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difference() giving extra NA values when the time
series is shorter than the lag length. (#310, @mitchelloharawild)vec_ptype2() for yearweek and
yearquarter for non-default week start. (#299)count_gaps() warning message due to dplyr
deprecation in favour of reframe(). (#295).full in *_gaps()
when inputs like T, F are valid. (#275)index2 in
build_tsibble() when a column name contains “index”.
(#284)time() methods (renamed to
time_ts()) to avoid contaminating time() in
stats::window(). (#277)make_year*() to easily supply time
components.stats::start() and stats::end()
for the .full argument in *_gaps(), when
*_gaps() are used in other packages. (#269)rbind() and cbind() for tsibble, and
suggest to use bind_rows() and bind_cols()
instead. (#256)filter_in() for yearweek
class with different week starts of than the default. (#261)as.ts.tbl_ts() to better handle a tsibble of
multiple key variables but single value. (#258).start and .end to
*_gaps() for custom starting and ending time. (#259)holiday_aus() for Boxing Day. If falling
on Sat/Sun, the holiday should be forwarded to 2 days. (#251)count() and tally() S3 methods for
tsibble to behave like tibble input, and hence add_count()
and add_tally(). (#232)yearmonth.character() when inputs
are all numbers without delimiter. (#264)NA is accepted in year*(), instead of
errors before.yearmonth(). (#250, @mitchelloharawild)yearmonth() underlying data for
character inputs. (#226)as_tsibble(). (#229)by is missing in year*().
(#228)intersect(),
union(), and setdiff()) for year*
class. (#235, @wkdavis)full_join() that produces an output with
wrong temporal order. (#247)yearmonth() supported more character formats.
(#142)summarise_all(), and made
dplyr::across() compatible with
summarise.tbl_ts().grouped_ts[0L, ]. (#194, @TylerGrantSmith)new_data() for the yearweek
class. (#199)lubridate::Period, since
Period can be ordered now. (#42)as.period() and
as.duration() for interval class. (#206)yearweek() gained a new argument
week_start to specify the day on which week starts based on
ISO 8601 standard. (#205)yearquarter() gained a new argument
fiscal_start to specify the starting month of a fiscal
year. (#174)index_by() + summarise()
with grouped factors. (#197)select() when removing redundant key. (#196)new_data.grouped_ts() to inform
“grouping structure is ignored”. (#193)vec_slice() updates tsibble data attributes.This release uses the vctrs package internally in {tsibble}.
new_interval() uses a new interface to create the interval
and supports custom interval. Old tsibble objects will receive an error
for “a corrupt tsibble”..full argument in *_gaps supports two
more options, start() and end(), for padding
NA to either starting or ending time. (#147)n in new_data() and
append_row() supports negative integer, appending past
observations to the data. (#186)scale_[x/y/colour/size/alpha/fill]_year*()
for custom ggplot2 scales.pivot_longer() and pivot_wider()
supporting methods for tsibble.bind_rows() and bind_cols() are now
possible for tsibble with dplyr v1.0.0.select() a tsibble now keeps both index and key by
default. (#155)tidyr::drop_na() support for tsibble. (#173)as.ts.tbl_ts() for ignoring implicit
missings. (#160)units_since() in favour of
vec_cast().is.tsibble().as.tsibble().This is a maintenance release due to tidyselect v1.0.0
changes.
This is a maintenance release for CRAN fixes.
This is a maintenance release due to the changes in the upstream package.
Suggests.is.tsibble().has_gaps() & count_gaps() gained a new
argument .name for naming new columns.new_data()/append_row().as.ts.tbl_ts() for
converting tsibble to ts.POSIXlt as index.filter(), slice(), and
[.tbl_ts.slide(), are slided into
the “questioning” lifecycle, in favour of the slide
package. (#143)index_by() supports lambda expression (#91).gather in
as_tsibble.mts() in favour of
pivot_longer.yearweek() handles characters containing keywords
“W”/“Wk”/“Week”, for example yearweek("2019 W03").This is a patch release.
as.ts.tbl_ts() for handling a tsibble of
one row (#124).nest.tbl_ts() for naming (#123).as_tsibble.ts() (#128).index_by(.data, <empty>) by default groups the index
variable rather than previously ungroup().unnest_tsibble() to minimise the impact from
the upcoming API changes in tidyr::unnest().index_by() allows for grouping index variable; and
group_by() will throw out an error for grouping index.semi_join.tbl_ts() and
anti_join.tbl_ts() without suffix (#122).as.tsibble() in favour of
as_tsibble().fill_na() in favour of
fill_gaps().pull_interval() in favour of
interval_pull().regular integrated to interval,
(2) attribute ordered integrated to index. A
previously stored tsibble object may receive a warning “corrupt tsibble
object”.library(tidyverse) in
front.id() for creating key for the consistence of
tidy selectors, and will be defunct as of v0.9.0.pull_interval() in favour of
interval_pull().as.tsibble() in favour of
as_tsibble(). The warning is displayed once per
session.gather = TRUE in as_tsibble.ts()
in favour of pivot_longer = TRUE.fill_na() in favour of
fill_gaps(), and will be defunct in the next release.difftime.hms..drop for dropping empty factor or
not in as_tsibble() and build_tsibble().- operator between yearweek, yearmonth, and
yearquarter returns class difftime.key_data to
build_tsibble() for the easy-to-reason purpose.yearquarter() better supports strings that contains
“Q”/“Qtr”/“Quarter”. (#107)as_tsibble.ts() for monthly series
starting at other months than January. (#89)guess_frequency.yearweek() returns 52.18 for more
accurate weekly representation, instead of 52.n() now can be called in slice.tbl_ts().
(#95)slice.tbl_ts().arrange.tbl_ts().*_join() for not finding key or index when
by is specified. (#102)data(tourism) and 2017 data.v0.8.0 grouped data
frames, tsibble allows for empty key values and disregards the lazily
stored key. All operations now recalculate the keying structure.grouped_ts) is a subclassing of
grouped_df..size is retired in stretch()
in favour of .step.stretch() gained a new .fill = NA
argument, which returns the same length as the input. To restore the
previous behaviour, please use .fill = NULL. (#88)slide_tsibble(), tile_tsibble(),
stretch_tsibble() provide fast and shorthand subsetting
tsibble by rolling rows.slide() gained a new .step argument for
calculating at every specified step instead of every single step.update_tsibble() to update key and index a bit
easier.rbind() for dropping custom index class.
(#78)count_gaps() for dropping custom index
class.count_gaps() now only summarises keys with gaps instead
of all the keys.guess_frequency.yearweek() returning
Inf. (@jeffzi, #84)fill_gaps() returns a grouped
tsibble too.find_duplicates().fill_na() in favour of
fill_gaps()..drop in column-wise dplyr verbs.key_by() (no idea why it’s there).scan_gaps() joins the family of implicit missing values
handlers.future_. It requires the furrr
package to be installed. (#66)[.tbl_ts. (#76)time_in() and filter_index().split_by() and case_na().find_duplicates().fill_na()..data is a complete tsibble,
fill_gaps() gives a warning instead of an error when
name-value pairs are supplied.filter_index() works for a grouped tsibble.This release simplifies the “key” structure. The nesting and crossing definition has been removed from the “key” specification. One or more variables forming the “key”, are required to identify observational units over time, but no longer assume the relationship between these variables. The nesting and crossing structure will be dealt with visualisation and forecasting reconciliation in downstream packages.
count_gaps.tbl_ts() returns a tibble containing gaps
for each key value rather than an overall gap, which is consistent with
the rest of tsibble methods. And all output column names that are not
supplied by users gain a prefixed “.”.time_unit() accepts interval input instead
of time vectors to avoid overheads, also marked as internal
function.slider() and
pslider() as new functions partial_slider()
and partial_pslider(). Argument .partial is
removed from slider() and pslider() to feature
a simpler interface.group from
build_tsibble(). In order to construct a grouped tsibble,
x requires a grouped df.has_gaps() to quickly check if there
are implicit time gaps for each key in a tsibble.new_data() to produce the future of a
tsibble.filter_index() to filter time window for a
tsibble.time_in() to check if time falls in the
ranges in compact expression, with no need for time zone
specification.new_tsibble() creates a subclass of a tsibble.fill_na() to fill_gaps(), for more
expressive function name and consistency to has_gaps() and
count_gaps(). Soft-deprecated fill_na().
(#71)is_duplicated(), are_duplicated()
and duplicates().POSIXct, time zone will be displayed in the header
via print().holiday_aus() that
requires package “timeDate”.summarise.tbl_ts(), select.tbl_ts()
& fill_na.tbl_ts() scoping issue (#67).slice.tbl_ts() correctly handles logical
NA.fill_na() will only replace implicit time gaps by
values and functions, and leave originally explicit NA
intact.tidyr::fill() gained support for class “grouped_ts”,
and it is re-exported again. (#73)fill_na(), in favour of
fill_gaps().find_duplicates(), in favour of
are_duplicated().case_na(), and will be defunct in next
release.split_by(), which is under development as S3
generic in dplyr.as.tsibble(), following
as.tibble() in tibble..drop argument in column-wise verbs, and
suggested to use as_tibble().select() doesn’t select index, it will inform users
and automatically select it.append_row() for easily appending new
observations to a tsibble. (#59)/, consistent
with lm. (#64)fill_na() for multiple replacements
when using with group_by(), introduced in v0.5.1.as_tsibble.grouped_df() respected its existing groups
and removed argument group. (#60)select.tbl_ts(). (#63)case_na() &
split_by().unnest.lst_ts respects the ordering of “key” values.
(#56)split_by() and nest.tbl_ts() respect the
appearance ordering of input variables. (#57)group_indices.tbl_ts() and key_indices()
return consistent formats as its generic.key no longer accepted character.nest.tbl_ts().index_by() gives more informative error when LHS is
named as index.tibbleas.tibble().tibble().tile() gained a new argument
.bind = FALSE.+ &
-) for yearweek, yearmonth, and yearquarter.new_interval() creates an “interval” object with the
specified values.fill_na() for replacing values
when group_by().[.group_by() +
summarise(). (#47)unnest.lst_ts().gather.tbl_ts(). (#54)slide() & stretch() use the same
coercion rules as dplyr::combine() now, if
.bind = TRUE.pillar.tibble.This release introduced the breaking changes to the “interval” class
to make tsibble better support finer time resolution (e.g. millisecond,
microsecond, and nanosecond). The “interval” format changes from upper
case to short hand. To support new time index class, only
index_valid() and pull_interval() need to be
defined now.
group_by_key() to easily group the
key variables.slide() gained a new argument
.align = "right" to align at “right”, “center”, or “left”.
If window size is even for center alignment, either “center-right” or
“center-left” is needed.+ & -)
for yearweek, yearmonth, and yearquarter.slide() and stretch() gained a new
argument .bind = FALSE.NA or NULL with 0 in
the “interval” class to make the representation simpler.interval class has new slots of “millisecond”,
“microsecond”, “nanosecond”.time_unit() is a function instead of S3 generic, and
made index extension a bit easier.format.yearweek().group_by.lst_ts() for dropping the grouping
information.stretch2() only applying .f to one
input.as_tsibble.grouped_df() for groups.
(#44).fill = NULL for
slide().purrr
style exactly (#35):
slide(), tile(), stretch()
return lists only instead of numerics before.slide2(), pslide() to map over
multiple inputs simultaneously.slide_dbl(),
slide_int(), slide_chr(),
slide_lgl().slide() gained a new argument .partial to
support partial sliding.x to .x in slider(),
tiler(), stretcher().pslider(), ptiler(),
pstretcher() support multiple inputs now, and split them in
parallel.holiday_aus() for Australian national and
state-based public holiday.diff() for year-week, year-month, and
year-quarter.yearweek(), yearmonth(),
yearquarter() supported for character.slide2(), pslide(),
tile2(), ptile(), stretch2(),
pstretch() to slide over multiple inputs simultaneously
(#33).units_since() for index classes.is_53weeks() for determine if the year has 53 ISO
weeks.key_sum() for extending tsibble.as_tsibble.ts() removed the tsp attribute
from the value column.lst_ts (#25).tsibble().fill_na() and
count_gaps when a tsibble of unknown interval.as_tsibble.tbl_ts() &
as_tsibble.grouped_ts() now return self (#34).id() is used in the tsibble context
(e.g. as_tsibble(), tsibble(),
build_tsibble()) regardless of the conflicts with dplyr or
plyr, to avoid frustrating message (#36).select.tbl_ts() now preserved index.as.ts.tbl_ts() for ignoring the
value argument when the key is empty.[.tbl_ts() when subsetting columns by
characters (#30).fill_na.tbl_ts() dropping custom index
class (#32).format.yearweek() due to the boundary
issue (#27).index contains NA,
abort.nycflights13 >= 1.0.0.The tsibble package has a hexagon logo now! Thanks Mitch (@mitchelloharawild).
difference() computes lagged differences of a
numeric vector. It returns a vector of the same length as the input with
NA padded. It works with mutate().gather()/spread(),
nest()/unnest() to tbl_ts.tsummarise().index2 deviates from index (using
index_by()), the index2 will be part of
grouping variables.This release (hopefully) marks the stability of a tsibble data object
(tbl_ts). A tbl_ts contains the following
components:
key: single or multiple columns uniquely identify
observational units over time. A key consisting of nested and crossed
variables reflects the structure underlying the data. The programme
itself takes care of the updates in the “key” when manipulating the
data. The “key” differs from the grouping variables with respect to
variables manipulated by users.index: a variable represents time. This together the
“key” uniquely identifies each observation in the data table.index2: why do we need the second index? It means
re-indexing to a variable, not the second index. It is
identical to the index most time, but start deviating when
using index_by(). index_by() works similarly
to group_by(), but groups the index only. The dplyr verbs,
like filter(), mutate(), operates on each time
group of the data defined by index_by(). You may wonder why
introducing a new function rather than using group_by()
that users are most familiar with. It’s because time is indispensable to
a tsibble, index_by() provides a trace to understanding how
the index changes. For this purpose, group_by() is just too
general. For example, index_by() + summarise()
aggregates data to less granular time period, leading to the update in
index, which is nicely and intuitively handled now.interval: an interval class to save a list
of time intervals. It computes the greatest common factor from the time
difference of the index column, which should give a
sensible interval for the almost all the cases, compared to minimal time
distance. It also depends on the time representation. For example, if
the data is monthly, the index is suggested to use a
yearmonth() format instead of Date, as
Date only gives the number of days not the number of
months.regular: since a tsibble factors in the implicit
missing cases, whether the data is regular or not cannot be determined.
This relies on the user’s specification.ordered: time-wise and rolling window functions assume
data of temporal ordering. A tsibble will be sorted by its time index.
If a key is explicitly declared, the key will be sorted first and
followed by arranging time in ascending order. If it’s not in time
order, it broadcasts a warning.tsummarise() and its scoped variants. It can
be replaced by the combo index_by() +
summarise() (#20). tsummarise() provides an
unintuitive interface where the first argument keeps the same size of
the index, but the remaining arguments reduces rows to a single one.
Analogously, it does group_by() and then
summarise(). The proposed index_by() solves
the issue of index update.inform_duplicates() (defunct) to
find_duplicates() to better reflect its functionality.key_vars() and group_vars() return a
vector of characters instead of a list.distinct.tbl_ts() now returns a tibble instead of an
error.dplyr::do() and
tidyr::fill(), as they respect the input structure.index_sum(), and replaced by
index_valid() to extend index type support.index_by() groups time index, as the counterpart of
group_by() in temporal context.count_gaps() and gaps()
counts time gaps (implicit missing observations in time).yearweek() creates and coerces to a year-week object.
(#17)fill_na.tbl_ts() gained a new argument of
.full = FALSE. .full = FALSE (the default)
inserts NA for each key within its time period,
TRUE for the entire time span. This affects the results of
fill_na.tbl_ts() as it only took TRUE into
account previously. (#15)drop argument to .drop in
column-wise dplyr verbs.duplicated argument in
pull_interval().group_by.tbl_ts() behaves exactly the same as
group_by.tbl_df now. Grouping variables are temporary for
data manipulation. Nested or crossed variables are not the type that
group_by() thinks.glimpse.tbl_ts().fill_na().transmute.tbl_ts() for a univariate time series
due to unregistered tidyselect helpers. (#9).select.tbl_ts() and
rename.tbl_ts() for not preserving grouped variables
(#12).select.tbl_ts() and
rename.tbl_ts() for renaming grouped variables.tbl_ts gains a new attribute index2,
which is a candidate of new index (symbol) used by
index_by().attr(grouped_ts, "vars") stores characters instead of
names, same as attr(grouped_df, "vars").This release introduces major changes into the underlying
tbl_ts object:
tbl_ts class
to reduce the object size, and computed on the fly when printing.arrange() and slice()
functions.tbl_ts object is a symbol
now instead of a quosure.tbl_ts object is an unnamed
list of symbols.key_update() to change/update the keys for a given
tsibble.unkey() as an S3 method for a tsibble of key size <
2.key_indices() as an S3 method to extract key
indices.split_by() to split a tsibble into a list of data by
unquoted variables.build_tsibble() allows users to gain more control over
a tsibble construction.as_tsibble.msts() for multiple seasonality time
series defined in the forecast package.as_tsibble.ts() for daily time series (when
frequency = 7).group_by.tbl_ts() does not accept named
expressions.filter() and slice()). This avoids unnecessary
re-computation for many function calls.tsummarise() including
tsummarise_all(), tsummarise_if(),
tsummarise_at().slide(),
tile(), stretch(), are no longer defined as S3
methods. Several new variants have been introduced for the purpose of
type stability, like slide_lst() (a list),
slide_dfr() (a row-binding data frame),
slide_dfc() (a column-binding data frame).index variable must sit in the first name-value
pair in tsummarise() instead of any position in the
call.transmute.tbl_ts() keeps the newly created variables
along with index and keys, instead of throwing an error before.glimpse.tbl_ts()format.key() for nesting crossed with another
nesting.This release marks the complete support of dplyr key verbs.
tidyr::fill() fills NA backward or forward
in tsibble.tbl_ts support for
dplyr::*_join().tbl_ts support for dplyr::transmute()
and dplyr::distinct() and return an error.inform_duplicates() informs which row has duplicated
elements of key and index variables.summarise.tbl_ts() and
tsummarise.tbl_ts(), when calling functions with no
parameters like dplyr::n().summarise.tbl_ts() and
tsummarise.tbl_ts(), one grouping level should be dropped
for the consistency with dplyr::summarise() for a grouped
tbl_ts.NULL and tbl_ts are supported in
as_tsibble(). An empty tsibble is not allowed.group_by.tbl_ts(.data, ..., add = TRUE) works as
expected now.grouped_ts and
grouped_df.NEWS.md file to track changes to the
package.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.