A number of unit tests are updated, such that daylight saving time errors cannot creep in when they are ran on a system with a different time zone than UTC or CET. Error pointed out to prof. Ripley of the CRAN team.
When the specified interval is equal or lower than the interval of the datetime variable, thicken will no longer throw an error, but a warning. Request by Matus Goljer, issue #84.
Bug fix, pad did not work when dplyr::group_by had irregular column names (using back ticks). Bug spotted by Jason Hunter in issue #69.
Bug fix, thicken used to return a vector instead of a data.frame when the drop argument was TRUE and the only column in the data.frame was the datetime variable. It will now return a data frame instead. issue #76.
Informative error thrown for Year 2038 problem when date time variable is POSIXt and the year is 2038 or greater. Problem detected by github users darneiri and Blundys. issue #51
The pad function used to break when the dt_var was of class POSIXt and the interval was week or day, when the period crosses a switch from or to daylight savings time. This is fixed. Also, an explicit warning is thrown when it is attempted to use “DSTday” as interval, which does not work. Bugs spotted by Tyler Grant Smith. issue #78
The pad function used to call the deprecated .dots argument in dplyr::groups, which threw a warning when pad was applied on a grouped tibble. This is updated. Thanks to Matt Cowgill and Kristian Gjerde for spotting this. issue #80
Patch release, adjusting the unit tests to play with R.4.* new time zone implementations.
Updated padr to make sure it will work with the upcoming v1.0.0 release of dplyr.
Patch release requested by CRAN maintainers, so package is up-to-date with latest version of tibble.
thicken preserves missing values in the datetime column and adds them to the added column. The missing values were placed on the wrong position. They were placed on NA position + nr of NAs earlier in the datetime variable, instead of the NA position. Only the first missing value was on the correct position. Bug reported by github user github user levi-nagy.thicken has gained the ties_to_earlier argument. Logical with FALSE as default value. By default when the rounding argument in thicken is set to “up” and the original observation is equal to a value in the higher interval variable, the observation is mapped to the next value in the new variable. (For example 2019-04-14 13:00:00 would be mapped to 2019-04-14 14:00:00 when rounding is “up” and interval is “hour”.) This can be undesired. When this argument is set to TRUE tied observations are mapped to their own value (thus to one value earlier in the new variable). For completeness this argument also works when rounding is “down”. Then, when original and new value are tied, the original value is mapped to the previous value of the higher level interval variable. (For example 2019-04-14 13:00:00 will be mapped to 2019-04-14 12:00:00 when the interval is hour). Feature request by github user stribstrib.
thicken has gained a drop argument. Logical with FALSE as default value. If TRUE the thickened datetime value is dropped from the data frame. Idea by Adam Stone.
An informative error is now thrown in pad, pad_cust, thicken, thicken_cust when a data frame does not have any rows. Requested by Julian During.
The functions thicken and thicken_cust no longer throw a warning when the input datetime variable is unsorted. The functions now silently return the a data frame with the same row order as the input data frame.
Error within padr for break_above error message is corrected. No longer prints the number of millions in millions. Bug found by Sharla Gelfand.
Patch releases with no impact for the user of the software.
thicken is sped up significantly:
get_interval no longer applied to assess interval validity (its slow on large variables because it converts a POSIX to character). Rather validity is now compared after thickening by checking if results differs from original. Makes function approximately four times faster.
get_interval is sped up significantly:
to convert date to character format is used, instead of as.character. For large vectors it 4 to 5 times faster.
span_date and span_time are new functions and they are wrappers around seq.Date and seq.POSIXt respectively. Because of their default settings (minimal specification of date and datetimes and interval inference) they require very little inputs for straightforward spanning.
The closest_weekday function is introduced. It finds the closest requested weekday around the start of a datetime variable. This function helps to find quickly the start_val for thicken when the interval is “week”.
Two new functions are introduced that help with visualising interval data.
center_interval shifts the datetime variable from either the beginning or the end of the interval, to the center of the interval. This will improve visualisations such as dot plots and bar plots, where the timestamp is still considered to be continuous.
format_interval takes the start_value of an interval and infers the end. It uses strftime on both the start value and the end value, to create a character vector that reflects the full interval.
The _cust suite allows for user-specified spanning to use in thickening and padding.
to create an asymmetric spanning, subset_span subsets a datetime vector to the desired date and time points. These are provided in a list.
span_around takes a datetime variable as input and spans a variable around it of a desired interval. This automates finding the min and the man of x manually, determining which values are needed to create a span of a desired interval, and do the actual spanning.
Both pad and thicken will no longer break when there are missing values in the datetime variable. Rows containing missing values will be retained in the returned data frame. In the case of thicken they will remain on the same position as the input data frame. The added column will have a missing value as well. For pad all the rows with missing values will be moved to the end of the dataframe, since there is no natural position for them in the order of padded rows.
When time variable has NULL as timezone, also posix_to_date used to break (related to #14). This made thicken break when the desired interval is “day” or higher. This is now fixed by don’t regarding the timezone.
get_interval now throws an informative error when the datetime variable has missing values (#33).
pad now throws an informative error when the datetime variable is used in the grouping (#38)
added “ByteCompile: true” to DESCRIPTION.
pad no longer throws a message when the interval is specified (#31).
span around hours and minutes now start at the current hour and minute. This to make span_around sensible.
The interval is no longer limited to be of a single unit, for each of the eight interval sizes. Every time span accepted by seq.Date or seq.POSIXt is now accepted. Since the original implementation was fully around single-unit-intervals, some default behavior had to change. Because of it, this version is not entirely backwards compatible with earlier versions of padr. The following functions are affected:
thicken: the interval argument now has to be specified. In earlier versions it was optional. When it was not specified, the added variable was one interval level higher than that of the input datetime variable. With the widening of the interval definition, there is not longer a natural step up.
get_interval: does no longer only retrieve the interval of a datetime variable, but also its unit (the step size). For instance, the following would have returned “day” in the past, but will now return “2 day”:
date_var <- as.Date(c(‘2017-01-01’, ‘2017-01-03’, ‘2017-01-05’)) get_interval(date_var)
pad: when the interval is not specified, get_interval is applied on the datetime variable. Its outcome might now be different. When get_interval returns a different interval than it used to, pad will do the padding at this different interval. Extending the above example, the have resulted in a data frame with two padded rows:x <- data.frame(date_var, y = 1:3)
Since the interval of date_var used be “day”, there were missing records for 2017-01-02 and 2017-01-04. These records were inserted, with missing values for y. However, now the interval of date_var is “2 day” and on this level there is no need for padding. To get the original result the interval argument should be specified with “day”.
padPad has been reimplemented
The function was slow when applied on many groups becuause it looped over them. Function has been reimplemented so it needs only one join to do the padding for all the groups simultaneously. dplyr functions are used for this new implementation, both for speed and coding clarity.
When applying pad to groups the interval is determined differently. It used to determine the interval separately for each of the groups. With the new interval definition this would often yield undesired results. Now, the interval on the full datetime variable, ignoring the groups. If the user would like to allow for differing intervals over the groups it is advised to use dplyr::do. See also the final example of pad.
dplyr::group_by
Besides its own argument for grouping, pad does now also accepts the grouping from dplyr. Making the following two results equal:
x %>% dplyr::group_by(z) %>% pad x %>% pad(group = ‘z’)
Moreover, both pad and thicken now maintain the grouping of the input data_frame. The return from both functions will have the exact same grouping.
break_above
This new argument to pad is a safety net for situations where the returned dataframe is much larger than the user anticipated. This would happen when the datetime variable is of a lower interval than the user thought it was. Before doing the actual padding, the function estimates the number of rows in the result. If these are above break_above the function will break.
thickenstart_val are now removed from the dataset (with a warning). They used to be all mapped to the start_val.They used to require specification of all the column names that had to filled. This is annoying when many columns had to filled. The functions no longer break when no variable names are specified, but they fill all columns in the data frame.
The new function pad_int does padding of an integer field. Its working is very similar to the general pad. The by argument must always be specified, since a data.frame would almost alway contain multiple numeric columns. Instead of the interval, one can specify the step size by which the integer increases.
Issue #13 When the end_val is specified in pad, it would mistakenly update the start_val with its value. This resulted in the return of only the last line of the padded data.frame, instead of the full padded data.frame.
Issue #14 When dt_var has NULL as timezone, to_posix (helper of round_thicken, which itself is a helper of thicken) used to break, and thereby thicken itself broke.
Issue #24 In pad with grouping, the function will no longer breaks if for one of the groups the start_val is behind its last observation, or the end_val is before its first observation. Group is omitted and warning is thrown. If all groups are omitted, function breaks with an informative error. The same goes when there is no grouping.
For determining the interval in pad the start_val and/or the end_val are taken into account, if specified. They are concatenated to the datetime variable before the interval is determined.
Both pad and thicken now throw informative errors when the start_val or end_val (pad only) are of the wrong class.
pad has gained a group parameter. This takes a character vector that indicates the column names within which group padding must be done. The returned data frame is complete for the grouping variable(s). Leaving no longer the doubt which record belongs to which group member, especially when start_val and / or end_val was specified.
Issue #8: pad does no longer break when datetime variable contains one value only. Returns x and a warning, if start_val and end_val are NULL and will do proper padding when one or both are specified.
Issue #9: when forgetting to specify at least one column, on which to apply the fill_ function, the fill_ function will now throw a meaningful error.
Issue #10: pad was broken with an error the interval was quarter, month, or year. This was done by check_start_end, even when neither a start_val nor an end_val was specified. It appeared that when concatenating POSIX vectors, as happened in the check_start_end function, the result is enforced to the timezone of the locale (including daylight savings time). This breaks the interval if the original vectors were not of this timezone. Workaround is implemented.