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This vignette assumes the reader has read the Introduction to
padr vignette. These functions are introduced in
version 0.4.0 of the package and should be considered experimental for
the moment.
thicken creates evenly spaced intervals to which
observations can be aggregated. pad subsequently checks for
missing observations and inserts records where needed. This procedure
can be largely automated because of the assumption of an evenly spaced
interval. However, the functions can’t deliver when asymmetry between
the datetime points is desired. This might occur when observations are
frequent during some moments, and sparse during others. For instance
daily hours vs night periods, or working days vs weekends. We might want
to perform a single analysis where the datetime spaces between
observations are of different lengths.
Lets have a look at all the vehicle incidents in the
emergency dataset. Say you want to monitor the trends of
number of accidents in the second quarter of 2016. You choose a three
hour interval to do the analysis.
library(padr); library(dplyr); library(ggplot2)
veh_acc <- emergency %>%
filter(title == "Traffic: VEHICLE ACCIDENT -",
lubridate::month(time_stamp) %in% 4:6)
veh_acc_line_plot <- function(padded_df,
units_to_last = NULL) {
padded_df %>%
fill_by_value() %>%
thicken("day", "day") %>%
mutate(interval = format_interval(day_hour, "%H", sep = "-" ,
units_to_last = units_to_last)) %>%
ggplot(aes(day, n)) +
facet_wrap(~interval) +
geom_line()
}
veh_acc %>%
thicken("3 hour", "day_hour") %>%
count(day_hour) %>%
pad() %>%
veh_acc_line_plot()
Accidents are more common around rush hours. During nightly hours there are hardly any accidents, and also in the daytime the number drops slightly. We would rather aggregate this to morning rush hour, day time, evening rush hour, and night time.
thicken’s internal work horse is a spanning function. It
takes the datetime variable and spans a datetime variable of the desired
interval around it. When you want your own asymmetric intervals, you
have to build the spanning variable yourself. To help with that, the
functions span_around and subset_span are
provided. span_around is a wrapper around the internal
span function. It will return a spanning with an evenly
spaced interval. Of course you can also create this yourself.
span_date and span_time are convenient
wrappers around seq.Date and seq.POSIXt for
manual spanning. Next, to create an asymmetrical spanning, apply
subset_span on the evenly spaced span. Together with a list
that describes the pattern of points that should be retained.
veh_acc_span <-
span_around(veh_acc$time_stamp, interval = "hour", start_shift = "7 hour") %>%
subset_span(list(hour = c(7, 10, 16, 19)))Note that the argument start_shift is used. This will
add an extra time period before the first observation in the datetime
variable. We do this to ensure that spanning is still fully around the
datetime variable after subsetting. The argument end_shift
is by default equal to start_shift. So we have set this to
7 hours as well in the example.
The thicken_cust and pad_cust functions
both take the spanned object as an argument. thicken_cust
will assign each observation to the closest value in the spanned object.
Note that thicken_cust only does round down, whereas in the
regular thicken there is also the option to round up. This
seems the mostly chooses option in the regular thicken, the
argument was omitted from thicken_cust for simplicity.
pad_cust also needs the spanned object, because it can no
longer infer which observations are lacking from the datetime variable.
By default, pad_cust drops the last value in the spanned
object. If the original spanning was around the datetime variable,
leaving the last value in would create an empty record after all the
observations.
veh_acc_cust <- veh_acc %>%
thicken_cust(spanned = veh_acc_span, colname = "day_hour") %>%
count(day_hour) %>%
pad_cust(veh_acc_span)Now we have our data aggregated to an asymmetric spanning, we can rebuild the plot.
And there we have it, we have aggregated and formatted our data to asymmetric intervals.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.