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