High-Performance Open-Source Archive
Thanks @vspinu for fixing a bug where examples would fail when run on leap days.
Fixed a bug where calling using
time_complete(.by = ) would cause an error.
Rolling calculations have been fixed to work with the new rolling data.table functions.
time_add gains the n arg to help with
adding timespans to time-based vectors more easily.
New function time_breakpoints as a simpler
alternative to time_breaks that works much better with
ggplot2
New options ‘xfirst’ and ‘xlast’ for ‘timeplyr.roll_month’ to signify how to handle impossible dates. When adding or subtracting months, when ‘xlast’ is chosen, the date which crosses impossible intervals last is returned and likewise when ‘xfirst’ is chosen, the date which crosses the interval first is returned. The default option is now ‘xlast’ and time differences are calculated using ‘xlast’ for consistency. This is in contrast to lubridate which calculates time differences using ‘preday’.
New functions time_add, time_subtract,
time_floor and time_ceiling for adding
timespans to dates, date-times and other time-based vectors.
Internally much of the methodology for calculating time differences has been re-written to be simpler and in many cases faster.
timeplyr now imports tzdb for access to the C++ date header file.
Internal speed improvements to sequence creation. Specifically period sequences are now vectorised.
Time intervals in timeplyr have been re-imagined. They are now
fixed-width intervals which means any one time interval vector will have
a common width for all intervals it contains, e.g. 1 month. This makes
internal operations much faster and simplifies the logic for working
with these objects. The data structure for these intervals is a vector
of LHS start times with an attribute to specify the timespan or width of
the interval. All intervals remain LHS closed and RHS open. As a
side-note this new data structure makes it possible for time intervals
to be used in data.table.
Most functions which are not time related have been removed.
Many arguments have been renamed or removed.
time_type argument which is no longer
used. When specifying units such as days, weeks, months or years, exact
timespans will be used.roll_dst and
roll_month with the exception of the sequence
functions.time_by argument has been renamed to either
width or timespan depending on the
context.Functions time_expandv, time_completev,
time_span_size have been renamed to time_grid,
time_complete_missing, time_grid_size
respectively.
Functions time_summarisev and
time_aggregate have been removed, use
time_cut_width instead.
time_cut has been deprecated and renamed to
time_cut_n.
edf, asc and desc have
been removed as they do not fit the purpose of the package.
.roll_na_fill has been removed.
A custom timeplyr object timespan to create and use
timespans for various operations.
New functions resolution and
granularity to calculate the resolution and granularity of
a time vector respectively. See the help page ?resolution
for more details.
All of the tidy functions like fslice that are not
time-related will be removed in the next release. These can now be found
in fastplyr.
It is likely that in the near future, objects of class ‘time_interval’ will be re-imagined to be more efficient fixed-width intervals with a different data structure. Currently the data structure is a length-two list containing start and end times. Most intervals in timeplyr are fixed-width intervals, such as for example a vector of intervals that span a week or a month. A more efficient data structure for this might be to floor the object to the start of its respective interval and to simply add an attribute that details the width of the interval.
The to argument that allows for aggregating date and
date-time variables to a specified time is currently inclusive and will
be changed to be non-inclusive in the near-future. This is because
intervals in timeplyr are left-closed right-open intervals and so the
to argument should reflect this.
time_intervals are now used by default. Use the ‘timeplyr.use_intervals’ option to control this behaviour globally.
New ggplot2 scales for year_months and year_quarters.
time_ggplot can now handle ‘year_month’ and
‘year_quarter’ objects.
New function time_cut_width which is the same as
time_aggregate but with less arguments.
New roll_lag and roll_diff methods for
time-series objects.
Creating ‘year_months’ and ‘year_quarters’ from numeric vectors now always coerces them to integer internally.
Fixed a small bug in roll_diff where the order
vector was not being used in the case when a vector of lags is
supplied.
Fixed a regression where some methods stopped being exported.
Fixed a regression where time_complete didn’t accept
a grouped_df.
Users should now be able to replace values of a
time_interval in the usual way.
roll_diff has been simplified and gains a new
argument differences to allow for recursive
differencing.
roll_lag has been simplified. It internally utilises
cheapr’s lag2_ with the recursive argument always set to
TRUE.
time_aggregate no longer accepts a group
g argument.
Removed some unnecessary arguments from
time_by.
Deprecated most of the data frame specific time_
functions, with the exception of time_by,
time_episodes, time_expand and
time_complete.
Internal bug fix for period time differences.
time_aggregate gains the from,
to, time_floor and week_start
arguments.
Moved much of the C++ functionality to the cheapr package, which is on CRAN.
roll_na_fill can now also handle data
frames.
time_episodes has a custom print method displaying
key summary metrics.
New class time_interval to represent right-open time
intervals.
The internal code of time_cut has been simplified
and improved. It can now also handle very large values of
n. When time_by is left NULL, the
maximum possible number of breaks used is
( diff(range(x)) / gcd_diff(x) ) + 1.
time_cut and time_summarisev are now
slightly faster.
scm now handles vectors containing only
NA values appropriately.
Exported additional sequence functions.
The default summary functions in stat_summarise
should now work for most vector types.
fdistinct is now faster when
sort = TRUE.
In time_episodes, the calculation for when there is
a mixture of events and non-events has been significantly
simplified.
New functions roll_lag and roll_diff
for rolling lags and differences.
The time_roll_ functions are now faster due to
having amended the time window size calculation.
age_years is now much faster when there are
relatively few distinct pairs of start and end dates compared to the
full data.
Period-arithmetic is now much faster and more efficient due a new method for time differencing where distinct start-end values are used.
time_count no longer completes implicit missing gaps
in time. Use time_complete instead. When using
from and to, time_count no longer
removes out-of-bounds time values and instead simply converted to
NA.
tidyverse-style functions now use a new method for data-masking
variables which aligns more closely with the tidyverse equivalents. The
previous method evaluated the expressions supplied through
... twice, once to generate the variables, and twice to
extract the resulting variable names. These are now evaluated
once.
Improved print method speed for year_month and
year_quarter
New classes year_month and
year_quarter. Inspired by ‘zoo’ and ‘tsibble’, these
operate similarly but the underlying data is an integer count of months
for year_month, and quarters for year_quarter.
This means that creating sequences is very fast and arithmetic is
simpler and more intuitive on the ‘year-month’ level.
cpp_roll_diff and scm now appropriately
handle NA values.
New function gcd for fast calculation of greatest
common divisor with tolerance. Time granularity calculations have also
been sped up.
Fixed a rare build issue using R_SHORT_LEN_MAX on
certain systems. Thanks @barracuda156.
This version brings major performance improvements, including new algorithms for subsetting and rolling calculations.
The roll_na_fill algorithm has been improved
significantly.
Calculation of row numbers are faster and more efficient.
All ‘C++’ functions are now registered using the cpp11 package.
cpp_which is now available as a more efficient and
sometimes faster alternative to which.
The double comparison functions have been migrated to the package ‘cppdoubles’.
roll_na_fill has been mostly rewritten in C++ for
speed and efficiency.
roll_growth_rate now accepts groups through the
g argument.
New function roll_across for grouped rolling
calculations.
Fixed a bug where sequence2 would error when
nvec was a zero-length vector.
Fixed a bug where time_granularity would error with
zero-length vectors.
is_whole_number is now faster and the underlying C++
function is safer.
Period calculations are now faster and more memory efficient and thus all the time functions are also faster.
The .keep_na argument of duplicate_rows
is now deprecated and replaced with drop_empty.
Most Rcpp functions are now more memory efficient due to disabling the RNGscope where possible.
Fixed an integer overflow bug in sequence2.
The as_period argument in time_diff has
been deprecated and removed.
time_num_gaps and time_has_gaps now
handle NA values more appropriately.
‘collapse’ pivot is now used for quantile summaries
in q_summarise.
timeplyr now utilises relative differences for all double comparisons using Rcpp
All double comparisons are now fully vectorised and recycling occurs without additional memory allocation.
New function num_na to efficiently calculate number
of missing values.
timeplyr 0.2.1 published to CRAN
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