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brolgar (development version)

brolgar 1.0.0 “On the QLD coat of arms since 1977” (2023-02-06)

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 “Also known as the ‘Australian Crane’” (2021-08-06)

CRAN release: 2021-08-25

Minor Improvements

  • Uses Testthat Third edition, and implements snapshot testing

brolgar 0.1.1 “Formerly known as the ‘native companion’” (2021-05-27)

CRAN release: 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 “Antigone rubicunda”

CRAN release: 2020-12-16


  • 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)


  • Made brolgar integrate with tsibble.


  • 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 file to track changes to the package.