# Process Data from Wearable Light Loggers and Optical Radiation Dosimeters

## Documentation for package ‘LightLogR’ version 0.9.2

- DESCRIPTION file.
- Package NEWS.

## Help Pages

add_clusters | Find and extract clusters from a dataset
add_Date_col | Create a Date column in the dataset
add_photoperiod | Calculate photoperiod and boundary times
add_states | Add states to a dataset based on groups and start/end times
add_Time_col | Create a Time-of-Day column in the dataset
aggregate_Date | Aggregate dates to a single day
aggregate_Datetime | Aggregate Datetime data
alphaopic.action.spectra | Alphaopic (+ photopic) action spectra
barroso_lighting_metrics | Circadian lighting metrics from Barroso et al. (2014)
bright_dark_period | Brightest or darkest continuous period
Brown2reference | Add Brown et al. (2022) reference illuminance to a dataset
Brown_check | Check whether a value is within the recommended illuminance/MEDI levels by Brown et al. (2022)
Brown_cut | Create a state column that cuts light levels into sections by Brown et al. (2022)
Brown_rec | Set the recommended illuminance/MEDI levels by Brown et al. (2022)
centroidLE | Centroid of light exposure
count_difftime | Counts the Time differences (epochs) per group (in a grouped dataset)
create_Timedata | create_Timedata
cut_Datetime | Create Datetime bins for visualization and calculation
data2reference | Create reference data from other data
Datetime2Time | Convert Datetime columns to Time columns
Datetime_breaks | Create a (shifted) sequence of Datetimes for axis breaks
Datetime_limits | Find or set sensible limits for Datetime axis
disparity_index | Disparity index
dominant_epoch | Determine the dominant epoch/interval of a dataset
dose | Calculate the dose (value·hours)
dst_change_handler | Handle jumps in Daylight Savings (DST) that are missing in the data
dst_change_summary | Get a summary of groups where a daylight saving time change occurs.
durations | Calculate duration of data in each group
duration_above_threshold | Duration above/below threshold or within threshold range
exponential_moving_average | Exponential moving average filter (EMA)
exp_zero_inflated | Add a defined number to a numeric and log transform it
extract_clusters | Find and extract clusters from a dataset
extract_gaps | Extract gap episodes from the data
extract_metric | Add metrics to extracted sSummary
extract_photoperiod | Calculate photoperiod and boundary times
extract_states | Extract summaries of states
filter_Date | Filter Datetimes in a dataset.
filter_Datetime | Filter Datetimes in a dataset.
filter_Datetime_multiple | Filter multiple times based on a list of arguments.
filter_Time | Filter Times in a dataset.
frequency_crossing_threshold | Frequency of crossing light threshold
gain.ratio.tables | Gain / Gain-ratio tables to normalize counts
gapless_Datetimes | Create a gapless sequence of Datetimes
gap_finder | Check for and output gaps in a dataset
gap_handler | Fill implicit gaps in a light logger dataset
gap_table | Tabular summary of data and gaps in all groups
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_doubleplot | Double Plots
gg_gaps | Visualize gaps and irregular data
gg_heatmap | Plot a heatmap across days and times of day
gg_overview | Plot an overview of dataset intervals with implicit missing data
gg_photoperiod | Add photoperiods to gg_day() or gg_days() plots
gg_state | Add states to gg_day() or gg_days() plots
has_gaps | Does a dataset have implicit gaps
has_irregulars | Does a dataset have irregular data
import | Import a light logger dataset or related data
import_adjustment | Adjust device imports or make your own
import_Dataset | Import a light logger dataset or related data
import_Statechanges | Import data that contain 'Datetimes' of 'Statechanges'
interdaily_stability | Interdaily stability (IS)
interval2state | Adds a state column to a dataset from interval data
intradaily_variability | Intradaily variability (IV)
join_datasets | Join similar Datasets
ll_import_expr | Get the import expression for a device
log_zero_inflated | Add a defined number to a numeric and log transform it
mean_daily | Calculate mean daily metrics from daily summary
mean_daily_metric | Calculate mean daily metrics from Time Series
midpointCE | Midpoint of cumulative light exposure.
normalize_counts | Normalize counts between sensor outputs
number_states | Number non-consecutive state occurrences
nvRC | Non-visual circadian response
nvRC_circadianBias | Performance metrics for circadian response
nvRC_circadianDisturbance | Performance metrics for circadian response
nvRC_metrics | Performance metrics for circadian response
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
photoperiod | Calculate photoperiod and boundary times
pulses_above_threshold | Pulses above threshold
remove_partial_data | Remove groups that have too few data points
reverse2_trans | Create a reverse transformation function specifically for date scales
sample.data.environment | Sample of wearable data combined with environmental data
sample.data.irregular | Sample of highly irregular wearable data
sc2interval | Statechange (sc) Timestamps to Intervals
sleep_int2Brown | Recode Sleep/Wake intervals to Brown state intervals
solar_noon | Calculate photoperiod and boundary times
spectral_integration | Integrate spectral irradiance with optional weighting
spectral_reconstruction | Reconstruct spectral irradiance from sensor counts
summarise_numeric | Summarize numeric columns in dataframes to means
summarize_numeric | Summarize numeric columns in dataframes to means
supported_devices | Get all the supported devices in LightLogR
symlog_trans | Scale positive and negative values on a log scale
threshold_for_duration | Find threshold for given duration
timing_above_threshold | Mean/first/last timing above/below threshold.