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.