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Generate Data

library(walkboutr)

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:

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:

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:

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.