trip-methods: Function to handle animal track data, organized as `"trip"`s

Description

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

Usage

trip(obj, TORnames)
"["(x, i, j, ..., drop = TRUE)

Arguments

obj

A SpatialPointsDataFrame, or an object that can be coerced to one, containing at least two columns with the DateTime and ID data as per TORnames. It can also be a trip object for redefining TORnames.

TORnames

Either a TimeOrderedRecords object, or a 2-element character vector specifying the DateTime and ID column of obj

trip object

i,j,...

indices specifying elements to extract

drop

unused but necessary for method consistency

Value

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

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="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

Run this code

d <- data.frame(x=1:10, y=rnorm(10), tms=Sys.time() + 1:10, id=gl(2, 5))
coordinates(d) <- ~x+y
## this avoids complaints later, but these are not real track data (!)
proj4string(d) <- CRS("+proj=laea +ellps=sphere")
(tr <- trip(d, c("tms", "id")))

## don't want adehabitatMA to be loaded as a requirement here
## Not run: 
# ## 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"))
# ## End(Not run)


## Not run: 
#    if (require(adehabitatLT)) {
#      data(porpoise)
#      porpoise <- as.trip(porpoise)
#      proj4string(porpoise) <- CRS("+proj=utm +zone=21 +ellps=WGS84 +units=m +no_defs")
#      summary(porpoise)
# 
#    }
# 
# 
#    ## extended example to check that our projection metadata is correct
#    library(maptools)
#    data(wrld_simpl)
#    library(rgeos)
#    library(raster)
# 
#    ## 3 degrees either side (for half a zone . . .)
#    ext <- as(extent(spTransform(porpoise, CRS(proj4string(wrld_simpl)))) + 3, "SpatialPolygons")
#    proj4string(ext) <- CRS(proj4string(wrld_simpl))
#    ## crop to the buffered tracks, and project to its native CRS
#    w <- spTransform(gIntersection(wrld_simpl[grep("United States", wrld_simpl$NAME), ], ext),
#     CRS(proj4string(porpoise)))
# 
#    plot(w)
#    lines(porpoise)
# ## End(Not run)