sftrack: Modern classes for tracking and movement data

sftrack provides modern classes for tracking and movement data, relying on sf spatial infrastructure. Tracking data are made of tracks, i.e. series of locations with at least 2-dimensional spatial coordinates (x,y), a time index (t), and individual identification (id) of the object being monitored; movement data are made of trajectories, i.e. the line representation of the path, composed by steps (the straight-line segments connecting successive locations). sftrack is designed to handle movement of both living organisms and inanimate objects.

The development and design of the sftrack package follow three simple principles:

Minimal and focused: this is basically the Unix philosophy. Do a simple thing, and do it well. The scope of the package is limited (see above), with as few dependencies as possible;
User-friendly: sftrack is designed to be as easy to use as familiar R structures like data.frames and sf objects. sftrack objects are tidy, and follow the idea that rows are records (locations) and columns are variable (following the semantics of tracking and movement data);
Flexible and extensible: sftrack is meant first for users to use on their data, but also directly designed to address other developers’ needs for their own tracking packages.

Getting started

To get started, install sftrack directly from CRAN, or check the development version on GitHub with the remotes package:

# To install the stable version from CRAN
install.packages("sftrack")

# To install the dev version with built vignettes
remotes::install_github("mablab/sftrack", ref = "dev", build_vignettes = TRUE)

The dev version is updated much more frequently and should pass the majority of CRAN checks. However, if you install the dev version, understand it may still contain some bugs. Please submit any bug you find to the issues page.

A minimal introduction to `sftrack` and `sftraj` objects

The easiest way to create an sftrack object is to start from a data.frame with all information as columns, typically the raw data extracted from telemetry devices:

library("sftrack")
data(raccoon)
raccoon$timestamp <- as.POSIXct(as.POSIXlt(raccoon$timestamp, tz = "EST5EDT"))
head(raccoon)
#>   animal_id latitude longitude           timestamp height hdop vdop fix
#> 1   TTP-058       NA        NA 2019-01-18 19:02:30     NA  0.0  0.0  NO
#> 2   TTP-058 26.06945 -80.27906 2019-01-18 20:02:30      7  6.2  3.2  2D
#> 3   TTP-058       NA        NA 2019-01-18 21:02:30     NA  0.0  0.0  NO
#> 4   TTP-058       NA        NA 2019-01-18 22:02:30     NA  0.0  0.0  NO
#> 5   TTP-058 26.06769 -80.27431 2019-01-18 23:02:30    858  5.1  3.2  2D
#> 6   TTP-058 26.06867 -80.27930 2019-01-19 00:02:30    350  1.9  3.2  3D

In order to convert your raw data into an sftrack object, use the function as_sftrack(). The function requires the three main elements of tracking data:

coordinates of the locations in at least the x and y axes (can be UTM, lat/long, etc., with projection provided in crs);
timestamps of the locations as POSIXct (or as integer);
grouping information (referred to as a “groups”), providing at least the identity of each individual.

my_sftrack <- as_sftrack(
  data = raccoon,
  coords = c("longitude","latitude"),
  time = "timestamp",
  group = "animal_id",
  crs = "+init=epsg:4326")
#> Warning in CPL_crs_from_input(x): GDAL Message 1: +init=epsg:XXXX syntax is deprecated.
#> It might return a CRS with a non-EPSG compliant axis order.
head(my_sftrack)
#> Sftrack with 6 features and 10 fields (3 empty geometries) 
#> Geometry : "geometry" (XY, crs: WGS 84) 
#> Timestamp : "timestamp" (POSIXct in EST5EDT) 
#> Groupings : "sft_group" (*id*) 
#> -------------------------------
#>   animal_id latitude longitude           timestamp height hdop vdop fix     sft_group
#> 1   TTP-058       NA        NA 2019-01-18 19:02:30     NA  0.0  0.0  NO (id: TTP-058)
#> 2   TTP-058 26.06945 -80.27906 2019-01-18 20:02:30      7  6.2  3.2  2D (id: TTP-058)
#> 3   TTP-058       NA        NA 2019-01-18 21:02:30     NA  0.0  0.0  NO (id: TTP-058)
#> 4   TTP-058       NA        NA 2019-01-18 22:02:30     NA  0.0  0.0  NO (id: TTP-058)
#> 5   TTP-058 26.06769 -80.27431 2019-01-18 23:02:30    858  5.1  3.2  2D (id: TTP-058)
#> 6   TTP-058 26.06867 -80.27930 2019-01-19 00:02:30    350  1.9  3.2  3D (id: TTP-058)
#>                     geometry
#> 1                POINT EMPTY
#> 2 POINT (-80.27906 26.06945)
#> 3                POINT EMPTY
#> 4                POINT EMPTY
#> 5 POINT (-80.27431 26.06769)
#> 6  POINT (-80.2793 26.06867)

summary_sftrack(my_sftrack)
#>     group points NAs          begin_time            end_time length_m
#> 1 TTP-041    223   0 2019-01-18 19:02:30 2019-02-01 18:02:07 10212.55
#> 2 TTP-058    222   0 2019-01-18 19:02:30 2019-02-01 18:02:30 26893.27

While sftrack objects contain tracking data (locations), they can easily be converted to movement data (with a step model instead) with as_sftraj:

my_sftraj <- as_sftraj(my_sftrack)
head(my_sftraj)
#> Sftraj with 6 features and 10 fields (3 empty geometries) 
#> Geometry : "geometry" (XY, crs: WGS 84) 
#> Timestamp : "timestamp" (POSIXct in EST5EDT) 
#> Grouping : "sft_group" (*id*) 
#> -------------------------------
#>   animal_id latitude longitude           timestamp height hdop vdop fix     sft_group
#> 1   TTP-058       NA        NA 2019-01-18 19:02:30     NA  0.0  0.0  NO (id: TTP-058)
#> 2   TTP-058 26.06945 -80.27906 2019-01-18 20:02:30      7  6.2  3.2  2D (id: TTP-058)
#> 3   TTP-058       NA        NA 2019-01-18 21:02:30     NA  0.0  0.0  NO (id: TTP-058)
#> 4   TTP-058       NA        NA 2019-01-18 22:02:30     NA  0.0  0.0  NO (id: TTP-058)
#> 5   TTP-058 26.06769 -80.27431 2019-01-18 23:02:30    858  5.1  3.2  2D (id: TTP-058)
#> 6   TTP-058 26.06867 -80.27930 2019-01-19 00:02:30    350  1.9  3.2  3D (id: TTP-058)
#>                         geometry
#> 1                    POINT EMPTY
#> 2     POINT (-80.27906 26.06945)
#> 3                    POINT EMPTY
#> 4                    POINT EMPTY
#> 5 LINESTRING (-80.27431 26.06...
#> 6 LINESTRING (-80.2793 26.068...

Both objects can easily be plotted with base R plot functions, which highlights the fundamental difference between tracking and movement data (sftrack on the left; sftraj on the right):

plot(my_sftrack, main = "Tracking data (locations)")
plot(my_sftraj, main = "Movement data (steps)")

Roadmap

Data class converters from the main tracking packages, such as move::Move and trackeR::trackeRdata, integrated into as_sftrack;
More plotting options for tracks and trajectories (in base R and ggplot2);
Provide Gantt chart-like or chronogram-like graphs;
Dynamic exploration of trajectories.

How can you help?

Submit any bug you find to the issues page;
Address open questions (see below);
Contribute use cases (see below).

Address open questions: We need your feedback!

While the foundations of the package are now pretty solid, we are still dealing with open questions about several aspects of the package, including the names of sftrack variables (e.g. coordinates, timestamps, id and error), the structure of the grouping factor, or the structure of the error term.

If you have strong opinions or simply want to help on the technical side, we invite you to comment on those open issues here.

Contribute use cases: We need your feedback!

We also need to precisely understand what is expected from such a package. The idea here is to collect all possible use cases for a trajectory object in R. We know they are multiple, and will contribute our own use cases — however, we want sftrack to be as useful as possible, and to act as a center piece for movement in R, so we need you to tell us how you would use it. In other words, we want to understand what you expect from such a package, as a user or as a developer. For this, we ask you to fill out special issues in the GitHub tracker of the package, following the ‘Use case’ template.

Use cases do not need to be very complicated, but need to present a specific use in human terms, the technical requirements associated to it, and the input and output of the use case. Such use case could look like this:

[Use case] Amazing plot for trajectory

Use case:

Plot a trajectory using my special_trajplot function, which shows [something amazing].

Requirements:

spatial coordinates (x,y) as geographic coordinates with projection information

a time (t) as POSIXt object, ordered in time

information that identifies individuals (e.g. animal) for each location

data associated to each location directly accessible

Input: a sftrack object

Output: a plot with [something amazing] about the trajectory

Additional information: See my special_trajplot function here [with link].

Another example could be like this:

[Use case] Fill in missing locations in a sequence

Use case: Fill in the missing locations of a trajectory that contains spatial or temporal gaps. (for instance coming from GPS with failed fixes); In other words add in the missing values of a trajectory, i.e. timestamps with no geographic coordinates.

Requirements:

a time (t) as POSIXt object, ordered in time

information that identifies sequences of locations (optional, if several sequences), which could be different circuits of one individual, or different individuals, etc.

sftrack should be capable of handling/storing missing values

Input: a sftrack object

Output: a sftrack object with additional timestamps for gaps (but otherwise identical in every way to the original sftrack)

Additional information: See adehabitatLT::setNA, which does exactly that on ltraj objects.