Generate Data

The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Generate Data

The walkboutr package has several functions to generate sample data so that you can see how the package works in practice. We generate GPS data and accelerometry data separately, as that is how you will provide your data to walkboutr.

GPS Data Generation

GPS data provided should contain the following columns and have the relevant characteristics:

time values should be in date-time format and be all non-missing
latitude values should be between -90 and 90 and be all non-missing
longitude values should be between -180 and 180 and be all non-missing
speed values should all be positive, non-missing values in km/h

These data will be processed and later combined with accelerometry data to generate walkbouts. #### Generating sample GPS data

gps_data <- generate_gps_data(start_lat = 40.7128, start_long = 74.0060, start_time = lubridate::ymd_hms('2012-04-07 00:00:30'))

These sample GPS data meet all of the characteristics outlined above:

time	latitude	longitude	speed
2012-04-07 00:00:30	40.71280	74.00600	3.8025233
2012-04-07 00:01:00	40.73027	74.02347	2.4004880
2012-04-07 00:01:30	40.73920	74.03240	0.6412646
2012-04-07 00:02:00	40.74236	74.03556	1.6616599
2012-04-07 00:02:30	40.74859	74.04179	2.0068013
2012-04-07 00:03:00	40.75639	74.04959	1.1009735

walkboutr also has a function to generate a realistic walking route in Seattle, which is simply meant to provide another example for generating data and becoming familiar with the package:

seattle <- generate_walking_in_seattle_gps_data()

These data look exactly the same as the randomly generated sample GPS data:

time	latitude	longitude	speed
2012-04-07 00:00:30	47.60620	122.3321	3.8025233
2012-04-07 00:01:00	47.62367	122.3496	2.4004880
2012-04-07 00:01:30	47.63260	122.3585	0.6412646
2012-04-07 00:02:00	47.63576	122.3617	1.6616599
2012-04-07 00:02:30	47.64199	122.3679	2.0068013
2012-04-07 00:03:00	47.64979	122.3757	1.1009735

Generating sample Accelerometry data

Accelerometry data provided should contain the following columns and have the relevant characteristics:

time values should be in date-time format and be all non-missing
activity_count values should be the output of accelerometers and represent activity counts in CPE. These values should be non-missing and non-negative.

These data will be processed and later combined with the GPS data to generate walkbouts.

There are more functions to generate accelerometry data so that you can see the differences based on the size of the dataset. The following functions are included for you to generate sample data:

For the purposes of this example, we will create generate the smallest walk bout.

accelerometry_counts <- make_smallest_bout_without_metadata()

These sample accelerometry data meet all of the characteristics outlined above:

activity_counts	time
0	2012-04-07 00:00:30
0	2012-04-07 00:01:00
0	2012-04-07 00:01:30
0	2012-04-07 00:02:00
500	2012-04-07 00:02:30
500	2012-04-07 00:03:00

Generating a full data frame of walk bouts

This function generates a data frame of walk bouts with accelerometry and GPS data so that you can get an idea of how some of the top level functions work. These data won’t be used directly by the package, but are here to give you an idea of what a full dataset looks like as it goes into the final steps of the package.

In order to generate these data, you can use the make_full_walk_bout_df function:

walk_bouts <- make_full_walk_bout_df()

This dataset looks like this:

time	latitude	longitude	speed	activity_counts	bout	inactive	non_wearing	n_epochs_date	complete_day
2012-04-07 00:00:30	47.60620	122.3321	3.8025233	0	NA	TRUE	FALSE	8616	TRUE
2012-04-07 00:01:00	47.62367	122.3496	2.4004880	0	NA	TRUE	FALSE	8616	TRUE
2012-04-07 00:01:30	47.63260	122.3585	0.6412646	0	NA	TRUE	FALSE	8616	TRUE
2012-04-07 00:02:00	47.63576	122.3617	1.6616599	0	NA	TRUE	FALSE	8616	TRUE
2012-04-07 00:02:30	47.64199	122.3679	2.0068013	500	1	FALSE	FALSE	8616	TRUE
2012-04-07 00:03:00	47.64979	122.3757	1.1009735	500	1	FALSE	FALSE	8616	TRUE

Please see docs for each of these functions for generating data. The full list of available functions is:

generate_gps_data()
generate_walking_in_seattle_gps_data()
make_smallest_bout_window()
make_smallest_nonwearing_window()
make_smallest_complete_day_activity()
make_smallest_bout()
make_smallest_bout_without_metadata()
make_smallest_bout_with_largest_inactive_period()
make_smallest_bout_with_smallest_non_wearing_period()
make_full_day_bout()
make_full_day_bout_without_metadata()
make_full_walk_bout_df()

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.
Health stats visible at Monitor.