interval = "day" or
interval = daily in a call to incidence will force the
resultant date_index variable to be a
<Date>. Functionally this is a wrapper around
as.Date() that ensures the underlying data are whole
numbers and that time zones are respected.incidence() will now warn if objects are created
with POSIXct columns. The motivation for this is that, internally,
The by parameter of complete_dates() is
now defunct. This was previously passed to an underlying
seq function when, in practice, it should always have been
forced to 1 to match the precision of the underlying
date_index.
complete_dates() will now error if called on an
input with a allow_POSIXct = TRUE to maintain old
behaviour.
Version 2.0.0 is a major, breaking release of incidence2. We have undertaken a significant refactor that both simplifies the underlying code base and makes the user interface more consistent and robust. Although the main changes are highlighted below, users are strongly advised to read through the accompanying documentation and vignettes.
We no longer support NSE (e.g. tidyselect semantics) within the package. Our motivation for removing support for NSE are both the complexity it can bring to the underlying code (making long term maintenance harder) and the complexity it can cause for other users / developers who want to build on top of incidence2.
new_incidence(), validate_incidence(),
build_incidence(), get_n(),
get_interval(), get_timespan() and
facet_plot() are now defunct and will error if
called.
complete_counts() is now renamed
complete_dates() and gains two new parameters,
expand and by. If expand is TRUE
(default) then complete_dates() will attempt to use
function(x) seq(min(x), max(x), by = by) to generate a
complete sequence of dates.
The incidence() function now always returns output
in long format with dedicated columns for the count variables and values
(set by arguments count_names_to and
count_values_to).
incidence() is now less flexible in what it can
accept for the interval argument. For more complex date
groupings users are encouraged to perform their require data
transformations prior to calling incidence().
The default plotting of incidence objects as been greatly simplified. Sensible defaults have been chosen to enable a quick visual overview of incidence objects but users are advised to call ggplot2 directly for more bespoke plotting.
cumulate() functionality (previously
deprecated in 1.2.0).fill argument in complete_counts()
is now 0 rather than NA.incidence() when more than one column was
given for the date_index.new_incidence(): A minimal incidence constructor.validate_incidence(): Check for internal consistency of
incidence-like object.build_incidence(): Allows you to construct an incidence
object whilst specifying your own date grouping function.format.incidence()cumulate() will now give a deprecation error. We have
removed the function to avoid users erroneously regressing against a
cumulative count.incidence() when dates were a character
vector and the the default, daily, interval was specified.dplyr to handle list based columns
(e.g. record-type objects from vctrs). For data.frames with
only atomic columns, data.table is still used.complete_counts().plot() and facet_plot() now have a
centre_dates argument which can be set to
FALSE to get histogram-esque date labels for single month,
quarter and yearweek groupings.Due to multiple changes in the underlying representation of incidence2 objects this release may possibly break old workflows particularly those relying on the old implementations of date grouping:
grates for
date grouping. This introduces the s3 classes yrwk,
yrmon, yrqtr, yr,
period and int_period as well as associated
constructors which incidence now builds upon. As a result
of this the aweek
dependency has been dropped.keep_first and keep_last
functions.incidence objects now faster due to
underlying use of data.table.show_cases = TRUE (see #42).count variable of a
pre-aggregated input to incidence function.