The goal of butterfly is to aid in the verification of continually updating timeseries data, where we expect new values over time, but want to ensure previous data remains unchanged, and timesteps remain continuous.
Unnoticed changes in previous data could have unintended consequences, such as invalidating DOIs, or altering future predictions if used as input in forecasting models.
Other unnoticed changes could include a jump in time or measurement frequency, due to instrument failure or software updates.
This package provides functionality that can be used as part of a data pipeline, to check and flag changes to previous data to prevent changes going unnoticed.
You can provide butterfly with a timeseries dataset to check for
continuity, with timeline(), and if it is not fully
continuous as expected, split it into continuous chunks with
timeline_group(). To check for changes to previous data,
you can provide two versions of the same dataset, and
loupe() will check if there are changes to matching rows,
and tell you which rows are new. You can use catch() and
release() to extract or remove rows with changes. Full
examples of functionality are provided below.
Data
This packages includes a small dummy dataset,
butterflycount, which contains a list of monthly dataframes
of butterfly counts for a given date.
library(butterfly)
butterflycount
#> $january
#> time count
#> 1 2024-01-01 22
#> 2 2023-12-01 55
#> 3 2023-11-01 11
#>
#> $february
#> time count
#> 1 2024-02-01 17
#> 2 2024-01-01 22
#> 3 2023-12-01 55
#> 4 2023-11-01 11
#>
#> $march
#> time count
#> 1 2024-03-01 23
#> 2 2024-02-01 17
#> 3 2024-01-01 22
#> 4 2023-12-01 55
#> 5 2023-11-01 18
#>
#> $april
#> time value species
#> 1 2024-04-01 12 Admiral
#> 2 2024-03-01 23 Admiral
#> 3 2024-02-01 NA Admiral
#> 4 2024-01-01 22 Admiral
#> 5 2023-12-01 55 Admiral
#> 6 2023-11-01 18 Admiral
#>
#> $may
#> time value species
#> 1 2024-05-01 70 Admiral
#> 2 2024-04-01 12 Admiral
#> 3 2024-03-01 23 Admiral
#> 4 2024-01-01 22 Admiral
#> 5 2024-12-01 55 Admiral
#> 6 2024-11-01 18 AdmiralThis dataset is entirely fictional, and merely included to aid in demonstrating butterfly’s functionality.
Another dummy dataset, forestprecipitation, also
contains a list of monthly dataframes, but for fictional rainfall data.
This dataset is intended to illustrate an instance of instrument failure
leading to timesteps being recorded out of sync.
forestprecipitation
#> $january
#> time rainfall_mm
#> 1 2024-01-01 0.0
#> 2 2024-01-02 2.6
#> 3 2024-01-03 0.0
#> 4 2024-01-04 0.0
#> 5 2024-01-05 3.7
#> 6 2024-01-06 0.8
#>
#> $february
#> time rainfall_mm
#> 1 2024-02-01 00:00:00 1.1
#> 2 2024-02-02 00:00:00 0.0
#> 3 2024-02-03 00:00:00 1.4
#> 4 2024-02-04 00:00:00 2.2
#> 5 1969-12-31 23:00:00 3.4
#> 6 1970-01-01 23:00:00 0.6Examining datasets: loupe()
We can use butterfly::loupe() to examine in detail
whether previous values have changed.
butterfly::loupe(
butterflycount$february,
butterflycount$january,
datetime_variable = "time"
)
#> The following rows are new in 'df_current':
#> time count
#> 1 2024-02-01 17
#> ✔ And there are no differences with previous data.
#> [1] TRUE
butterfly::loupe(
butterflycount$march,
butterflycount$february,
datetime_variable = "time"
)
#> The following rows are new in 'df_current':
#> time count
#> 1 2024-03-01 23
#>
#> ℹ The following values have changes from the previous data.
#> old vs new
#> count
#> old[1, ] 17
#> old[2, ] 22
#> old[3, ] 55
#> - old[4, ] 18
#> + new[4, ] 11
#>
#> `old$count`: 17.0 22.0 55.0 18.0
#> `new$count`: 17.0 22.0 55.0 11.0
#> [1] FALSEbutterfly::loupe() uses dplyr::semi_join()
to match the new and old objects using a common unique identifier, which
in a timeseries will be the timestep. waldo::compare() is
then used to compare these and provide a detailed report of the
differences.
In addition to a report, loupe() also returns
TRUE if there are no differences and FALSE
when there are differences. This is especially useful when using
butterfly in a pipeline that runs in a shell environment, a check for
differences to fail gracefully.
butterfly follows the waldo philosophy of
erring on the side of providing too much information, rather than too
little. It will give a detailed feedback message on the status between
two objects.
Additional arguments from waldo::compare()
You have the flexibility to pass further arguments accepted by
waldo::compare()to any of loupe(),
catch() or release().
One such argument is tolerance. If we add a tolerance of
2 to the previous example, no differences should be returned:
butterfly::loupe(
butterflycount$march,
butterflycount$february,
datetime_variable = "time",
tolerance = 2 # <- setting a tolerance of 2
)
#> The following rows are new in 'df_current':
#> time count
#> 1 2024-03-01 23
#> ✔ And there are no differences with previous data.
#> [1] TRUESee ?waldo::compare() for the full list of
arguments.
Extracting unexpected changes: catch()
You might want to return changed rows as a dataframe, instead of
returning TRUE/FALSE. For this
butterfly::catch()is provided.
butterfly::catch() only returns rows which have
changed from the previous version. It will not return
new rows.
df_caught <- butterfly::catch(
butterflycount$march,
butterflycount$february,
datetime_variable = "time"
)
#> The following rows are new in 'df_current':
#> time count
#> 1 2024-03-01 23
#>
#> ℹ The following values have changes from the previous data.
#> old vs new
#> count
#> old[1, ] 17
#> old[2, ] 22
#> old[3, ] 55
#> - old[4, ] 18
#> + new[4, ] 11
#>
#> `old$count`: 17.0 22.0 55.0 18.0
#> `new$count`: 17.0 22.0 55.0 11.0
#>
#> ℹ Only these rows are returned.
df_caught
#> time count
#> 1 2023-11-01 18Dropping unexpected changes: release()
Conversely, butterfly::release() drops all rows which
have changed from the previous version. Note it retains new rows, as
these were expected.
df_released <- butterfly::release(
butterflycount$march,
butterflycount$february,
datetime_variable = "time"
)
#> The following rows are new in 'df_current':
#> time count
#> 1 2024-03-01 23
#>
#> ℹ The following values have changes from the previous data.
#> old vs new
#> count
#> old[1, ] 17
#> old[2, ] 22
#> old[3, ] 55
#> - old[4, ] 18
#> + new[4, ] 11
#>
#> `old$count`: 17.0 22.0 55.0 18.0
#> `new$count`: 17.0 22.0 55.0 11.0
#>
#> ℹ These will be dropped, but new rows are included.
df_released
#> time count
#> 1 2024-03-01 23
#> 2 2024-02-01 17
#> 3 2024-01-01 22
#> 4 2023-12-01 55However, you do have the option to exclude new rows as well with the
argument include_new set to FALSE.
df_release_without_new <- butterfly::release(
butterflycount$march,
butterflycount$february,
datetime_variable = "time",
include_new = FALSE
)
#> The following rows are new in 'df_current':
#> time count
#> 1 2024-03-01 23
#>
#> ℹ The following values have changes from the previous data.
#> old vs new
#> count
#> old[1, ] 17
#> old[2, ] 22
#> old[3, ] 55
#> - old[4, ] 18
#> + new[4, ] 11
#>
#> `old$count`: 17.0 22.0 55.0 18.0
#> `new$count`: 17.0 22.0 55.0 11.0
#>
#> ℹ These will be dropped, along with new rows.
df_release_without_new
#> time count
#> 1 2024-02-01 17
#> 2 2024-01-01 22
#> 3 2023-12-01 55Checking for continuity: timeline()
To check if a timeseries is continuous, timeline() and
timeline_group() are provided. Even if a timeseries does
not contain obvious gaps, this does not automatically mean it is also
continuous.
Measuring instruments can have different behaviours when they fail. For example, during power failure an internal clock could reset to “1970-01-01”, or the manufacturing date (e.g. “2021-01-01”, this is common behaviour for Raspberry Pi’s). This leads to unpredictable ways of checking if a dataset is continuous.
# A rain gauge which measures precipitation every day
butterfly::forestprecipitation$january
#> time rainfall_mm
#> 1 2024-01-01 0.0
#> 2 2024-01-02 2.6
#> 3 2024-01-03 0.0
#> 4 2024-01-04 0.0
#> 5 2024-01-05 3.7
#> 6 2024-01-06 0.8
# In February there is a power failure in the instrument
butterfly::forestprecipitation$february
#> time rainfall_mm
#> 1 2024-02-01 00:00:00 1.1
#> 2 2024-02-02 00:00:00 0.0
#> 3 2024-02-03 00:00:00 1.4
#> 4 2024-02-04 00:00:00 2.2
#> 5 1969-12-31 23:00:00 3.4
#> 6 1970-01-01 23:00:00 0.6To check if a timeseries is continuous:
butterfly::timeline(
forestprecipitation$january,
datetime_variable = "time",
expected_lag = 1
)
#> ✔ There are no time lags which are greater than the expected lag: 1 days. By this measure, the timeseries is continuous.
#> [1] TRUEAs expected January is a continuous dataset, where there is no more than a difference of 1 day between timesteps.
However, in February our imaginary rain gauge’s onboard computer had a failure.
The timestamp was reset to 1970-01-01:
forestprecipitation$february
#> time rainfall_mm
#> 1 2024-02-01 00:00:00 1.1
#> 2 2024-02-02 00:00:00 0.0
#> 3 2024-02-03 00:00:00 1.4
#> 4 2024-02-04 00:00:00 2.2
#> 5 1969-12-31 23:00:00 3.4
#> 6 1970-01-01 23:00:00 0.6
butterfly::timeline(
forestprecipitation$february,
datetime_variable = "time",
expected_lag = 1
)
#> ℹ There are time lags which are greater than the expected lag: 1 days. This indicates the timeseries is not continuous. There are 2 distinct continuous sequences. Use `timeline_group()` to extract.
#> [1] FALSEGrouping distinct continuous sequences:
timeline_group()
If we wanted to group chunks of our timeseries that are distinct, or
broken up in some way, but still continuous, we can use
timeline_group():
butterfly::timeline_group(
forestprecipitation$february,
datetime_variable = "time",
expected_lag = 1
)
#> time rainfall_mm timelag timeline_group
#> 1 2024-02-01 00:00:00 1.1 NA days 1
#> 2 2024-02-02 00:00:00 0.0 1.00 days 1
#> 3 2024-02-03 00:00:00 1.4 1.00 days 1
#> 4 2024-02-04 00:00:00 2.2 1.00 days 1
#> 5 1969-12-31 23:00:00 3.4 -19757.04 days 2
#> 6 1970-01-01 23:00:00 0.6 1.00 days 2We now have groups 1 & 2, which are both continuous sets of data, but there is no continuity between them.
Using butterfly in a data processing pipeline
If you would like to know more about using butterfly in
an operational data processing pipeline, please refer to the article on
using
butterfly in an operational pipeline.
A note on controlling verbosity
Although verbosity is mostly the purpose if this package,
should you wish to silence messages and warnings, you
can do so with options(rlib_message_verbosity = "quiet")
and options (rlib_warning_verbosity = "quiet").
Rationale
There are a lot of other data comparison and QA/QC packages out there, why butterfly?
Unexpected changes in models
This package was originally developed to deal with ERA5’s initial release data, ERA5T. ERA5T data for a month is overwritten with the final ERA5 data two months after the month in question.
Usually ERA5 and ERA5T are identical, but occasionally an issue with input data can (for example for 09/21 - 12/21, and 07/24) force a recalculation, meaning previously published data differs from the final product.
When publishing ERA5-derived datasets, and minting it with a DOI, it is possible to continuously append without invalidating that DOI. However, recalculation would overwrite previously published data, thereby forcing a new publication and DOI to be minted.
We use the functionality in this package in an automated data processing pipeline to detect changes, stop data transfer and notify the user.
Unexpected changes in data acquisition
Measuring instruments can have different behaviours when they have a power failure. For example, during power failure an internal clock could reset to “1970-01-01”, or the manufacturing date (say, “2021-01-01”). If we are automatically ingesting and processing this data, it would be great to get a head’s up that a timeseries is no longer continuous in the way we expect it to be. This could have consequences for any calculation happening downstream.
To prevent writing different ways of checking for this depending on
the instrument, we wrote butterfly::timeline().
Variable measurement frequencies
In other cases, a non-continuous timeseries is intentional, for example when there is temporal variability in the measurements taken depending on events. At BAS, we collect data from a penguin weighbridge on Bird Island, South Georgia. This weighbridge measure weight on two different load cells (scales), one on the colony side and one on the ocean side, to determine penguin weight and the direction they came from. The idea is that we may be able to derive information on their diet, as they return from the ocean to the colony.
You can read about this work in more detail in Afanasyev et al. (2015), but the important point here is that the weighbridge does not collect continuous measurements. In a remote, off-grid location this would drain batteries far too quickly.
Therefore, when no weight is detected on the load cells it only samples at 1hz, but as soon as any change in weight is detected it will start collecting data at 100hz. This is of course intentional, to reduce the sheer volume of data we need to process, but also has another benefit in isolating (or attempting to) individual crossings.
The individual crossings are the most valuables pieces of data, as these allow us to deduce information on weight, direction and ultimately, diet.
In this case separating distinct, but continuous segments of data is
required. This is the reasoning behind timeline_group().
This function allows us to split our timeseries in groups of individual
crossings.