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brolgar (development version)

brolgar 1.0.1 “Conservation status: Least Concern” (2024-05-07)

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

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)

Minor Improvements

• Uses Testthat Third edition, and implements snapshot testing

brolgar 0.1.1 “Formerly known as the ‘native companion’” (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”

• 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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.