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.