---
title: "Programming with a move2 object"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Programming with a move2 object}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
library(move2)
library(assertthat)
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

# Function overview

## Extracting information from a `move2` object

- `sf::st_coordinates()`: returns the coordinates from the the events in the
  track(s)

- `sf::st_crs()`: returns the projection of the tracks(s)

- `mt_time()`: returns the timestamps for each event in the track

- `mt_track_data()`: returns the table containing the information associated to
  the tracks

- `mt_track_id()`: returns a vector of the track id associated to each event

- `unique(mt_track_id())`: returns the names of the tracks

- `mt_n_tracks()`: returns the number of the tracks

- `nrow()/table(mt_track_id())`: returns the number of events in total and per
  track respectively

- `mt_time_column()`: returns the name of the column containing the timestamps
  used by the `move2` object

- `mt_track_id_column()`: returns the name of the column containing the track
  ids used by the `move2` object

## Transforming other classes to a `move2` object

- `mt_as_move2()`: creates a `move2` object from objects of class `sf`, 
`data.frame`, `telemetry`/`telemetry list` from *ctmm*, `track_xyt` from *amt* 
or `Move`/`MoveStack` from *move*. 
  
## Transforming a `move2` object into other classes

- `to_move()`: converts to a object of class `Move`/`MoveStack`

- `mt_track_id(x) <- NULL`: to remove `move2` class from the object, it will 
be recognized as a object of class `sf`

- `mt_as_event_attribute(x, names(mt_track_data(x)))`: join all attributes (event and track) 
into one table. If additionally `mt_track_id(x) <- NULL` is applied, the 
resulting `sf` object should behave as any `data.frame`

## Useful functions

- `mt_read()`: read in data downloaded from movebank, by just stating the path to the file

- `mt_read(mt_example())`: example dataset

- `dplyr::filter(x, !sf::st_is_empty(x))`: exclude all empty locations

- `filter_track_data(x, .track_id = c("nameTrack1", "nameTrack3")`: subset to one or more tracks

- `split(x, mt_track_id(x))`: split a `move2` object into a list of single
  objects per track. Alternatively see `dplyr::mutate()`, `dplyr::group_by()`, 
  `group_by_track_data()` to apply calculations to tracks separately

- `mt_stack()`: combine multiple `move2` objects into one

- `mt_as_track_attribute()`/`mt_as_event_attribute()`: move columns between 
track and event attributes (and vv)

- `mt_set_track_id()`: replace track ids with new values, set new column to 
define tracks or rename track id column

- `mutate_track_data()`: add or modify attributes in the track data

- `sf::st_transform()`: to reproject the `move2` into a different projection

- `mt_aeqd_crs()`: create a AEQD coordinate reference system

- `mt_track_lines()`: convert a trajectory into lines for plotting with e.g.
  `ggplot`

- use the option `options(sf_max.plot=1)` to display a single plot of the track.
  The attribute that should be used to color the tracks can be specified, e.g.
  `plot(x["individual_local_identifier"])`.
  [Here](https://r-spatial.github.io/sf/articles/sf5.html) is more information
  on how to do simple plots.

All functions of the `move2` package are described
[here.](https://bartk.gitlab.io/move2/reference/index.html)

# Structure of the `move2` object 
- it is based on the `sf` objects and compatible with a lot of
  `dplyr`/`tidyverse` based functionality

- information of non location data (other sensors as e.g. acceleration,
  magnetometer,etc) are associated to an empty locations.

- *track attributes* and *event attributes* are distinguished. *event
  attributes* are attributes associated to each recorded event (location or non
  location), these will at least have a time and track id associated to them.
  *track attributes* are attributes associated to each track (e.g. individual,
  species, sex, etc), these will at least contain the track id, and can be
  retrieved with the function `mt_track_data()`

## Explanation

To be able to expand and use the object in `move2` it is important to understand
how the objects is structured. Here we explain some of the choices and explain
the requirements.

A move object in `move2` uses the `S3` class system, this is less rigors then
the `S4` system that was used in the original `move` package. The objects are
based on the `sf` objects from the `sf` package. This change is inspired by
several factors, first by basing on `sf` we are able to profit from the speed
and improvements that went into that package, second it makes it directly
compatible with a lot of `dplyr`/`tidyverse` based functionality. To ensure
information specific to movement is retrained we use attributes. This is in a
fairly similar style to `sf`.

To facilitate working with the associated sensor data we store other records
with an empty point. This means, for example, acceleration and activity
measurements can be part of the same `tbl`/`data.frame`.

The `sf` package and `sf` in general allow to store coordinates as three
dimensional records. As the altitude of tracking devices is typically much less
accurate, few functions actually support this functionality we do not use it at
this time.

In the `move` package we implemented separate objects for one single individuals
(`Move`) and multiple individuals (`MoveStack`). Here we choose to not do this.
This reduces complexity. If functions require single individuals to work it is
easy enough to split these of.

### Event data

Tracking data generally consists of a time series of observations from a range
of "sensors". Each of these observation or events at least have a time and a
sensor associated with them. Some have a location recorded by, for example, a
gps sensor other have non locations data like acceleration or gyroscope
measurements. All events are combined in one large dataset, this facilitates
combined analysis between them (e.g. interpolation to the position of an
acceleration measurement). However for some analysis specific sensors or data
types will be needed therefore filtering functions are available that subset the
data to, for example, all location data.

### Separating track attributes

To facilitate working with the trajectories we distinguish between track
attributes and event attributes. Track level data could be individual and
species names, sex and age. This can furthermore greatly facilitate object sizes
as that is not duplicated. Keeping track attributes separate also contributes to
data integrity as ensures track level attributes are consistent within a track.

## Attributes

In this section we go through the attributes that `move2` uses.

### `time_column`

This attributes should contain a string with a length of `1`. This string
indicates in which column the timestamp information of the locations in it. The
string should thus be an existing column. The time column in most cases will
contain timestamps in the `POSIXct` format. In some cases timestamps will not be
referring to an exact time point. For example when simulating movement data or
analysis from a video. In these cases times can also be stored as `integer` or
`numeric` values.

### `track_id_column`

This attribute should contain a string of length `1`. A column with this name
should be contained both in the `track_data` attribute and in the main dataset.
This column also functions as the link between the `track_data` and the main
data, linking the individual attributes to the individual data.

### `track_data`

This dataset contains the track level data. Properties of the individual follows
(e.g. sex, age and name) can be stored here. Additionally other deployment level
information can be contained. As the move2 package does not separate
individuals, tags and deployments. All information from these 3 entities in
movebank are combined here.

## Special columns

### `time_column`

Using the `time_column` attribute this column can be identified, for quick
retrieval there is the `mt_time` function. Values should be either timestamps
(e.g. `POSIXct`, `Date`) or `numeric`. Numeric values are facilitated as it can
be useful for simulation, videos and laboratory experiments were absolute time
reference is not available or relevant.

### `track_id_column`

This column is identified by the `track_id_column` attributes, values can either
be a `character`, `factor` or `integer` like values. For retrieval there is the
`mt_track_id` function.

## General considerations

### Quality checking

In `move` relatively stringent quality checking was done on the object. This
enforced certain attributes for a trajectory that are sensible but in practice
are not always adhered to. Some of these properties are:

- Every record had a valid location (except for `unUsedRecords` but those were
  rarely used)

- Records were time ordered within individual

- All individuals were ordered

- Timestamps could not be duplicated.

Even though these are some useful properties for subsequent work when reading
not all data adheres to these standards. To solve this there were options to
remove duplicated records but these simply took the first record. Here we take a
more permissive approach where less stringent checking is done on the input
side. This means functions working with `move2` need to ensure input data
adheres to their expectations. To facilitate that several assertion functions
are provided that can quickly check data. Taking this approach gives the users
more flexibility in resolving inconsistencies within R. We provide several
functions to make this work quick. For specific use cases more informed
functions can be developed.

If you are writing functions based on the `move2` package and your function
assumes a specific data structure this can best be checked with `assert_that` in
combination with one of the assertion functions. This construct results in
informative error messages:

```{r, error=T}
data <- mt_sim_brownian_motion(1:3)[c(1, 3, 2, 6, 4, 5), ]
assert_that(mt_is_time_ordered(data))
```

### Function naming schemes

To facilitate finding functions and assist in recognizably we use a prefix. For
functions relating to movement trajectories we use `mt_`, similar to how the
`sf` package uses `st_` for spatial type. This prefix has the advantage of being
short compared to `move_`. Functions for accessing data from
[movebank](https://www.movebank.org) use the prefix `movebank_`. Furthermore do
all assertions functions start with either `mt_is_` or `mt_has_`.

### Return type segment wise properties

When analyzing trajectories frequently metrics are calculated that are
properties of the time period in between two observations. Prime examples are
the distance and speed between locations. This means that for each track with a
length of $n$ locations there are $n-1$ measurements. To facilitate storing and
processing this data we pad each track with a `NA` value at the end. This
ensured that return vectors from functions like `mt_distance`, `mt_speed` and
`mt_azimuth` return vectors with the same length of as the number of rows in the
`move2` object. If the return values from these kind of functions are assigned
to the `move2` object the properties stored in the first row reflect the value
for the interval between the first and second row.

Some metrics are calculated as a function of the segment before and after a
segment (e.g. turn angles). In these cases the return vectors still have the
same length however they are padded by a `NA` value at the beginning and end of
each track so that the metric is stored with the location it is representative
for.

### Data size

Data sets have been growing considerably over the past decade since `move` was
written. The ambition with `move2` is to facilitate this trend. It should work
smoothly with trajectories of more then a million records. We have successfully
loaded up to 30 million events into R, however at some stage memory limitations
of the host computer start being a concern. This can to some extent be
alleviated by omitting unnecessary columns from the data set, either at download
or when reading the data. An alternative approach would be to facilitate working
with trajectories on disk or within a database (alike `dbplyr`). However since
many functions and packages we rely on do not support this, we opt not to do
this. Therefore, if reducing the data loaded does not solve the problem, it can
be advisable to use a computer with more memory or when possible split up
analysis per track.
