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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.
Health stats visible at Monitor.