Implementation Information of padr

Edwn Thoen

2017-02-19

Regarding daylight savings time and different time zones, some explicit choices had to be made. They are explained here, so the user knows what to expect when using the padr functions. The last paragraph provides detailed information about the workings of thicken.

Daylight savings time

The padr package relies heavily on the base R functions seq.Date and seq.POSIXt to span the date and datetime variables for thicken and pad In the latter, choices had to be made about ignoring daylight savings time or not. Take the following example.

library(lubridate)
start_val <- ymd_hms('2016-03-25 08:15:00', tz = 'CET')
seq(start_val, by = 'day', length.out = 4) 
## [1] "2016-03-25 08:15:00 CET"  "2016-03-26 08:15:00 CET" 
## [3] "2016-03-27 09:15:00 CEST" "2016-03-28 09:15:00 CEST"
seq(start_val, by = 'DSTdays', length.out = 4)
## [1] "2016-03-25 08:15:00 CET"  "2016-03-26 08:15:00 CET" 
## [3] "2016-03-27 08:15:00 CEST" "2016-03-28 08:15:00 CEST"

As the help file of seq.POSIXt mentions: ‘The difference between “day” and “DSTday” is that the former ignores changes to/from daylight savings time and the latter takes the same clock time each day.’ The interval of a datetime variable in the padr context is determined by the lowest time unit that differs over the individual datetime observations. When the hour of the observation shifts as a result of daylight savings time this will cause the interval to change from ‘day’ to ‘hour’. This implies that when we pad a variable that has interval ‘day’, the daylight savings time can’t be ignored and we thus take the same clock time everyday. The only other interval that can be distorted by daylight savings time is ‘week’.

seq(start_val, by = 'week', length.out = 2)
## [1] "2016-03-25 08:15:00 CET"  "2016-04-01 09:15:00 CEST"

Spanning of variables of this interval is therefore implemented as:

seq(start_val, by = '7 DSTdays', length.out = 2)
## [1] "2016-03-25 08:15:00 CET"  "2016-04-01 08:15:00 CEST"

Different time zones

The arguments start_val (in pad and thicken) and end_val (in pad) can have a different time zone than the input datetime variable. In this case the function will coerce the argument to be of same time zone as the input variable while keeping the clock time equal. It does so with throwing a warning. Take the following example:

library(padr)
to_pad <- data.frame(dt = seq(ymd_hms('20160202 145312', tz = 'CET'),
                                      length.out = 2, by = 'day'))
pad(to_pad, start_val = ymd_hms('20160201 145312', tz = 'EST'))
## Warning: coercing time zone from EST to CET
##                    dt
## 1 2016-02-01 14:53:12
## 2 2016-02-02 14:53:12
## 3 2016-02-03 14:53:12

This behavior was chosen over throwing an error or converting to the other time zone with adjusting the clock time, because it is assumed that the user did not use two time zones on purpose. Time zone differences are more likely due to different object sources, rather than being created intentional. For instance using as.POSIXct without specifying the time zone, will use the time zone from the System’s locale. When creating a datetime variable with lubridate on the ohter hand, it will use UTC as the default time zone.

Details working thicken

thicken takes the interval of the input datetime variable and spans a variable of a higher interval around it. When rounding is “down” each value in the input variable will be mapped to the closest value in the spanned variable, that is lower or equal than itself. When rounding is “up” this is to the closest value that is larger than itself. Graphically this looks like:

The blue dots are the values of the input datetime variable. The red dots are the timepoints that are spanned around the original time points. In this example the rounding is “down”, so the original values are mapped to the closest value in spanned, that is earlier than the original.

For all the seven intervals that could be spanned, by default the spanned variable will be as ‘tightly’ around the input variable as the interval allows. Meaning that the first value of the spanned variable is the latest possible timepoint that is still smaller than the smallest value of the input variable. The last value is the first possible timepoint that is larger than the the largest value of the input variable.

This is the default spanning behavior of the seven intervals. The first five will be of class Date, the last two will be of class POSIXct.