Package index • LightLogR

Import

Wearable light logger data can be imported from a variety of sources, i.e., exports from measurement devices or online databases. This section also includes functions to import auxiliary data, such as sleep/wake data. The family of import functions works on a variety of device-specific files through import_*().

import_Dataset() import: Import a light logger dataset or related data

import_Statechanges(): Import data that contain Datetimes of Statechanges

import_adjustment(): Adjust device imports or make your own

Insight

Functions to gain more insight into the data. Functions in this section will not return a version of the input dataset, but rather information based on it.

count_difftime(): Counts the Time differences (epochs) per group (in a grouped dataset)

dominant_epoch(): Determine the dominant epoch/interval of a dataset

durations(): Calculate duration of data in each group

gapless_Datetimes(): Create a gapless sequence of Datetimes

gap_finder(): Check for and output gaps in a dataset

gap_table(): Tabular summary of data and gaps in all groups

dst_change_summary(): Get a summary of groups where a daylight saving time change occurs.

photoperiod() extract_photoperiod() add_photoperiod() solar_noon(): Calculate photoperiod and boundary times

extract_clusters() add_clusters(): Find and extract clusters from a dataset

extract_states(): Extract summaries of states

extract_metric(): Add metrics to extracted sSummary

extract_gaps(): Extract gap episodes from the data

has_gaps(): Does a dataset have implicit gaps

has_irregulars(): Does a dataset have irregular data

Process

Functions to process light logger data, e.g., to validate and clean data, to filter, cut or aggreagate data, or to join datasets. All of these functions will return a version of the input dataset.

gap_handler(): Fill implicit gaps in a light logger dataset

remove_partial_data(): Remove groups that have too few data points

cut_Datetime(): Create Datetime bins for visualization and calculation

aggregate_Datetime(): Aggregate Datetime data

aggregate_Date(): Aggregate dates to a single day

create_Timedata(): Create a Time-of-Day column in the dataset

filter_Datetime() filter_Date(): Filter Datetimes in a dataset.

filter_Datetime_multiple(): Filter multiple times based on a list of arguments.

filter_Time(): Filter Times in a dataset.

join_datasets(): Join similar Datasets

dst_change_handler(): Handle jumps in Daylight Savings (DST) that are missing in the data

Expand

Expanding light logger data through auxiliary data and/or reference data allows for a more comprehensive analysis.

data2reference(): Create reference data from other data

sc2interval(): Statechange (sc) Timestamps to Intervals

interval2state(): Adds a state column to a dataset from interval data

photoperiod() extract_photoperiod() add_photoperiod() solar_noon(): Calculate photoperiod and boundary times

extract_clusters() add_clusters(): Find and extract clusters from a dataset

add_states(): Add states to a dataset based on groups and start/end times

number_states(): Number non-consecutive state occurrences

sleep_int2Brown(): Recode Sleep/Wake intervals to Brown state intervals

Brown_check(): Check whether a value is within the recommended illuminance/MEDI levels by Brown et al. (2022)

Brown_rec(): Set the recommended illuminance/MEDI levels by Brown et al. (2022)

Brown2reference(): Add Brown et al. (2022) reference illuminance to a dataset

Brown_cut(): Create a state column that cuts light levels into sections by Brown et al. (2022)

spectral_reconstruction(): Reconstruct spectral irradiance from sensor counts

Visualize

Functions to visualize light logger data, e.g., to plot light exposure or to plot sleep/wake data.

gg_overview(): Plot an overview of dataset intervals with implicit missing data

gg_day(): Create a simple Time-of-Day plot of light logger data, faceted by Date

gg_days(): Create a simple datetime plot of light logger data, faceted by group

gg_heatmap(): Plot a heatmap across days and times of day

gg_doubleplot(): Double Plots

gg_photoperiod(): Add photoperiods to gg_day() or gg_days() plots

gg_state(): Add states to gg_day() or gg_days() plots

gg_gaps(): Visualize gaps and irregular data

Metrics

Functions to calculate light exposure metrics.

barroso_lighting_metrics(): Circadian lighting metrics from Barroso et al. (2014)

bright_dark_period(): Brightest or darkest continuous period

centroidLE(): Centroid of light exposure

disparity_index(): Disparity index

duration_above_threshold(): Duration above/below threshold or within threshold range

exponential_moving_average(): Exponential moving average filter (EMA)

frequency_crossing_threshold(): Frequency of crossing light threshold

intradaily_variability(): Intradaily variability (IV)

interdaily_stability(): Interdaily stability (IS)

midpointCE(): Midpoint of cumulative light exposure.

nvRC(): Non-visual circadian response

nvRC_circadianDisturbance() nvRC_circadianBias() nvRC_relativeAmplitudeError(): Performance metrics for circadian response

nvRD(): Non-visual direct response

nvRD_cumulative_response(): Cumulative non-visual direct response

period_above_threshold(): Length of longest continuous period above/below threshold

pulses_above_threshold(): Pulses above threshold

threshold_for_duration(): Find threshold for given duration

timing_above_threshold(): Mean/first/last timing above/below threshold.

spectral_integration(): Integrate spectral irradiance with optional weighting

Photoperiod

Functions that deal with photoperiod aspects of a dataset

photoperiod() extract_photoperiod() add_photoperiod() solar_noon(): Calculate photoperiod and boundary times

gg_photoperiod(): Add photoperiods to gg_day() or gg_days() plots

Helpers

Helper functions that are used in the other sections.

mean_daily(): Calculate mean daily metrics from daily summary

mean_daily_metric(): Calculate mean daily metrics from Time Series

symlog_trans(): Scale positive and negative values on a log scale

reverse2_trans(): Create a reverse transformation function specifically for date scales

Datetime_breaks(): Create a (shifted) sequence of Datetimes for axis breaks

Datetime_limits(): Find or set sensible limits for Datetime axis

photoperiod() extract_photoperiod() add_photoperiod() solar_noon(): Calculate photoperiod and boundary times

spectral_reconstruction(): Reconstruct spectral irradiance from sensor counts

normalize_counts(): Normalize counts between sensor outputs

summarize_numeric() summarise_numeric(): Summarize numeric columns in dataframes to means

log_zero_inflated() exp_zero_inflated(): Add a defined number to a numeric and log transform it

Datasets

Datasets that are used in LightLogR and in examples.

sample.data.environment: Sample of wearable data combined with environmental data

alphaopic.action.spectra: Alphaopic (+ photopic) action spectra

gain.ratio.tables: Gain / Gain-ratio tables to normalize counts

supported_devices(): Get all the supported devices in LightLogR

ll_import_expr(): Get the import expression for a device