Package: walkboutr 0.5.0

Lauren Blair Wilner

walkboutr: Generate Walk Bouts from GPS and Accelerometry Data

Process GPS and accelerometry data to generate walk bouts. A walk bout is a period of activity with accelerometer movement matching the patterns of walking with corresponding GPS measurements that confirm travel. The inputs of the 'walkboutr' package are individual-level accelerometry and GPS data. The outputs of the model are walk bouts with corresponding times, duration, and summary statistics on the sample population, which collapse all personally identifying information. These bouts can be used to measure walking both as an outcome of a change to the built environment or as a predictor of health outcomes such as a cardioprotective behavior. Kang B, Moudon AV, Hurvitz PM, Saelens BE (2017) <doi:10.1016/j.trd.2017.09.026>.

Authors:Lauren Blair Wilner [aut, cre, cph], Stephen J Mooney [aut]

walkboutr_0.5.0.tar.gz
walkboutr_0.5.0.zip(r-4.5)walkboutr_0.5.0.zip(r-4.4)walkboutr_0.5.0.zip(r-4.3)
walkboutr_0.5.0.tgz(r-4.4-any)walkboutr_0.5.0.tgz(r-4.3-any)
walkboutr_0.5.0.tar.gz(r-4.5-noble)walkboutr_0.5.0.tar.gz(r-4.4-noble)
walkboutr_0.5.0.tgz(r-4.4-emscripten)walkboutr_0.5.0.tgz(r-4.3-emscripten)
walkboutr.pdf |walkboutr.html
walkboutr/json (API)
NEWS

# Install 'walkboutr' in R:
install.packages('walkboutr', repos = c('https://rwalkbout.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/rwalkbout/walkboutr/issues

On CRAN:

4.48 score 9 scripts 151 downloads 22 exports 59 dependencies

Last updated 10 months agofrom:329d000a6b. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 21 2024
R-4.5-winOKNov 21 2024
R-4.5-linuxOKNov 21 2024
R-4.4-winOKNov 21 2024
R-4.4-macOKNov 21 2024
R-4.3-winOKNov 21 2024
R-4.3-macOKNov 21 2024

Exports:%>%assign_epoch_start_timegenerate_bout_plotgenerate_gps_datagenerate_walking_in_seattle_gps_dataidentify_walk_bouts_in_gps_and_accelerometry_datamake_full_day_boutmake_full_day_bout_without_metadatamake_full_walk_bout_dfmake_non_bout_windowmake_smallest_boutmake_smallest_bout_with_largest_inactive_periodmake_smallest_bout_with_smallest_non_wearing_periodmake_smallest_bout_without_metadatamake_smallest_complete_day_activitymake_smallest_nonwearing_windowprocess_accelerometry_counts_into_boutsprocess_bouts_and_gps_epochs_into_walkboutsprocess_gps_data_into_gps_epochssummarize_walk_boutsvalidate_accelerometry_datavalidate_gps_data

Dependencies:classclassIntclicolorspacecpp11data.tableDBIdplyre1071fansifarvergenericsgeosphereggforceggplot2gluegtableisobandKernSmoothlabelinglatticelifecyclelubridatelwgeommagrittrMASSMatrixmeasurementsmgcvmunsellnlmepillarpkgconfigpolyclipproxypurrrR6RColorBrewerRcppRcppEigenrlangs2scalessfspstringistringrsystemfontstibbletidyrtidyselecttimechangetweenrunitsutf8vctrsviridisLitewithrwk

Changing Default Parameters

Rendered fromchanging_default_parameters.Rmdusingknitr::rmarkdownon Nov 21 2024.

Last update: 2023-09-22
Started: 2023-05-18

Generate Data

Rendered fromgenerate_data.Rmdusingknitr::rmarkdownon Nov 21 2024.

Last update: 2023-05-23
Started: 2023-05-15

Generate Walk Bouts

Rendered fromprocess_bouts.Rmdusingknitr::rmarkdownon Nov 21 2024.

Last update: 2023-05-23
Started: 2023-05-15

Readme and manuals

Help Manual

Help pageTopics
Add date and format to activity countsadd_date_and_format
Assign Epoch Start Timeassign_epoch_start_time
Collate Arguments This function collates user-provided arguments with pre-defined parameters and constants.collate_arguments
List of Constants 'non_wearing_min_threshold_epochs' Number of consecutive epochs with activity counts of 0 that constitute a non_wearing period. 'min_wearing_hours_per_day' Minimum number of hours in a day an individual must wear an accelerometer for the day to be considered complete. 'min_gps_obs_within_bout' Minimum number of GPS observations within a bout for that bout to be considered to have complete GPS data. 'min_gps_coverage_ratio' Minimum ratio of data points with versus without GPS data for the bout to be considered to have complete GPS data. 'dwellbout_radii_quantile' Threshold for outliering GPS data points - any data points above the 95th percentile are outliered. 'max_dwellbout_radii_ft' Maximum radius, in feet, of a bounding circle that would be considered a dwell bout (rather than a potential walk bout). 'min_dwellbout_obs' Minimum number of observations to consider something a potential dwell bout. 'max_walking_cpe' Maxiumum CPE value before the accelerometer is considered to be picking up on an activity other than walking. 'min_walking_speed_km_h' Minimum speed considered walking. 'max_walking_speed_km_h' Maximum speed considered walking.constants
Evaluate GPS completeness for each walking boutevaluate_gps_completeness
Generate bout categoriesgenerate_bout_category
Generate Bout Plotgenerate_bout_plot
Generate Bounding Circle Radius for Walking Boutsgenerate_bout_radius
Generate a dataset with date-time, speed, and latitude and longitude of someone moving through space on a walk in Seattlegenerate_gps_data
Generate GPS data for a walking activity in Seattle, WAgenerate_walking_in_seattle_gps_data
Identify Bouts:identify_bouts
Identify complete wearing days This function identifies complete days based on accelerometry data by calculating the total number of epochs worn per day and comparing it to the minimum number of wearing epochs per day required to consider a day complete.identify_complete_days
Identify non-wearing periods: This function identifies non-wearing periods in accelerometry data based on a threshold of consecutive epochs with activity counts of 0.identify_non_wearing_periods
Identify walking bouts in GPS and accelerometry data:identify_walk_bouts_in_gps_and_accelerometry_data
Generate accelerometry datasetsmake_active_period
Create activity counts for a full day boutmake_full_day_bout
Create activity counts for a full day bout without metadatamake_full_day_bout_without_metadata
Create a data frame of walking bouts with GPS datamake_full_walk_bout_df
Create an inactive periodmake_inactive_period
Create a non-bout windowmake_non_bout_window
Make the smallest bout datasetmake_smallest_bout
Create the smallest bout windowmake_smallest_bout_window
Generate a sequence of accelerometer counts representing the smallest bout with the largest inactive periodmake_smallest_bout_with_largest_inactive_period
Generate the smallest bout with the smallest non-wearing period datasetmake_smallest_bout_with_smallest_non_wearing_period
Create the smallest bout window without metadatamake_smallest_bout_without_metadata
Generate an activity sequence for a complete day with minimal activitymake_smallest_complete_day_activity
Create smallest non-wearing windowmake_smallest_nonwearing_window
Calculate next latitude and longitude based on current location, speed, direction, and time elapsed.next_lat_long
Outlier GPS data points This function identifies outlier GPS points for the bout radius calculation from a given set of latitude and longitude coordinates.outlier_gps_points
Global parameters and constantsparameters
Process Accelerometry Counts into Boutsprocess_accelerometry_counts_into_bouts
Process bouts and GPS epochs into walk boutsprocess_bouts_and_gps_epochs_into_walkbouts
Convert GPS data into GPS epochsprocess_gps_data_into_gps_epochs
Run Length Encoding:run_length_encode
Summarize walking bouts: This function summarizes walking bouts and calculates the median speed, complete day, non-wearing, bout start, and duration of each bout.summarize_walk_bouts
Validate accelerometry input datavalidate_accelerometry_data
Validate GPS datavalidate_gps_data