3. Structure of sftrack/sftraj objects

In sftrack: Modern Classes for Tracking and Movement Data

knitr::opts_chunk$set(
    echo = TRUE,
    fig.width = 6,
    fig.asp = 0.7
)
library("lwgeom")

This is section will go more depth into the structure of sftrack/sftraj objects and how to work with them.

To begin, sftrack and sftraj objects are essentially data.frame objects with the 3 required columns (group, geometry, and time). However, they are also of the subclass sf. This allows them to act like sf objects when working with functions in the sf package but act as sftrack objects for all other actions. It should be noted that when possible sftrack objects will mimic sf functionality and thus many of the same words and tactics are used.

library("sftrack")

# Make tracks from raw data
data("raccoon", package = "sftrack")
#raccoon <- read.csv(system.file("extdata/raccoon_data.csv", package="sftrack"))
raccoon$month <- as.POSIXlt(raccoon$timestamp)$mon + 1

raccoon$time <- as.POSIXct(raccoon$timestamp, tz = "EST")
coords <- c("longitude","latitude")
group <- list(id = raccoon$animal_id, month = as.POSIXlt(raccoon$timestamp)$mon+1)
time <- "time"
error <- "fix"
crs <- 4326
# create a sftrack object
my_sftrack <- as_sftrack(data = raccoon, coords = coords, group = group, time = time, error = error, crs = crs)

# create a sftraj object
my_sftraj <- as_sftraj(data = raccoon, coords = coords, group = group, time = time, error = error, crs = crs)

In order for sftrack to function as an sf object, we create the data object as an sf object first (using st_as_sf()), and then add the sftrack attributes to the object. The class of an sftrack object is sftrack -> sf -> data.frame although the data.frame class is rarely called upon.

There are five attributes total to an sftrack object, two of these are created by sf (sf_column and agr), and the additional three are created by sftrack (group_col, time_col, and error_col).

attributes(my_sftrack)[-(1:2)]

The sftrack level attributes are simply pointers to the data. Any attributes relevant to the grouping or geometry are stored in those columns themselves.

Geometry

The geometry column is an sfc object and contains the important spatial information for the track. As NA points are common and important in movement data, we create the sfc object with the option na.fail = TRUE.

my_sftrack$geometry

The sfc column varies in structure dependent on the movement class. An sftrack is a collection of POINTs while an sftraj is a GEOMETRY collection of POINTs and LINESTRINGs.

df1 <- data.frame(
    id = c(1, 1, 1, 1,1,1),
    month = c(1,1,1,1,1,1),
    x = c(27, 27, 27, NA,29,30),
    y = c(-80,-81,-82,NA, 83,83),
    timez = as.POSIXct("2020-01-01 12:00:00", tz = "UTC") + 60*60*(1:6)
)

test_sftraj <- as_sftraj(data = df1, group = list(id = df1$id, month = df1$month),
    time = df1$timez, active_group = c("id","month"), coords = df1[,c("x","y")])
test_sftraj$geometry

Grouping

The grouping column is very specialized, and we will cover it in its own section. To begin, the novel attributes it stores are the active_group and the sort_index which is a factor of the current active groups. The grouping class consists of single row level group: s_groups (a.k.a single groups) and a column level collection of s_groups called a c_grouping (a.k.a column/collection grouping). This column acts as a robust storage column for the groupings and is maintained across a sftrack object.

attributes(my_sftrack$sft_group[1:10])
summary(my_sftrack)

Time

The time column must be either an integer or POSIXct and the column must be of one type of time. Beyond that there is not much specialized functionality in the column. Sftrack uses the time column to order outputs for analysis, and attempts to order outputs when originally making an sftrack object, however, the data.frame is not required to be ordered for analysis. A call to check_ordered() is called before analysis, and otherwise it is assumed the order does not matter. This is particularly true for a sftraj, where the geometry level contains information about t1 and t2.

Error

The error column is the column with the relevant error information for the spatial points in it. At present we have not built particular functionality but plan to in the future or reserve this for other developers to build upon.

Subsetting

An sftrack object acts like a data.frame and sf whenever appropriate. Because of this you can subset an sftrack object as you would a data.frame.

In this way row subsetting is very straight forward, as each row represents an individual point in time.

my_sftrack[1:10,]

Unlike a data.frame, however, sftrack attempts to retain the geometry, group, and time columns, in order to maintain sftrack status. This is similar to how sf deals with the geometry column.

my_sftrack[1:3,c(1:3)]

To turn off this feature, use the drop = TRUE argument which returns a data.frame object instead. If you would like to revert to an sf object, sf::st_sf(data) will return the object to an sf object.

my_sftrack[1:3,c(1:3), drop = TRUE]

sftrajs work nearly the same as sftracks, however because they are a step model where the steps are modeled as step1 (t1 ->t2) its important to note that subsetting will not automatically recalculate any new steps for you even if the original t2 point has been deleted.

If your subsetting will also change the end points for steps, then you can recalculate using step_recalc(). The output which is your original sftraj object but with the geometry column recalculated to the new t2s based on the timestamp. The results of which can be wildly different than the original subsetted data.frame. So be careful.

plot(my_sftraj, main = "Original")
new_traj <- my_sftraj[seq(10, nrow(my_sftraj), 10), ]

plot(new_traj, main = "Before recalculation")

plot(step_recalc(new_traj),  main = "After recalculation")

Some basic methods and functions of sftrack and sftraj objects

print

The print() layout is a modified version of the sf print function. It returns important info summarazing the sftrack object like the geometry information and burst information. The print function defaults to printing 1000 rows and displaying all the columns. This can be modified using the n_row and n_col arguments, which subset the printed output in the repsective axis. When using n_col the display will show the grouping geometry, and time fields as well as any other columns starting from column 1 until #columns + 3 = n_col. n_col and n_row are optional arguments, and if values are not supplied they default to the data.frame defaults. Note : n_row is not a corrolary to head(), as head() physically subsets the data while the n_row option just modifies the printed output.

If

print(my_sftrack, 5, 10)

summary

summary() works as youd expect for a data.frame, except it displays the grouping column as a count of each active_group combination and the active_groupfor that column.

summary(my_sftrack)

summary_sftrack

summary_sftrack() is a special summary function specific for sftrack objects. It summarizes the data based on the beginning and end of each grouping as well as the total distance of the grouping. This function uses st_distance from the sf package and therefore outputs in units of the CRS. In this example the distance is in meters.

summary_sftrack(my_sftrack)

You can also trigger this function by using summary(data, stats = TRUE)

summary(my_sftrack, stats = TRUE)

sftrack documentation built on March 31, 2023, 7:27 p.m.