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add_Date_col() is a new convenience function to add
a Date column to the dataset, optionally showing the weekday.
Datetime2Time() is a new convenience function that
is used in other functions that average over datetimes, which is often
more sensible over times.
reworked the README file to reflect some of the features
LightLogR has gained over time
summarize_numeric() now calculates
total_duration correctly, even when the prefix is
removed.
added the sample.data.irregular internal
dataset
removed LYS wearable sample file, due to package
size limitations
add_Time_col() replaces
create_Time_data(), also, the new column is called
Time by default instead of Time.data
extract_clusters() has the option to show the
cluster condition in the output with add.label = TRUE,
e.g., MEDI>500|d≥30min|i≤5min for clusters of melanopic
EDI larger than 500, at least 30 minutes long (d), allowing
interruptions of up to 5 minutes at a time (i).
add_clusters() now drops empty groups, which has led
to warnings before
Added many more unit tests - 888 and counting!
Removed a nasty bug in the internal functions that could lead to a shift in how dominant epochs are assigned to durations when groups are dropped due to singular observations
gapless_datetimes(): fixed a bug that prohibited the
use of durations. Had downstream effects on basically all gap
functions
extract_gaps() now warns correctly if there are
implicit gaps with a non-default epoch
import has the option to remove data before a
specified date. Default is not.before = 2001-01-01. Some
devices fall back to a time stamp in the year 2000 after the battery
drained completely. This makes the import problematic in terms of gap
searching.
extract_states() has the option not to group by the
extracted state.
extract_clusters() and extract_states()
do not drop empty groups, which is important for summaries.
extract_clusters() does it by default,
extract_states() does not.
summarize_numeric() has the option to show
zero-instances of groups. Helpful to make certain groups with zero
instances are not dropped, especially in a chain with
mean_daily()
mean_daily(): Automatic conversion to weekdays from
dates. Further it has the option to replace NA with zeros before
calculating mean daily values
SunriseThis is a huge update for LightLogR, bringing many new
features and twenty-two new functions
spectral_reconstruction():reconstruct a spectral
power distribution (SPD) from sensor channels that provide (normalized)
counts, and a calibration matrix. Examples for such devices include the
the ActLumus from Condor instruments, or the VEET from Meta
Reality labs (after normalization of counts, e.g., through
normalize_counts())
alphaopic.action.spectra: New dataset containing
alphaopic action spectra (CIE S026) plus the photopic action spectrum in
1-nm wavelength steps.
spectral_integration(): integrate over all or just
parts of the spectrum, including the option to weigh the spectrum with
an action spectrum (e.g., from
alphaopic.action.spectra)
remove_partial_data(): remove groups with a below
than user-specified amount of data
extract_gaps(): provides a start and end times, as
well as durations for all gaps in the dataset.
has_gaps(), has_irregulars(); provide a
logical feedback on whether a dataset has (implicit) gaps or irregular
data.
gap_table() provides a comprehensive summary of
available and missing data.
durations(): calculate the groupwise duration of a
dataset, based on datapoints, the dominant interval, and missing
data
mean_daily() and mean_daily_metric():
give a three-row summary of weekday, weekend, and mean daily (numeric)
values. mean_daily_metric() skips the prior metric
calculation for duration-based metrics, and directly calculates the mean
daily value.
extract_clusters(), add_clusters():
find clusters of a user-specified condition and either summarize them or
add them to a dataset.
extract_states(), add_states():
provides a summary of every state in the dataset or add them to a
dataset.
extract_metric(): add a calculation to extracted
data, such as from extract_state() or
extract_clusters().
summarize_numeric()/summarise_numeric():
calculate means across numeric values, ideal to summarize results from
extract_state(), extract_gaps(), or
extract_clusters.
Brown_cut(): divide light exposure variables into
sections ≤1lx, ≤10lx, and ≥250lx according to Brown et al. 2022
log_zero_inflated() and `exp_zero_inflated(): apply
or reverse a logarithmic transformation after adding a small value to a
vector so as to provide zero values in logarithmic transformation, which
is especially important for light exposure.
gg_gaps(): visualize gaps and shows instances of
irregular data.
gg_state(): is an addon-function to
gg_day() or gg_days(), which adds a state or
cluster indicator to the plot
gg_heatmap(): visualize a condensed version of time
series patterns, optionally as double plots.
Import support for the Clouclip device.
number_states() added the option to just output a
count number without the original state
gg_days(): jco_color = TRUE is now the
default
import_Dataset() no longer changes a pre-existing
Id column (if it is not called Id). The
function is also more informative for the daylight savings time handling
in files with more than one Id.
gg_photoperiod() does no longer throw an error when
the main plots y.axis is not based on a MEDI
column.
gapless_Datetimes() now ignores groups with only a
single measurement, instead of throwing an error. This function is the
basis for all calculations regarding gaps.
changed the behavior of all metric functions that calculate an in-between value (such as the duration of light between 250 and 1000 lux). Up until now, the function would use inclusive bounds on both sides, i.e. the value of 250 lux and 1000 lux would be included in the calculation. This is now changed to right exclusive bounds, i.e. the value of 250 lux will still be included in the calculation, whereas 1000 lux will not. While of little practical difference in a realistic dataset (where exact values matching the threshold are likely not present), it is relevant when calculating, e.g., the time spent in various levels of light or any other variable. The sum of those times should always add up to the total time. With inclusive bounds on both sides, the sum could theoretically be larger, with right exclusive bounds it cannot.
Metrics intradaily_variability() and
interdaily_stability() now use the population variance
(divide by N), instead of the sample variance (divide by N-1). The
legacy behavior can still be accessed by setting the argument
use.samplevar = TRUE. #55
Metric IS now correctly uses the overall mean in
relation to the variance, instead of the
mean of hourly averages across days. #56
passed 300 unit tests for LightLogR 🎉
normalize_counts() was added as a low-level helper
function and the accompanying dataset gain.ratio.tables to
facilitate calculating normalized sensor values when comparing across
different sensors, e.g. to assess daylighting conditions based on UV,
IR, and photopic sensing ranges. See documentation for more
infos.
Update to the import function of GENEActiv devices,
based on input from the author of the GGIR package. The timezone
tz argument in LightLogR now is just set on the timestamp
provided by the GGIR export, instead of shifting the datetime. This
requires the correct setting of
desiredtz/configtz arguments in GGIR during
preprocessing.
refinement and cross-referencing in tutorials on photoperiod
fixing a bug in the photoperiod family of functions when using a timezone with a large offset to the coordinates where photoperiod is calculated and crosses a date.
added a suite of functions to deal with photoperiod:
photoperiod() and solar_noon() to calculate
dusk, dawn, and noon times of the day.
extract_photoperiod() and add_photoperiod()
utilize datasets imported with LightLogR to calculate and
deal with photoperiods in the context of your own datasets.
gg_photoperiod() brings this functionality to the
visualization tools of LightLogR in an easy and powerful
way.
added the function number_states() that relabels
states based on their non-consecutive appearance. This is especially
useful when labelling photoperiod states, as the function will allow for
an easy classifier of “day 1”, “day 2”, …, and “night 1”, “night 2”, ….
These can be used to, e.g., calculate metrics for individual photoperiod
sections throughout the observed time frame.
added a tutorial on the new functions Photoperiod.
This also details how to calculate metrics based on photoperiod
(#39).
implemented further changes in the paper.md based on the JOSS Reviews
implemented changes based on the JOSS Reviews
added a Code of Conduct and a Contributing file for the project
updated the license to MIT: LightLogR is now permissively licensed
import functions will now give a warning message
about identical observations in the provided data files, stop the import
process and return a tibble with the duplicate rows. Through the
remove_duplicates parameter, the user can decide to
automatically remove these duplicates during import. Note:
identical observations refers to identical rows when disregarding the
filename.
added support for OcuWEAR devices
added support for MotionWatch 8 devices #32
added support for LIMO devices
added support for GENEActiv devices, when data was preprocessed
with the GGIR
package. The function import$GENEActiv_GGIR() takes the
GGIR output and imports it with LightLogR naming schemes.
#27
release on CRAN!
changed the supported.devices list to a function
supported_devices() instead, so the documentation
automatically updates with the list of supported devices. Similarly,
ll_import_expr is now
ll_import_expr().
added support for the Meta VEET device for visual
experience measurements
added support for the Kronowise device
added support for the MPI melanopiQ Circadian Eye
(Prototype)
rewrote the import function for Actiwatch_Spectrum,
as the sample file the original was based off, had specific formatting
to German standards. Now, the German version can still be called through
Actiwatch_Spectrum_de, wheras the main function refers to
the english/international format.
updated the landing page for the website with a list of supported devices and a table of metrics
small changes to documentation
Changes to the tutorial articles on the website
Integration of a community survey on the website and Github Readme.
bright_dark_period() now maintains the date when
looping the data.
Added articles on Import & Cleaning,
Metrics, and Visualizations to the
website.
Added the option for more print rows of observation intervals
during import.
Added the option to set a length for the dataset starting from
the end in filter_Datetime() and family.
Added the function aggregate_Date() to aggregate
long datasets to one day per group.
New function gg_doubleplot() for … well, double
plots.
added import functions for nanoLambdaand
LightWatcher devices
new Logo!
fixed bug in interval2state() that would dismiss the
first state if it starts before the actual data
fixed a bug in interval2state() that would add other
columns then the State column present in the interval
dataset to the output dataset, but leave them empty. Added an example
that shows how to add multiple columns to the output dataset
correctly.
in aggregate_Datetime(), added the option to set the
dominant.epoch, i.e., the most common interval, as the
unit parameter, to effectively deal with irregular
data.
Added the functions dst_change_summary() and
dst_change_handler() to detect and deal with Daylight
Savings. The functionality is also integrated into the import functions,
so that a user can automatically apply it during the import
process.
Added Steffen Hartmeyer as a collaborator, who
added a number of light metrics from the lightdosimetry
package.
Added the import_adjustment() function for more
flexibility when importing light logger data that does not conform to
the standard format. This goes hand in hand with the
ll_import_expr list that contains specific expressions for
all supported devices.
lots of bug fixes and improvements
Bugfix for LiDo import
Added import support for new devices: LiDo,
DeLux, and Speccy
Removed minor inconsistencies in naming conventions. Also, all imported columns will have syntactic naming now
Added an option to all gap functions, to extend the
gapless Datetime range to full days.
Exports the up to now internal function
count_difftime() that is the basis for
dominant_epoch(). But whereas the latter gets only the most
common epoch, count_difftime() returns a table with the
counts of all epochs. This is useful in conjunction with
gap_finder(), to check the distribution of data
intervals.
Added the gg_days() function to visualize multiple
days of data in a single plot. Alongside come two helper functions,
Datetime_limits() and Datetime_breaks(), to
set the limits and breaks of the x-axis.
Added the filter_Datetime_multiple() function to
filter for multiple Datetime ranges depending on certain conditions,
e.g. different filter cutoffs for different participants. It wraps
around filter_Datetime() or
filter_Date().
Reworked the internals of the light logger data import functions.
They now use a more straightforward function factory approach. For users
the only visible change it that device specific functions now have the
form import$device() instead of the old
import.device().
Added the symlog_trans() function from a post on stack overflow.
This function leads to a better visualization of light logger data, as a
logarithmic transformation is necessary, but values of 0 are common. The
function was integrated as a default for gg_day() and will
likely be the basis of upcoming visualization functions.
Added the aggregate_Datetime() function to aggregate
data to a given time interval.
Added the gg_overview() function to get a sense for
the timeframe of measurement data.
Added the family of regularize functions to find and
deal with implicit missing data. These functions include
dominant_epoch(), gapless_Datetimes(),
gap_handler(), and gap_finder().
A ton of updates to documentation, unit tests, and bug fixes.
Added Unit tests and documentation for all new functions.
To filter_Datetime() and filter_Date()
added the option to filter for group specific dates.
Added the family of functions around States and
Reference to import, process, and add states to light
logger data, like sleep/wake times, wear times, or other data. This
family includes import_Statechanges(),
sc2interval(), ìnterval2state(),
data2reference(), sleep_int2Brown(),
Brown_check(), Brown_rec(), and
Brown2reference().
Added the Article/Vignette “What´s in a Day” to demonstrate the LightLogR workflow.
Added the convenience function create_Timedata() to
create a Time-of-Day column in datasets.
Added the family of filter_Datetime(),
filter_Date() and filter_Time() functions to
easily filter datasets.
Added unit tests for the first functions.
Added several helper functions to work with states like sleep or wear times.
Added an automatic ID creation at import and streamlined the
import functions.
Added the function join_datasets to combine imported
datasets with sensible constraints.
gg_day():Added major grid marks for the y-axis.
Added a message when using start or end dates to
make it clear, that only the Date portion of the input will be
used.
Changed the behavior, when there is already a Day.data column present in the data. It will only create a new column if none is present, otherwise it will use the existing column for faceting (after factorization)
Added the option to create an interactive plot by
feeding the plot to the [plotly] package.
NEWS.md file to track changes to the
package.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.