Vectors and scalars versus structures

tagtools project team

2023-07-12

Welcome to the vectors-vs-structures vignette! Thanks for taking some time to get to know our package; we hope it is useful to you. In this quick vignette, you will distinguish between vectors and structures.

Estimated time for this practical: 15 minutes.

These vignettes assume that you have R/Rstudio installed on your machine, some basic experience working with them, and can execute provided code, making some user-specific changes along the way (e.g. to help R find a file you downloaded). We will provide you with quite a few lines. To boost your own learning, you would do well to try and write them before opening what we give, using this just to check your work.

Setup: write “cats_test_raw.nc”, and load it as a large list, MN

For this vignette we will use the NetCDF file cats_test_raw.nc. If you want to run this example, download the “cats_test_raw.nc” file from https://github.com/animaltags/tagtools_data and change the file path to match where you’ve saved the files

library(tagtools)
cats_file_path <- "nc_files/cats_test_raw.nc"
MN <- load_nc(cats_file_path)

This will write the file “cats_test_raw.nc”, and load it into your current R session as a list object called MN. Looks good? Good work! Confused? See the vignette load-tag-data.

Sensor data structures & extracting data vectors

Having caught up, we return to sensor data structures. Like $info:

MN$info

Show/Hide Results

#> $creation_date
#> [1] "2023-06-14 12:58:11.202458"
#> 
#> $depid
#> [1] "cats_test_raw"
#> 
#> $data_source
#> [1] "20160730-091117-Froback-11-part.csv"
#> 
#> $data_nfiles
#> [1] "1"
#> 
#> $data_format
#> [1] "csv"
#> 
#> $device_make
#> [1] "CATS"
#> 
#> $device_type
#> [1] "Archival"
#> 
#> $dephist_device_tzone
#> [1] "0"
#> 
#> $dephist_device_regset
#> [1] "dd-mm-yyyy HH:MM:SS"
#> 
#> $dephist_device_datetime_start
#> [1] "2016-07-27 16:29:38"
#> 
#> $sensors_list
#> [1] "3 axis Accelerometer,3 axis Magnetometer,3 axis Gyroscope,Temperature,Pressure,Light level,"

these are structures that contain both the data and some information about it. Typing any of the variable names followed by enter will show what is in it. For example, try this for Acceleration:

str(MN$A, max.level = 1) # display the STRucture of MN$A
#> List of 19
#>  $ data              : num [1:720000, 1:3] -2.42 -2.4 -2.35 -2.3 -2.2 ...
#>  $ sampling          : chr "regular"
#>  $ sampling_rate     : num 400
#>  $ sampling_rate_unit: chr "Hz"
#>  $ depid             : chr "cats_test_raw"
#>  $ creation_date     : chr "2023-06-14 12:58:08.013504"
#>  $ history           : chr "read_cats"
#>  $ type              : chr "A"
#>  $ full_name         : chr "Acceleration"
#>  $ description       : chr "triaxial acceleration"
#>  $ unit              : chr "m/s2"
#>  $ unit_name         : chr "metres per second squared"
#>  $ unit_label        : chr "m/s^2"
#>  $ start_offset      : num 0
#>  $ start_offset_units: chr "second"
#>  $ column_name       : chr "x,y,z"
#>  $ frame             : chr "tag"
#>  $ axes              : chr "FRU"
#>  $ files             : chr "20160730-091117-Froback-11-part.csv"

In Matlab, Octave and R it will look a little different, but will have all the same information. Specifically, we are after the variable A, which is contained within the large list MN. MN$A is how we access it, in R.

The results tell you that the acceleration data were sampled at a nominal rate of 32 Hz (32 samples per second) and are in units of meters per second squared. If you are more familiar with data vectors than with structures, you can easily get the data out of the structure, and into its own standalone object, using:

A <- MN$A$data
sampling_rate <- MN$A$sampling_rate
str(A, max.level = 1)
sampling_rate

Show/Hide Results

#> [1] "Results for str(A, max.level = 1):"
#> [1] "----------------------------------"
#>  num [1:720000, 1:3] -2.42 -2.4 -2.35 -2.3 -2.2 ...
#> [1] "----------------------------------"
#> [1] "Result for sampling_rate:"
#> [1] "----------------------------------"
#> [1] 400

This makes a matrix A with the acceleration data and a scalar sampling_rate with the sampling rate, which in this case is 32. You can also just keep everything stored in the list object, if you like (in R there is not a clear reason to pull them out). If you do this, you’d just have to keep typing MN$A$data and MN$A$sampling_rate instead of A or sampling_rate. If you move forward to plots-and-cropping, you will still use the animaltag list MN, pulling data directly from it.

Review

You’ve learned to distinguish between vectors and structures, and go from a structure to a vector (or scalar).

Nice work! You’re all set on vectors-vs-structures.

If you’d like to keep working through these practicals, plots-and-cropping is a logical continuation. It’s particularly nice to learn to use the plotter that comes with tagtools, called plott.

vignette('plots-and-cropping', package = 'tagtools')

Otherwise, you might consider jumping ahead to data-quality-error-correction. In it, you’ll get started with the work of fixing errors in data.

vignette('data-quality-error-correction', package = 'tagtools')

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Animaltags home pages: http://animaltags.org/ (old), https://animaltags.netlify.app/ (new), https://github.com/stacyderuiter/TagTools (for latest beta source code), https://stacyderuiter.github.io/TagTools/articles/TagTools (vignettes overview)