Temporarily remove the ympes dependency due to up stream changes.
No other user facing changes.
The bootstrap_incidence()
, estimate_peak()
and first_peak()
functions, and have been integrated from the downstream i2extras package.
split.incidence2()
mutate.incidence2()
summarise.incidence2()
nest.incidence2()
as_tibble.incidence2()
as.data.table.incidence2()
New methods, $<-.incidence2
and rbind.incidence2
, that drop the incidence2 class if the required invariants are broken.
New functions incidence_()
and regroup_()
that work similar to their existing namesakes save for additional support for tidy-select semantics in some of their arguments.
Named vectors can now be used for the groups
and counts
arguments of incidence()
to allow renaming prior to aggregation. Previously this was only possible with the date_index
input.
incidence()
gains a complete_dates
argument defaulting to FALSE
. If set this is equivalent of a call to incidence()
followed by a call to complete_dates()
with the default arguments. Users wanting more flexibility can still call the complete_dates()
function with different arguments.
All of the aforementioned new methods would previously have dispatched on the underlying data.frame method. If you were relying on this behaviour then you will now need to add a call to as.data.frame()
prior to continuing your pipeline.
incidence2
objects are now built upon tibbles rather than standard data frames. This means where we do not provide methods for incidence2
objects tibble (as opposed to data.frame) methods will be called. An overview of the differences between tibbles and data.frames can be found at https://tibble.tidyverse.org/articles/invariants.html.
incidence()
now warns if a count variable contains missing values and encourages users to handle these prior to calling incidence()
.
The Package now has a hard dependency on the R version (>= 4.1.0).
plot.incidence2()
now works again when applied to incidence2
objects with a grates_period
date_index
. This was inadvertently broken in the 2.2.1 release. Thanks to @Bisaloo for the report (#110).plot.incidence2()
gains arguments n_breaks
, fill
, show_cases
and legend
allowing for a wider range of plot styles. See vignette("incidence2", package = "incidence2")
for examples.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, POSIXct
objects are represented as seconds since the UNIX epoch and, in our experience, this level of granularity is not normally desired for aggregation.
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.