NEWS

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brolgar 1.0.1 (2024-05-10)

• This is a patch release that fixed a couple of small issues that got it removed from CRAN

brolgar 1.0.0 (2023-02-07)

This is a major release to signify that this version is associated with a publication (woo!) for this paper in the R Journal. However, this release only represents minor changes, summarised below:

• small fix for dev dplyr 113
• minor updates to GH actions and website.

brolgar 0.1.2 (2021-08-25)

Minor Improvements

• Uses Testthat Third edition, and implements snapshot testing

brolgar 0.1.1 (2021-05-27)

Bug fixes

• Address bug with not creating equal strata
• Address warnings when using b_diff_summary, which now returns NA if there is only one observation, as we can't take the difference of one observation, and a difference of 0 in these cases would be misleading.

brolgar 0.1.0 (2020-12-16)

• fix warning bug in keys_near related to factors
• Add feat_diff_summary() functions to help summarise diff(). Useful for exploring the time gaps in the index. (#100)
• sample functions now work with multiple keys (#85, #89) (Thanks to @earowang and @deanmarchiori for their help with this.)
• facet_sample() now has a default of 3 per plot
• resolve features(data ,.key, n_obs) error (#71)
• For near_quantile(), the tol argument now defaults to 0.01.
• provide an S3 generic for tbl_ts objects for keys_near() - #76
• Add new dataset, pisa containing a short summary of the PISA dataset from https://github.com/ropenscilabs/learningtower for three (of 99) countries
• add helper functions index_regular() and index_summary() to help identify index variables

brolgar 0.0.4.9000

• remove feasts from dependencies as the functions required in brolgar are actually in fabletools.
• add nearest_lgl and nearest_qt_lgl
• Gave more verbose names to the wages_ts data.
• renamed sample_n_obs() to sample_n_keys() and sample_frac_keys()
• renamed add_k_groups() to stratify_keys()
• removed many of the l_<summary> functions in favour of the features approach.
• rename l_summarise_fivenum to l_summarise, and have an option to pass a list of functions.
• rename l_n_obs() to n_key_obs()
• rename l_slope() to key_slope()
• added monotonic summaries and feat_monotonic
• rename l_summarise() to keys_near()
• make monotonic functions return FALSE if length == 1.
• add monotonic function, which returns TRUE if increasing or decreasing, and false otherwise.
• re export as_tsibble() and n_keys() from `tsibble
• Data world_heights gains a continent column
• Implement facet_strata() to create a random group of size n_strata to put the data into (#32). Add support for along, and fun.
• Implement facet_sample() to create facetted plots with a set number of keys inside each facet. (#32).
• add_ functions now return a tsibble() (#49).
• Fixed bug where stratify_keys() didn't assign an equal number of keys per strata (#55)
• Update wages_ts dataset to now just be wages data, and remove previous tibble() version of wages (#39).
• Add top_n argument to keys_near to provide control over the number of observations near a stat that are returned.
• change world_heights to heights.
• remove function n_key_obs() in favour of using n_obs() (#62)
• remove function filter_n_obs() in favour of cleaner workflow with add_n_obs() (#63)

brolgar 0.0.1.9000

• Made brolgar integrate with tsibble.

brolgar 0.0.0.9990

• Added the world_heights dataset, which contains average male height in centimetres for many countries. #28
• created near_ family of functions to find values near to a quantile or percentile. So far there are near_quantile(), near_middle(), and near_between() (#11).
  • near_quantile() Specify some quantile and then find those values around it (within some specified tolerance).
  • near_middle() Specify some middle percentile value and find values within given percentiles.
  • near_between() Extract percentile values from a given percentile to another percentile.
• Create add_k_groups() (#20) to randomly split the data into groups to explore the data.
• Add sample_n_obs() and sample_frac_obs() (#19) to select a random group of ids.
• Add filter_n_obs() to filter the data by the number of observations #15
• Remove unnecessary use of var, in l_n_obs(), since it only needs information on the id. Also gets a nice 5x speedup with simpler code
• calculate all longnostics (#4)
• use the word longnostic instead of lognostic (#9)
• l_slope now returns l_intercept and l_slope instead of intercept and slope.
• l_slope now takes bare variable names
• Renamed l_d1 to l_diff and added a lag argument. This makes l_diff more flexible and the function more clearly describes its purpose.
• Rename l_length to l_n_obs to more clearly indicate that this counts the number of observations.
• Create longnostic function to create longnostic functions to package up reproduced code inside the l_ functions.
• Added a NEWS.md file to track changes to the package.