336, -6336, -6272, -6464,
-6208, -6272, -6208, -6272, -6272, -6336, -6464, -6464, -6208,
-6208, -6400, -6336, -6464, -6336, -6144, -6464, -6464, -6208,
-6464, -6464, -6272, -6272, -6272, -6336, -6464, -6272, -6400,
-6400, -6336)), row.names = c(NA, -491L), class = c("tbl_df",
"tbl", "data.frame"))
As you assumed, the date-times are not the same, they are saved with microseconds information which can be returned with e.g.
format.Date(ACCL_20220211_190222_$time, "%H:%M:%OS3")
[1] "20:03:31.006" "20:03:31.026" "20:03:31.048" "20:03:31.068" "20:03:31.088"
[6] "20:03:31.108" "20:03:31.128" "20:03:31.150" "20:03:31.170" "20:03:31.190"
If you want to go with your approach you can group by the complete string
library(dplyr)
ACCL_20220211_190222_ %>%
group_by(grp = format.Date(time, "%F %T")) %>%
slice_sample(n=30)