Function to handle animal track data, organized as trip objects

Create an object of class trip, extending the basic functionality of SpatialPointsDataFrame-class by specifying the data columns that define the "TimeOrdered" quality of the records.

trip(obj, TORnames, correct_all = TRUE)

trip(obj) <- value

# S4 method for trip
split(x, f, drop = FALSE, ...)

# S4 method for trip,ANY,ANY,ANY
[(x, i, j, ..., drop = TRUE)

Arguments

obj	A data frame, a grouped data frame or a `SpatialPointsDataFrame-class` containing at least two columns with the DateTime and ID data as per `TORnames`. See Details.
TORnames	Either a `TimeOrderedRecords` object, or a 2-element character vector specifying the DateTime and ID column of `obj`
correct_all	logical value, if `TRUE` the input data is corrected for common problems
value	A 4-element character vector specifying the X, Y, DateTime coordinates and ID of `obj`.
x	trip object
f	grouping vector as per `split()`
drop	unused but necessary for method consistency
i, j, ...	indices specifying elements to extract

Value

A trip object, with the usual slots of a SpatialPointsDataFrame-class and the added TimeOrderedRecords. For the most part this can be treated as a data.frame with Spatial coordinates.

Details

The original form of trip() required very strict input as a 'SpatialPointsDataFrame' and specifying which were the time and ID columns, but the input can be more flexible. If the object is a grouped data frame ('dplyr-style') then the (first) grouping is assumed to define individual trips and that columns 1, 2, 3 are the x-, y-, time-coordinates in that order. It can also be a trip object for redefining TORnames.

The trip() function can ingest track_xyt, telemetry, SpatialPointsDataFrame, sf, trackeRdata, grouped_df, data.frame, tbl_df, mousetrap, and in some cases lists of those objects. Please get in touch if you think something that should work does not.

Track data often contains problems, with missing values in location or time, times out of order or with duplicated times. The correct_all argument is set to TRUE by default and will report any inconsistencies. Data really should be checked first rather than relying on this auto-cleanup. The following problems are common:

duplicated records (every column with the same value in another row)
duplicated date-time values
missing date-time values, or missing x or y coordinates
records out of order within trip ID

For some data types there's no formal structure, but a simple convention such as a set of names in a data frame. For example, the VTrack package has AATAMS1 which may be turned into a trip with trip(AATAMS1 %>% dplyr::select(longitude, latitude, timestamp, tag.ID, everything()) In time we can add support for all kinds of variants, detected by the names and contents.

See Chapter 2 of the trip thesis for more details.

Methods

Most of the methods available are by virtue of the sp package. Some, such as split.data.frame have been added to SPDF so that trip has the same functionality.

trip: signature(obj="SpatialPointsDataFrame", TORnames="ANY")
trip: signature(obj="SpatialPointsDataFrame", TORnames="TimeOrderedRecords")
trip: signature(obj="ANY", TORnames="TimeOrderedRecords"): create a trip object from a data frame.
trip: signature(obj="trip", TORnames="ANY"): (Re)-create a trip object using a character vector for TORnames.
trip: signature(obj="trip", TORnames="TimeOrderedRecords"): (re)-create a trip object using a TimeOrderedRecords object.

Examples



d <- data.frame(x=1:10, y=rnorm(10), tms=Sys.time() + 1:10, id=gl(2, 5))

## the simplest way to create a trip is by order of columns

trip(d)
#> Warning: input looks like longitude/latitude data, assuming +proj=longlat +datum=WGS84
#> 
#> Object of class trip
#>   tripID ("id") No.Records   startTime ("tms")     endTime ("tms") tripDuration
#> 1             1          5 2020-10-27 23:24:33 2020-10-27 23:24:37       4 secs
#> 2             2          5 2020-10-27 23:24:38 2020-10-27 23:24:42       4 secs
#> 
#>   data.columns data.class                  
#> 1          tms    POSIXct **trip DateTime**
#> 2           id     factor **trip ID**      
#> 

## or a grouped data frame can be used, the grouping is used as the trip ID
## library(dplyr)
## # use everything() to keep all other columns
## d %>% group_by(id) %>% select(x, y, tms, everything())

sp::coordinates(d) <- ~x+y
## this avoids complaints later, but these are not real track data (!)
sp::proj4string(d) <- sp::CRS("+proj=laea +ellps=sphere", doCheckCRSArgs = FALSE)
(tr <- trip(d, c("tms", "id")))
#> 
#> Object of class trip
#>   tripID ("id") No.Records   startTime ("tms")     endTime ("tms") tripDuration
#> 1             1          5 2020-10-27 23:24:33 2020-10-27 23:24:37       4 secs
#> 2             2          5 2020-10-27 23:24:38 2020-10-27 23:24:42       4 secs
#> 
#>   data.columns data.class                  
#> 1          tms    POSIXct **trip DateTime**
#> 2           id     factor **trip ID**      
#> 

 ## real world data in CSV
mi_dat <- read.csv(system.file("extdata/MI_albatross_sub10.csv", package = "trip"),
            stringsAsFactors = FALSE)
## installed subset because the data is quite dense
## mi_dat <- mi_dat[seq(1, nrow(mi_dat), by = 10), ]
mi_dat$gmt <- as.POSIXct(mi_dat$gmt, tz = "UTC")
mi_dat$sp_id <-  sprintf("%s%s_%s_%s", mi_dat$species,
         substr(mi_dat$breeding_status, 1, 1), mi_dat$band, mi_dat$tag_ID)
sp::coordinates(mi_dat) <- c("lon", "lat")
## there are many warnings, but the outcome is fine
## (sp_id == 'WAi_14030938_2123' has < 3 locations as does LMi_12143650_14257)
mi_dat <- trip(mi_dat, c("gmt", "sp_id") )
#> Warning: ordering input records by trip ID, then time
#> Warning: removing trip IDs that have too few elements (<3):  
#> 'LMi_12143650_14257,WAi_14030938_2123'
#> Warning: input looks like longitude/latitude data, assuming +proj=longlat +datum=WGS84
plot(mi_dat, pch = ".")
#lines(mi_dat)  ## ugly

mi_dat_polar <- reproj(mi_dat, "+proj=stere +lat_0=-90 +lon_0=154 +datum=WGS84")
plot(mi_dat_polar, pch = ".")
lines(mi_dat_polar)
if (FALSE) {
## a simple example with the common fixes required for basic track data

dat <- read.csv("trackfile.csv")
names(dat)  ## e.g. [1] "long" "lat" "seal" "date" "local" "lq"
library(sp)
coordinates(dat) <- c("long", "lat")

## date/times may be in a particular time zone, please check
dat$gmt <- as.POSIXct(strptime(paste(dat$date, dat$local),
                      "%d-%b-%y %H:%M:%S"), tz="GMT")

## if there are problems in the data, this will error
tr <- trip(dat, c("gmt", "seal"))

## the following code tries to fix common problems

## remove completely-duplicated rows
dat <- dat[!duplicated(dat), ]
## order the rows by seal, then by time
dat <- dat[order(dat$seal, dat$gmt), ]
## fudge duplicated times
dat$gmt <- adjust.duplicateTimes(dat$gmt, dat$seal)

## finally, convert to Spatial and create trip object
coordinates(dat) <- c("long", "lat")
tr <- trip(dat, c("gmt", "seal"))
}

Function to handle animal track data, organized as `trip` objects

Arguments

Value

Details

Methods

See also

Examples