These are GPS information from 3 trips.
Data frames with the following variables.
IndexMeasurement number
Timea POSIXt, Time of measurement
Elevationa numeric vector, Elevation in Feet
Leg.Dista character/numeric vector, The distance
traveled in that leg (in feet for ccc)
Leg.Timea difftime, the time of that leg
Speeda numeric vector, Speed in mph
Directiona numeric vector, Direction in Degrees, 0 is North, 90 is East, 180 is South, 270 is West
LatLona character vector, Latitude and Longitude as characters
Leg.Dist.fa numeric vector, Length of that leg in feet
Leg.Dist.ma numeric vector, Length of that leg in miles
Lata numeric vector, Numeric latitude
Lona numeric vector, Numeric longitude (negative for west)
Distancea numeric vector, Distance from start in feet
Distance.fa numeric vector, Distance from start in feet
Distance.ma numeric vector, Distance from start in miles
Time2a difftime, Time from start
Time3a difftime, cumsum of Leg.Time
The data frame ccc came from when I was walking back to my
office from a meeting and decided to take the scenic route and started
the GPS.
The data frame h2h is a trip from my office to another for a
meeting. The first part is traveling by car, the last part by foot
from the parking lot to the building. Speed is a mixture of
distributions.
The data frame towork came from driving to work one morning
(the first point is where the GPS got it's first lock, not my house).
The overall trip was mostly NorthWest but with enough North and
NorthEast that a simple average of direction shows SouthEast, good
example for circular stats.
# NOT RUN {
if( interactive() ){
with(ccc, TkApprox(Distance, Elevation))
}
# }
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