Description Usage Arguments Details Value References Examples

Calculates the number of items between time stamps using an efficient algorithm.

1 2 |

`startTimes` |
a vector of POSIXct objects representing the time that an item arrived |

`stopTimes` |
a vector of POSIXct objects representing the time that an item departed |

`resolution` |
size of buckets to calculate occupancy within. May be "sec" "min" "hour" or "day" |

`fillup` |
a percentile of the residence times of items to exclude from the return. Only either fillup or initial can be defined. |

`initial` |
the initial count of items in the system at time 0. Only either fillup or initial can be defined. |

`countlast` |
if true, then the last then items are treated as if they depart at the end of the depart time stamp. See more information in Details |

fillup should be used when the initial conditions of the system are unknown. For example a value of 0.95 indicates a 95% chance that any items that were in the system at time 0 have departed and will not be included in the occupancy count.

countlast determines how the stopTimes timestamps are treated. For example if an item has a startTime of 07/07/2014 and the endTime of 07/07/2014 with resolution = "day", then the item will be counted as in the system on 07/07/2014 with countlast = TRUE, but will not be counted as in the system with countlast = FALSE

A data.frame with two coloumns

`times` |
A datetime, class POSIXct |

`counts` |
The number of items in the system at current time |

Concept based on software written by MW Isken. See similar work at http://www.hselab.org/tags/hillmaker.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | ```
library(plyr) # need ddply
attach(hflights) # use hflights dataset
# determine how many planes are at airport each minute
planeCount <- occupancy(startTimes=Arrivals,
stopTimes=Departures, resolution="min", fillup = 0.95)
# determine how many planes are at airport by hour of day using ddply for summary stats
planeCount$hour <- as.POSIXlt(planeCount$times)$hour
byHourOfDay <- ddply(planeCount, c("hour"),
function(x) c(mean = mean(x$counts),
median = median(x$counts),
q90 = quantile(x$counts, 0.9, names = FALSE)))
# display output graphically
plot(byHourOfDay$mean, type = "o")
# Repeat by Carrier, wrapping ddply around call to hillmakeR functions
# This is the pattern to use whenever there are different types of items to count
planeCountbyCarrier <- ddply(hflights, "Carrier",
function(x) occupancy(startTimes=x$Arrivals,
stopTimes=x$Departures, resolution="min", fillup = 0.95))
# determine how many planes are at airport by hour of day
planeCountbyCarrier$hour <- as.POSIXlt(planeCountbyCarrier$times)$hour
byHourAndCarrier <- ddply(planeCountbyCarrier, c("Carrier", "hour"),
function(x) c(mean = mean(x$counts),
median = median(x$counts),
q90 = quantile(x$counts, 0.9)))
plot(subset(byHourAndCarrier, Carrier == "AA")$mean, ylim = c(0, 12.5), type = "o")
for (i in levels(Carrier)){
lines(subset(byHourAndCarrier, Carrier == i)$mean, col = sample(colours(), 1), type = "o" )
}
# example of using initial values instead of fillup option with several types of items
# make a dummy lookup table
initial.lookup <- data.frame(key = levels(Carrier),
value = c(380, 164, 131, 74, 114,
161, 334, 106, 127, 498, 485, 158,
68, 100, 60))
planeCountbyCarrier <- ddply(hflights, "Carrier",
function(x) occupancy(startTimes=x$Arrivals,
stopTimes=x$Departures,
resolution="min",
initial = initial.lookup[initial.lookup$key == x$Carrier[1], "value"]))
``` |

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