Nothing
butterfly::timeline()
function, which checks if a time series is continuous. The user can specify the difference between timesteps expected (#24).butterfly::timeline_group()
function, which groups a time series in distinct, but continuous groups (#24).butterflymess
dataset, which provides a "messy" version of butterflycount
for testing purposes (#33).waldo
parameters (such as tolerance) (#18).butterflymess
, to test function response to badly formatted datasets (#33).loupe()
feedback when there are no new rows (#34).README
(#32).loupe()
does (#36).all.equal()
, in addition to waldo::compare()
(#36).catch()
description, where it was mentioned the function uses inner_join()
, when actually it uses anti_join()
(#36).timeline()
description on how the expected lag units work for different periods of time (days, weeks) (#39).Initial release:
butterfly::loupe()
- examines in detail whether previous values have changed, and returns TRUE/FALSE for no change/change.
butterfly::catch()
- returns rows which contain previously changed values in a dataframe.butterfly::release()
- drops rows which contain previously changed values, and returns a dataframe containing new and unchanged rows.butterfly::create_object_list()
- returns a list of objects required by all of loupe()
, catch()
and release()
. Contains underlying functionality.butterflycount
- a list of monthly dataframes, which contain fictional butterfly counts for a given date.Any scripts or data that you put into this service are public.
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