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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.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.