walkabillylab/activitycounts

Creates ActiLife counts based on the raw acceleration data

activitycounts's Issues

Missing versioning and change log

As discussed over e-mail, I have now removed activityCounts as a dependency of the GGIR package as there are a number of issues that worry me. If you need help addressing these then let me know.

• GitHub master branch is not consistent with counts derived from current CRAN version 0.1.2. The repo does not have a change log to describe what has changed and it is unclear whether the one unit-test covers all essential functionality. As a result, I find it hard to oversee whether the package still functions in line with the work by Jan Brond.
• I see activityCounts has undergone changes in recent years on GitHub but no effort has been made to create a GitHub release or to keep CRAN version up to date. It would be helpful if you could adopt the habit of making numbered GitHub and CRAN releases once in a while as long as the code is under development.
• ggplot2 is used as a dependency only for the purpose of creating a simple scatter plot in the vignette. This seems overkill and a simple base::plot() would be sufficient and make the package lighter.

Gain changing during loop

The gain is being changed each time through the loop; to fix this, the result from multiplication should not be reassigned to B. That is, instead of

B = B * gain
fx8up = filter(B, A, datab)

the code should be simply

fx8up = filter(B * gain, A, datab)

Error when passing data in tibble format.

Environment:

• OS: Ubuntu 20.04
• Tester: Javad Khataei
• Software version: R 4.1.2 from RStudio docker image

Description:

When the input data is passed in tibble format, it throws the following error.
Error: Must subset rows with a valid subscript vector. x Can't convert from <double> to <integer> due to loss of precision.

Steps to reproduce:

library(activityCounts)  
bad_input =  dplyr::as_tibble(activityCounts::sampleXYZ)  
activityCounts::counts(bad_input)

Expected Result:

A data frame with counts:

                  Time  x  y  z
1   2022-01-16 17:57:49  4 93 13
2   2022-01-16 17:57:50 21 50 14
3   2022-01-16 17:57:51 17 22 15
4   2022-01-16 17:57:52 22 39 23
5   2022-01-16 17:57:53 15 25 16
...

Actual Result:

Error: Must subset rows with a valid subscript vector.
x Can't convert from <double> to <integer> due to loss of precision.
Run `rlang::last_error()` to see where the error occurred.
In addition: Warning message:
In counts(data = dplyr::as_tibble(input), hertz = 100, x_axis = 2, 

acknowledgement:

This bug report is created based on Karl Brown feedback.

Expected unit of acceleration in counts function?

What is the expected unit of measurement for the data argument in function counts()?

When I express the data in mg units the mean count is around 500, while when expressed in g it is around 3, so I am guessing the expected unit of measurement is mg. Can you please confirm?

INFO: Actigraph counts

Actigraph released code to calculate counts in Python: https://github.com/actigraph/agcounts

BUG: Counts are not agnostic to labeling of axis

@martakarass, @andrew-leroux

I believe there is a potentially big issue with the calculation of counts, as it is not agnostic to the labeling of axes. I believe if you passed in a vector of X or a vector of Y, you should get the same result as if you passed in all the data. If that is incorrect, please let me know.
The issue is the update of B for each iteration: https://github.com/walkabillylab/activityCounts/blob/master/R/counts.R#L162 for the count filtering @khataei. Thus, the first axis has a value of B * gain and the second is B * gain^2 and third is B * gain^3. This is not likely what we want happening, correct?

Note in the code below, the column for z is always the same, but that is only an artifact, not a representation of correctness. If I reordered the columns, it'd be off in the same way.

library(activityCounts)
data("sampleXYZ")
head(sampleXYZ)
#>                  Time accelerometer_X accelerometer_Y accelerometer_Z
#> 1 2019-01-01 03:30:00          -0.035           0.816          -0.063
#> 2 2019-01-01 03:30:00          -0.047           0.805          -0.023
#> 3 2019-01-01 03:30:00          -0.031           0.793          -0.008
#> 4 2019-01-01 03:30:00          -0.020           0.809           0.012
#> 5 2019-01-01 03:30:00          -0.016           0.844           0.023
#> 6 2019-01-01 03:30:00          -0.004           0.879           0.012

Here we use the correct columns and use the time column

sampleXYZ_counts <- counts(
  data = sampleXYZ, hertz = 100,
  x_axis = 2,y_axis = 3,z_axis = 4, time_column = 1)
head(sampleXYZ_counts)
#>                  Time  x  y  z
#> 1 2019-01-01 03:30:00  4 89 11
#> 2 2019-01-01 03:30:01 21 48 13
#> 3 2019-01-01 03:30:02 17 19 14
#> 4 2019-01-01 03:30:03 22 39 22
#> 5 2019-01-01 03:30:04 15 21 15
#> 6 2019-01-01 03:30:05 23 40 34

Here simply reorder the columns so Y is first

data2 = sampleXYZ
data2 = data2[, c("Time", paste0("accelerometer_", c("Y", "X", "Z")))]

when we calculate counts, this lines up correctly (x is 3rd axis, y is 2nd)

sampleXYZ_counts2 <- counts(data = data2, hertz = 100,
                            x_axis = 3,y_axis = 2,z_axis = 4, time_column = 1)
head(sampleXYZ_counts2)
#>                  Time  x  y  z
#> 1 2019-01-01 03:30:00  4 89 11
#> 2 2019-01-01 03:30:01 21 48 13
#> 3 2019-01-01 03:30:02 17 19 14
#> 4 2019-01-01 03:30:03 22 39 22
#> 5 2019-01-01 03:30:04 15 21 15
#> 6 2019-01-01 03:30:05 23 40 34

they are equal

all.equal(sampleXYZ_counts, sampleXYZ_counts2)
#> [1] TRUE

If we pass in the original data, but “incorrectly” specify the 3rd column is X

sampleXYZ_counts3 <- counts(data = sampleXYZ, hertz = 100,
                            x_axis = 3, y_axis = 2,z_axis = 4, time_column = 1)
head(sampleXYZ_counts3)
#>                  Time  x  y  z
#> 1 2019-01-01 03:30:00 93  4 11
#> 2 2019-01-01 03:30:01 50 21 13
#> 3 2019-01-01 03:30:02 22 17 14
#> 4 2019-01-01 03:30:03 39 21 22
#> 5 2019-01-01 03:30:04 25 15 15
#> 6 2019-01-01 03:30:05 42 22 34

We see these are different, which is understandable because “x” means different things

all.equal(sampleXYZ_counts, sampleXYZ_counts3)
#> [1] "Component \"x\": Mean relative difference: 1.149771" 
#> [2] "Component \"y\": Mean relative difference: 0.6900884"

If we reorder the columns it still persists, which means the method is
not agnostic to the order of the columns

sampleXYZ_counts3 = sampleXYZ_counts3[c("Time", "y", "x", "z")]
colnames(sampleXYZ_counts3) = c("Time", "x", "y", "z")
all.equal(sampleXYZ_counts, sampleXYZ_counts3)
#> [1] "Component \"x\": Mean relative difference: 0.09035909"
#> [2] "Component \"y\": Mean relative difference: 0.06063813"

head(sampleXYZ_counts)
#>                  Time  x  y  z
#> 1 2019-01-01 03:30:00  4 89 11
#> 2 2019-01-01 03:30:01 21 48 13
#> 3 2019-01-01 03:30:02 17 19 14
#> 4 2019-01-01 03:30:03 22 39 22
#> 5 2019-01-01 03:30:04 15 21 15
#> 6 2019-01-01 03:30:05 23 40 34
head(sampleXYZ_counts3)
#>                  Time  x  y  z
#> 1 2019-01-01 03:30:00  4 93 11
#> 2 2019-01-01 03:30:01 21 50 13
#> 3 2019-01-01 03:30:02 17 22 14
#> 4 2019-01-01 03:30:03 21 39 22
#> 5 2019-01-01 03:30:04 15 25 15
#> 6 2019-01-01 03:30:05 22 42 34

^{Created on 2022-03-24 by the reprex package (v2.0.1)}

Session info

sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.1.2 (2021-11-01)
#>  os       Debian GNU/Linux 10 (buster)
#>  system   x86_64, linux-gnu
#>  ui       X11
#>  language (EN)
#>  collate  C.UTF-8
#>  ctype    C.UTF-8
#>  tz       Etc/UTC
#>  date     2022-03-24
#>  pandoc   2.14.0.3 @ /usr/lib/rstudio-server/bin/pandoc/ (via rmarkdown)
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  package        * version    date (UTC) lib source
#>  activityCounts * 0.1.2      2019-07-31 [1] CRAN (R 4.1.2)
#>  cli              3.2.0.9000 2022-03-16 [1] Github (r-lib/cli@51463d2)
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#> ──────────────────────────────────────────────────────────────────────────────

Clarify input acceleration units in the Readme or the Vignette.

Explain the input units.