Introduction

Basic use of the trip package.

Data input and validation

library(trip)

## Loading required package: sp

d <- data.frame(x=1:10, y=rnorm(10), tms=Sys.time() + 1:10, id=gl(2, 5))
coordinates(d) <- ~x+y
## a projection should always be set, is it WGS84 or NAD83 . . .
proj4string(d) <- CRS("+proj=laea +ellps=sphere")
tr <- trip(d, c("tms", "id"))
summary(tr)

## 
## Object of class trip
##   tripID ("id") No.Records   startTime ("tms")     endTime ("tms")
## 1             1          5 2015-05-20 21:44:02 2015-05-20 21:44:06
## 2             2          5 2015-05-20 21:44:07 2015-05-20 21:44:11
##   tripDuration tripDistance meanSpeed  maxSpeed meanRMSspeed maxRMSspeed
## 1       4 secs     5.952738  5357.464  7246.576     1301.295    5205.180
## 2       4 secs     6.835383  6151.845 11650.035     1831.194    7324.777
## 
## Total trip duration: 8 seconds (0 hours, 8 seconds)
## 
## Derived from Spatial data:
## 
## Object of class SpatialPointsDataFrame
## Coordinates:
##       min       max
## x  1.0000 10.000000
## y -1.6838  1.397778
## Is projected: TRUE 
## proj4string : [+proj=laea +ellps=sphere]
## Number of points: 10
## Data attributes:
##       tms                      id   
##  Min.   :2015-05-21 07:44:02   1:5  
##  1st Qu.:2015-05-21 07:44:04   2:5  
##  Median :2015-05-21 07:44:06        
##  Mean   :2015-05-21 07:44:06        
##  3rd Qu.:2015-05-21 07:44:08        
##  Max.   :2015-05-21 07:44:11

Simple plotting

plot(tr)
lines(tr)

Plot of a very simple trip object.

Gridding for time spent

tg <- rasterize(tr)
plot(tg, col = c("transparent", heat.colors(25)))

Plot of a rasterized version of the trip, by default this is a map of time-spent.

Example data from diveMove

##library(diveMove)
fname <- system.file(file.path("data", "sealLocs.csv"),
                       package="diveMove")
if (file.exists(fname)) {
    dat <- read.table(fname, sep=";", header = TRUE, stringsAsFactors = FALSE)
    dat$class <- ordered(dat$class, c("Z", "B", "A", "0", "1", "2", "3"))
    dat$time <- as.POSIXct(strptime(dat$time, "%Y-%m-%d %H:%M:%S"), tz = "GMT")
}

dat1 <- subset(dat, !is.na(dat$lon))
coordinates(dat1) <- c("lon", "lat")
sl <- trip(dat1, c("time", "id"))

## merge with data from argosfilter
#library(argosfilter)
#data(seal)
##seal$id <- "ringy2"
##seal[["time"]] <- seal$dtime
##seal$dtime <- NULL
## reconstruct the Argos labels
##seal[["class"]] <- ordered(levels(dat$class)[factor(seal$lc, sort(unique(seal$lc)))], levels(dat$class))
##seal$lc <- NULL


## also adehabitatLT, crawl

## what are we supposed to do with duplicated times?
##  which(!diff(seal$time) > 0)
##[1]   17  116  122 1008 1158 1231 1293 1300
##plot(seal[which(!diff(seal$time) > 0),c("lon", "lat") ])
##points(seal[1 + which(!diff(seal$time) > 0),c("lon", "lat") ], col = "red")

##seal <- seal[!duplicated(seal$time), ]

## drop missing data and combine
##dat <- rbind(dat[!is.na(dat$lon), ], seal[,names(dat)])
##coordinates(dat) <- c("lon", "lat")
##proj4string(dat) <- CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs +towgs84=0,0,0")

##tr <- trip(dat, c("time", "id"))