Version:	1.6.0
Title:	Infrastructure for Running, Cycling and Swimming Data from GPS-Enabled Tracking Devices
Description:	Provides infrastructure for handling running, cycling and swimming data from GPS-enabled tracking devices within R. The package provides methods to extract, clean and organise workout and competition data into session-based and unit-aware data objects of class 'trackeRdata' (S3 class). The information can then be visualised, summarised, and analysed through flexible and extensible methods. Frick and Kosmidis (2017) <doi:10.18637/jss.v082.i07>, which is updated and maintained as one of the vignettes, provides detailed descriptions of the package and its methods, and real-data demonstrations of the package functionality.
Depends:	R (≥ 3.1.0), zoo
Imports:	ggplot2, ggridges, xml2, RSQLite, jsonlite, raster, scam, foreach, fda, sp, leaflet, ggmap, gridExtra, gtable
Suggests:	spelling, testthat, knitr, rmarkdown, covr
VignetteBuilder:	knitr
License:	GPL-3
URL:	https://github.com/trackerproject/trackeR
BugReports:	https://github.com/trackerproject/trackeR/issues
RoxygenNote:	7.2.3
Encoding:	UTF-8
LazyData:	true
Language:	en-GB
NeedsCompilation:	no
Packaged:	2024-01-11 16:31:42 UTC; yiannis
Author:	Ioannis Kosmidis [aut, cre], Hannah Frick [aut], Robin Hornak [aut]
Maintainer:	Ioannis Kosmidis <ioannis.kosmidis@warwick.ac.uk>
Repository:	CRAN
Date/Publication:	2024-01-11 17:30:02 UTC

Coercion function for use in Golden Cheetah

Description

Coercion function for use in Golden Cheetah

Usage

GC2trackeRdata(
  gc,
  cycling = TRUE,
  correct_distances = FALSE,
  country = NULL,
  mask = TRUE,
  from_distances = FALSE,
  lgap = 30,
  lskip = 5,
  m = 11,
  silent = FALSE
)

Arguments

See Also

trackeRdata

W' expended.

Description

Calculate W' expended, i.e., the work capacity above critical power/speed which has been depleted and not yet been replenished.

Usage

Wexp(object, w0, cp, version = c("2015", "2012"), meanRecoveryPower = FALSE)

Arguments

Details

Skiba et al. (2015) and Skiba et al. (2012) both describe an exponential decay of W' expended over an interval [t_{i-1}, t_i) if the power output during this interval is below critical power:

W_exp (t_i) = W_exp(t_{i-1}) * exp(nu * (t_i - t_{i-1}))

However, the factor nu differs: Skiba et al. (2012) describe it as 1/\tau with \tau estimated as

tau = 546 * exp(-0.01 * (CP - P_i)) + 316

Skiba et al. (2015) use (P_i - CP) / W'_0. Skiba et al. (2012) and Skiba et al. (2015) employ a constant recovery power (calculated as the mean over all power outputs below critical power). This rationale can be applied by setting the argument meanRecoveryPower to TRUE. Note that this uses information from all observations with a power output below critical power, not just those prior to the current time point.

References

Monod H, Scherrer J (1965). 'The Work Capacity of a Synergic Muscular Group.' Ergonomics, 8(3), 329–338.

Skiba PF, Chidnok W, Vanhatalo A, Jones AM (2012). 'Modeling the Expenditure and Reconstitution of Work Capacity above Critical Power.' Medicine & Science in Sports & Exercise, 44(8), 1526–1532.

Skiba PF, Fulford J, Clarke DC, Vanhatalo A, Jones AM (2015). 'Intramuscular Determinants of the Ability to Recover Work Capacity above Critical Power.' European Journal of Applied Physiology, 115(4), 703–713.

W': work capacity above critical power/speed.

Description

W': work capacity above critical power/speed.

Usage

Wprime(
  object,
  session = NULL,
  quantity = c("expended", "balance"),
  w0,
  cp,
  version = c("2015", "2012"),
  meanRecoveryPower = FALSE,
  parallel = FALSE,
  ...
)

Arguments

Details

#' Skiba et al. (2015) and Skiba et al. (2012) both describe an exponential decay of W' expended over an interval [t_{i-1}, t_i) if the power output during this interval is below critical power:

W_exp (t_i) = W_exp(t_{i-1}) * exp(nu * (t_i - t_{i-1}))

However, the factor nu differs: Skiba et al. (2012) describe it as 1/\tau with \tau estimated as

tau = 546 * exp(-0.01 * (CP - P_i)) + 316

Skiba et al. (2015) use (P_i - CP) / W'_0. Skiba et al. (2012) and Skiba et al. (2015) employ a constant recovery power (calculated as the mean over all power outputs below critical power). This rationale can be applied by setting the argument meanRecoveryPower to TRUE. Note that this uses information from all observations with a power output below critical power, not just those prior to the current time point.

Value

An object of class trackeRWprime.

References

Monod H, Scherrer J (1965). 'The Work Capacity of a Synergic Muscular Group.' Ergonomics, 8(3), 329–338.

Skiba PF, Chidnok W, Vanhatalo A, Jones AM (2012). 'Modeling the Expenditure and Reconstitution of Work Capacity above Critical Power.' Medicine & Science in Sports & Exercise, 44(8), 1526–1532.

Skiba PF, Fulford J, Clarke DC, Vanhatalo A, Jones AM (2015). 'Intramuscular Determinants of the Ability to Recover Work Capacity above Critical Power.' European Journal of Applied Physiology, 115(4), 703–713.

Examples

## Not run: 
data('runs', package = 'trackeR')
wexp <- Wprime(runs, session = c(11,13), cp = 4, version = '2012')
plot(wexp)

## End(Not run)

Generic function for appending data to existing files

Description

Generic function for appending data to existing files

Usage

append(object, file, ...)

Arguments

Append training sessions to existing file

Description

Append training sessions to existing file

Usage

## S3 method for class 'trackeRdata'
append(object, file, ...)

Arguments

Transform concentration profile to distribution profile.

Description

Transform concentration profile to distribution profile.

Usage

c2d(cp)

Arguments

Generic function for changing the units of measurement

Description

Generic function for changing the units of measurement

Usage

change_units(object, variable, unit, sport, ...)

changeUnits(object, variable, unit, sport, ...)

Arguments

Change the units of the variables in an conProfile object

Description

Change the units of the variables in an conProfile object

Usage

## S3 method for class 'conProfile'
change_units(object, variable, unit, ...)

Arguments

Change the units of the variables in an distrProfile object

Description

Change the units of the variables in an distrProfile object

Usage

## S3 method for class 'distrProfile'
change_units(object, variable, unit, ...)

Arguments

Change the units of the variables in an trackeRWprime object

Description

Change the units of the variables in an trackeRWprime object

Usage

## S3 method for class 'trackeRWprime'
change_units(object, variable, unit, ...)

Arguments

Change the units of the variables in an trackeRdata object

Description

Change the units of the variables in an trackeRdata object

Usage

## S3 method for class 'trackeRdata'
change_units(object, variable, unit, sport, ...)

Arguments

Change the units of the variables in an trackeRdataSummary object

Description

Change the units of the variables in an trackeRdataSummary object

Usage

## S3 method for class 'trackeRdataSummary'
change_units(object, variable, unit, ...)

Arguments

Change the units of the variables in an trackeRdataZones object

Description

Change the units of the variables in an trackeRdataZones object

Usage

## S3 method for class 'trackeRdataZones'
change_units(object, variable, unit, ...)

Arguments

Change the units of the variables in an trackeRthresholds object

Description

Change the units of the variables in an trackeRthresholds object

Usage

## S3 method for class 'trackeRthresholds'
change_units(object, variable, unit, sport, ...)

Arguments

Collect units from the result of generate_units

Description

Collects the units from the results of generate_units according to a unit_reference_sport

Usage

collect_units(object, unit_reference_sport = NULL)

Arguments

Compute a grid of breakpoints per variable from a trackeRdata object.

Description

Compute a grid of breakpoints per variable from a trackeRdata object.

Usage

compute_breaks(
  object,
  a = 1e-04,
  n_breaks = 9,
  limits = NULL,
  what = c("speed", "heart_rate")
)

Arguments

Value

A named list with names as in what, with elements the grids of breakpoints per variable.

Examples

data("runs")
compute_breaks(runs, what = c("speed", "heart_rate", "altitude"))

Compute variable limits from a trackeRdata object.

Description

Compute variable limits from a trackeRdata object.

Usage

compute_limits(object, a = 1e-04)

Arguments

Details

compute_limits computes limits by finding the a and 1 - a quantiles for each variable in each session, and then taking the minimum and maximum of the a and 1 - a, respectively, across sessions.

Generic method for concentration profiles

Description

Generic method for concentration profiles

Usage

concentration_profile(object, session = NULL, what = NULL, ...)

concentrationProfile(object, session = NULL, what = NULL, ...)

Arguments

See Also

concentration_profile.distrProfile concentration_profile.trackeRdata

Examples

## Not run: 
## Compute conecntration profiles from distribution profiles
data('run', package = 'trackeR')
dProfile <- distributionProfile(run, what = 'speed', grid = seq(0, 12.5, by = 0.05))
cProfile <- concentrationProfile(dProfile)
plot(cProfile, smooth = FALSE)
plot(cProfile)

## And now directly from the 'trackeRdata' object, which is a
## considerably faster if all that is needed are the concentration
## profiles
cProfile <- concentrationProfile(runs, what = 'speed',
                                 limits = list(speed = c(0, 12.5)))
plot(cProfile, smooth = FALSE)
ridges(cProfile)
plot(cProfile, smooth = TRUE)

## End(Not run)

Generate training concentration profiles.

Description

Generate training concentration profiles.

Usage

## S3 method for class 'distrProfile'
concentration_profile(object, session = NULL, what = NULL, ...)

## S3 method for class 'trackeRdata'
concentration_profile(
  object,
  session = NULL,
  what = NULL,
  limits = NULL,
  parallel = FALSE,
  unit_reference_sport = NULL,
  scale = FALSE,
  ...
)

Arguments

Value

An object of class conProfile.

Object:

A named list with one or more components, corresponding to the value of what. Each component is a matrix of dimension g times n, where g is the length of the grids set in grid (or 200 if grid = NULL) and n is the number of sessions requested in the session argument.

Attributes:

Each conProfile object has the following attributes:

• sport: the sports corresponding to the columns of each list component

• session_times: the session start and end times corresponding to the columns of each list component

• unit_reference_sport: the sport where the units have been inherited from

• operations: a list with the operations that have been applied to the object. See get_operations.distrProfile

• limits: The variable limits that have been used for the computation of the concentration profiles.

• units: an object listing the units used for the calculation of distribution profiles. These is the output of get_units on the corresponding trackeRdata object, after inheriting units from unit_reference_sport.

References

Kosmidis, I., and Passfield, L. (2015). Linking the Performance of Endurance Runners to Training and Physiological Effects via Multi-Resolution Elastic Net. ArXiv e-print arXiv:1506.01388.

Frick, H., Kosmidis, I. (2017). trackeR: Infrastructure for Running and Cycling Data from GPS-Enabled Tracking Devices in R. Journal of Statistical Software, 82(7), 1–29. doi:10.18637/jss.v082.i07

Auxiliary conversion functions

Description

Conversion functions for distance, duration, speed, pace, power, cadence and temperature.

Usage

m2km(variable)

km2m(variable)

m2ft(variable)

ft2m(variable)

m2mi(variable)

mi2m(variable)

km2ft(variable)

ft2km(variable)

km2mi(variable)

mi2km(variable)

ft2mi(variable)

mi2ft(variable)

m2m(variable)

km2km(variable)

ft2ft(variable)

mi2mi(variable)

s2min(variable)

min2s(variable)

s2h(variable)

h2s(variable)

min2h(variable)

h2min(variable)

h2h(variable)

min2min(variable)

s2s(variable)

min2min(variable)

h2h(variable)

degree2degree(variable)

m_per_s2km_per_h(variable)

km_per_h2m_per_s(variable)

m_per_s2ft_per_min(variable)

ft_per_min2m_per_s(variable)

m_per_s2ft_per_s(variable)

ft_per_s2m_per_s(variable)

m_per_s2mi_per_h(variable)

mi_per_h2m_per_s(variable)

m_per_s2km_per_min(variable)

km_per_min2m_per_s(variable)

m_per_s2mi_per_min(variable)

mi_per_min2m_per_s(variable)

km_per_h2ft_per_min(variable)

ft_per_min2km_per_h(variable)

km_per_h2ft_per_s(variable)

ft_per_s2km_per_h(variable)

km_per_h2mi_per_h(variable)

mi_per_h2km_per_h(variable)

km_per_h2km_per_min(variable)

km_per_min2km_per_h(variable)

km_per_h2mi_per_min(variable)

mi_per_min2km_per_h(variable)

ft_per_min2ft_per_s(variable)

ft_per_s2ft_per_min(variable)

ft_per_min2mi_per_h(variable)

mi_per_h2ft_per_min(variable)

ft_per_min2km_per_min(variable)

km_per_min2ft_per_min(variable)

ft_per_min2mi_per_min(variable)

mi_per_min2ft_per_min(variable)

ft_per_s2mi_per_h(variable)

mi_per_h2ft_per_s(variable)

ft_per_s2km_per_min(variable)

km_per_min2ft_per_s(variable)

ft_per_s2mi_per_min(variable)

mi_per_min2ft_per_s(variable)

mi_per_h2km_per_min(variable)

km_per_min2mi_per_h(variable)

mi_per_h2mi_per_min(variable)

mi_per_min2mi_per_h(variable)

km_per_min2mi_per_min(variable)

mi_per_min2km_per_min(variable)

m_per_s2m_per_s(variable)

km_per_h2km_per_h(variable)

ft_per_min2ft_per_min(variable)

ft_per_s2ft_per_s(variable)

mi_per_h2mi_per_h(variable)

km_per_min2km_per_min(variable)

mi_per_min2mi_per_min(variable)

m_per_s2m_per_min(variable)

m_per_min2m_per_s(variable)

m_per_min2m_per_min(variable)

bpm2bpm(variable)

s_per_m2min_per_km(variable)

min_per_km2s_per_m(variable)

s_per_m2min_per_mi(variable)

min_per_mi2s_per_m(variable)

min_per_km2min_per_mi(variable)

min_per_mi2min_per_km(variable)

min_per_ft2min_per_km(variable)

min_per_ft2min_per_mi(variable)

s_per_m2s_per_m(variable)

min_per_km2min_per_km(variable)

min_per_mi2min_per_mi(variable)

h_per_km2min_per_km(variable)

h_per_km2min_per_mi(variable)

h_per_mi2min_per_km(variable)

h_per_mi2min_per_mi(variable)

W2kW(variable)

kW2W(variable)

W2W(variable)

kW2kW(variable)

steps_per_min2steps_per_min(variable)

rev_per_min2rev_per_min(variable)

steps_per_min2rev_per_min(variable)

rev_per_min2steps_per_min(variable)

C2F(variable)

C2C(variable)

F2F(variable)

F2C(variable)

Arguments

Smooth a decreasing function.

Description

This smoother ensures a positive response that is a monotone decreasing function of x.

Usage

decreasing_smoother(x, y, k = 30, len = NULL, sp = NULL)

decreasingSmoother(x, y, k = 30, len = NULL, sp = NULL)

Arguments

Convert distance to speed.

Description

Convert distance to speed.

Usage

distance2speed(distance, time, timeunit)

Arguments

Value

Speed in meters per second.

Generate training distribution profiles.

Description

Generate training distribution profiles.

Usage

distribution_profile(
  object,
  session = NULL,
  what = NULL,
  grid = NULL,
  parallel = FALSE,
  unit_reference_sport = NULL
)

distributionProfile(
  object,
  session = NULL,
  what = NULL,
  grid = NULL,
  parallel = FALSE,
  unit_reference_sport = NULL
)

Arguments

Value

An object of class distrProfile.

Object:

A named list with one or more components, corresponding to the value of what. Each component is a matrix of dimension g times n, where g is the length of the grids set in grid (or 201 if grid = NULL) and n is the number of sessions requested in the session argument.

Attributes:

Each distrProfile object has the following attributes:

• sport: the sports corresponding to the columns of each list component

• session_times: the session start and end times corresponding to the columns of each list component

• unit_reference_sport: the sport where the units have been inherited from

• operations: a list with the operations that have been applied to the object. See get_operations.distrProfile

• limits: The variable limits that have been used for the computation of the distribution profiles

• units: an object listing the units used for the calculation of distribution profiles. These is the output of get_units on the corresponding trackeRdata object, after inheriting units from unit_reference_sport.

References

Kosmidis, I., and Passfield, L. (2015). Linking the Performance of Endurance Runners to Training and Physiological Effects via Multi-Resolution Elastic Net. ArXiv e-print arXiv:1506.01388.

Frick, H., Kosmidis, I. (2017). trackeR: Infrastructure for Running and Cycling Data from GPS-Enabled Tracking Devices in R. Journal of Statistical Software, 82(7), 1–29. doi:10.18637/jss.v082.i07

Examples

data('run', package = 'trackeR')
dProfile <- distribution_profile(run, what = c("speed", "cadence_running"))
## Not run: 
plot(dProfile, smooth = FALSE)

## End(Not run)

Find the most frequent sport in an object

Description

Find the most frequent sport in an object

Usage

find_unit_reference_sport(object)

Arguments

Fortify a conProfile object for plotting with ggplot2.

Description

Fortify a conProfile object for plotting with ggplot2.

Usage

## S3 method for class 'conProfile'
fortify(model, data, melt = FALSE, ...)

fortify_conProfile(model, data, melt = FALSE, ...)

Arguments

Fortify a distrProfile object for plotting with ggplot2.

Description

Fortify a distrProfile object for plotting with ggplot2.

Usage

## S3 method for class 'distrProfile'
fortify(model, data, melt = FALSE, ...)

fortify_distrProfile(model, data, melt = FALSE, ...)

Arguments

Fortify a trackeRWprime object for plotting with ggplot2.

Description

Fortify a trackeRWprime object for plotting with ggplot2.

Usage

## S3 method for class 'trackeRWprime'
fortify(model, data, melt = FALSE, ...)

fortify_trackeRWprime(model, data, melt = FALSE, ...)

Arguments

Fortify a trackeRdata object for plotting with ggplot2

Description

Fortify a trackeRdata object for plotting with ggplot2

Usage

## S3 method for class 'trackeRdata'
fortify(model, data, melt = FALSE, ...)

fortify_trackeRdata(model, data, melt = FALSE, ...)

Arguments

Fortify a trackeRdataSummary object for plotting with ggplot2.

Description

Fortify a trackeRdataSummary object for plotting with ggplot2.

Usage

## S3 method for class 'trackeRdataSummary'
fortify(model, data, melt = FALSE, ...)

fortify_trackeRdataSummary(model, data, melt = FALSE, ...)

Arguments

Functional principal components analysis of distribution or concentration profiles.

Description

Functional principal components analysis of distribution or concentration profiles.

Generic function for functional principal components analysis

Usage

## S3 method for class 'distrProfile'
funPCA(object, what, nharm = 4, ...)

## S3 method for class 'conProfile'
funPCA(object, what, nharm = 4, ...)

funPCA(object, ...)

Arguments

Details

The ... argument is passed on to pca.fd.

Value

An object of class trackeRfpca.

References

Ramsay JO, Silverman BW (2005). Functional Data Analysis. Springer-Verlag New York.

Examples

## Not run: 
data('runs', package = 'trackeR')
dp <- distributionProfile(runs, what = 'speed')
dp.pca <- funPCA(dp, what = 'speed', nharm = 4)
## 1st harmonic  captures vast majority of the variation
plot(dp.pca, harm = 1)
## time spent above speed = 0 is the characteristic distinguishing the profiles
sumRuns <- summary(runs)
plot(sumRuns$durationMoving, dp.pca$scores[,1])

## End(Not run)

Generate default thresholds.

Description

Generate default thresholds.

Usage

generate_thresholds(variable, lower, upper, sport, ...)

generateDefaultThresholds(variable, lower, upper, sport, ...)

Arguments

Generate and set base units.

Description

Generate and set base units.

Usage

generate_units(variable, unit, sport, ...)

generateBaseUnits(variable, unit, sport, ...)

Arguments

Details

The available units are

• variables latitude and longitude with unit degree (default)

• variables altitude, distance with unit m (default), km, mi or ft

• variable heart_rate with unit bpm (default)

• variable speed with unit m_per_s (default), km_per_h, ft_per_min, ft_per_s or mi_per_h

• variable cadence_running with unit steps_per_min (default; running only)

• variable cadence_cycling with unit rev_per_min (default; cycling only)

• variable power with unit W (Watt; default) or kW (cycling only)

• variable temperature with unit C (Celsius; default) or F

Note that generate_units checks if the supplied combinations of variable and sport are valid. generate_units will not check if any of the supplied units are correct for the corresponding combination of variable and sport.

(Cumulative) Elevation gain.

Description

(Cumulative) Elevation gain.

Usage

get_elevation_gain(
  object,
  smooth = FALSE,
  cumulative = FALSE,
  vertical_noise = 0
)

Arguments

Details

The elevation gain is defined here as the difference in altitude between two consecutive observations. If cumulative = FALSE then the elevation gain is returned, otherwise any elevation loses (i.e. negative elevation gain) are ignored and the cumulative elevation gain is returned. If smooth = TRUE then the elevation gain will be smoothed using a spline smoother before either returning it or computing cumulative elevation gains.

Generic function for retrieving the operation settings

Description

Generic function for retrieving the operation settings

Usage

get_operations(object, ...)

getOperations(object, ...)

Arguments

Get the operation settings of an conProfile object

Description

Get the operation settings of an conProfile object

Usage

## S3 method for class 'conProfile'
get_operations(object, ...)

Arguments

Get the operation settings of an distrProfile object

Description

Get the operation settings of an distrProfile object

Usage

## S3 method for class 'distrProfile'
get_operations(object, ...)

Arguments

Get the operation settings of an trackeRdata object

Description

Get the operation settings of an trackeRdata object

Usage

## S3 method for class 'trackeRdata'
get_operations(object, ...)

Arguments

Generic function to subset distribution and concentration profiles

Description

Generic function to subset distribution and concentration profiles

Usage

## S3 method for class 'distrProfile'
get_profile(object, session = NULL, what = NULL, ...)

## S3 method for class 'conProfile'
get_profile(object, session = NULL, what = NULL, ...)

get_profile(object, session, what, ...)

Arguments

Extract resting period characteristics

Description

Extract resting period characteristics

Usage

get_resting_periods(times, session_threshold)

restingPeriods(times, session_threshold)

Arguments

Value

A list containing a dataframe with start, end, and duration for each session and the resting time between sessions, named 'sessions' and 'restingTime', respectively.

Generic function for extracting sports

Description

Generic function for extracting sports

Usage

## S3 method for class 'trackeRWprime'
get_sport(object, ...)

## S3 method for class 'conProfile'
get_sport(object, session = NULL, ...)

## S3 method for class 'distrProfile'
get_sport(object, session = NULL, ...)

get_sport(object, session, ...)

## S3 method for class 'trackeRdata'
get_sport(object, session = NULL, ...)

## S3 method for class 'trackeRdataSummary'
get_sport(object, session = NULL, ...)

Arguments

Generic function for extracting the units of measurement

Description

Generic function for extracting the units of measurement

Usage

get_units(object, ...)

getUnits(object, ...)

Arguments

Get the units of the variables in an conProfile object

Description

Get the units of the variables in an conProfile object

Usage

## S3 method for class 'conProfile'
get_units(object, ...)

Arguments

Get the units of the variables in an distrProfile object

Description

Get the units of the variables in an distrProfile object

Usage

## S3 method for class 'distrProfile'
get_units(object, ...)

Arguments

Get the units of the variables in an trackeRWprime object

Description

Get the units of the variables in an trackeRWprime object

Usage

## S3 method for class 'trackeRWprime'
get_units(object, ...)

Arguments

Get the units of the variables in an trackeRdata object

Description

Get the units of the variables in an trackeRdata object

Usage

## S3 method for class 'trackeRdata'
get_units(object, ...)

Arguments

Get the units of the variables in an trackeRdataSummary object

Description

Get the units of the variables in an trackeRdataSummary object

Usage

## S3 method for class 'trackeRdataSummary'
get_units(object, ...)

Arguments

Get the units of the variables in an trackeRdataZones object

Description

Get the units of the variables in an trackeRdataZones object

Usage

## S3 method for class 'trackeRdataZones'
get_units(object, ...)

Arguments

Get the units of the variables in an trackeRfpca object

Description

Get the units of the variables in an trackeRfpca object

Usage

## S3 method for class 'trackeRfpca'
get_units(object, ...)

Arguments

Get the units of the variables in an trackeRthresholds object

Description

Get the units of the variables in an trackeRthresholds object

Usage

## S3 method for class 'trackeRthresholds'
get_units(object, ...)

Arguments

Impute speeds

Description

Impute speeds of 0 during small breaks within a session.

Usage

impute_speeds(
  session_data,
  from_distances = TRUE,
  lgap = 30,
  lskip = 5,
  m = 11,
  sport = "cycling",
  units = NULL
)

imputeSpeeds(
  session_data,
  from_distances = TRUE,
  lgap = 30,
  lskip = 5,
  m = 11,
  sport = "cycling",
  units = NULL
)

Arguments

Value

A multivariate zoo object with imputed observations: 0 for speed, last known position for latitude, longitude and altitude, NA for all other variables. Distances are calculated based on speeds after imputation.

References

Kosmidis, I., and Passfield, L. (2015). Linking the Performance of Endurance Runners to Training and Physiological Effects via Multi-Resolution Elastic Net. ArXiv e-print arXiv:1506.01388.

Frick, H., Kosmidis, I. (2017). trackeR: Infrastructure for Running and Cycling Data from GPS-Enabled Tracking Devices in R. Journal of Statistical Software, 82(7), 1–29. doi:10.18637/jss.v082.i07

Plot routes for training sessions

Description

Plot the route ran/cycled during training on an interactive map. Internet connection is required to download the background map. Icons are by Maps Icons Collection https://mapicons.mapsmarker.com

Usage

leaflet_route(x, session = NULL, threshold = TRUE, ...)

leafletRoute(x, session = NULL, threshold = TRUE, ...)

Arguments

Examples

## Not run: 
data('runs', package = 'trackeR')
leafletRoute(runs, session = 23:24)

## End(Not run)

Generic function for calculating number of sessions

Description

Generic function for calculating number of sessions

Usage

## S3 method for class 'trackeRWprime'
nsessions(object, ...)

## S3 method for class 'distrProfile'
nsessions(object, ...)

## S3 method for class 'conProfile'
nsessions(object, ...)

nsessions(object, ...)

## S3 method for class 'trackeRdataSummary'
nsessions(object, ...)

Arguments

Plot concentration profiles.

Description

Plot concentration profiles.

Usage

## S3 method for class 'conProfile'
plot(x, session = NULL, what = NULL, multiple = FALSE, smooth = FALSE, ...)

Arguments

Examples

data('runs', package = 'trackeR')
dProfile <- distributionProfile(runs, session = 1:3, what = 'speed',
                                grid = seq(0, 12.5, by = 0.05))
cProfile <- concentrationProfile(dProfile)
## Not run: 
plot(cProfile, smooth = FALSE)
plot(cProfile)

## End(Not run)

Plot distribution profiles.

Description

Plot distribution profiles.

Usage

## S3 method for class 'distrProfile'
plot(x, session = NULL, what = NULL, multiple = FALSE, smooth = FALSE, ...)

Arguments

Examples

## Not run: 
data('runs', package = 'trackeR')
dProfile <- distribution_profile(runs, session = 1:2,
    what = "speed", grid = seq(0, 12.5, by = 0.05))
plot(dProfile, smooth = FALSE)
plot(dProfile, smooth = FALSE, multiple = TRUE)
plot(dProfile, multiple = TRUE)

## End(Not run)

Plot W'.

Description

Plot W'.

Usage

## S3 method for class 'trackeRWprime'
plot(x, session = NULL, dates = TRUE, scaled = TRUE, ...)

Arguments

Examples

## Not run: 
data('runs', package = 'trackeR')
wexp <- Wprime(runs, session = 1:3, cp = 4, version = '2012')
plot(wexp, session = 1:2)

## End(Not run)

Plot training sessions in form of trackeRdata objects

Description

Plot training sessions in form of trackeRdata objects

Usage

## S3 method for class 'trackeRdata'
plot(
  x,
  session = NULL,
  what = c("pace", "heart_rate"),
  threshold = TRUE,
  smooth = FALSE,
  trend = TRUE,
  dates = TRUE,
  unit_reference_sport = NULL,
  moving_threshold = NULL,
  ...
)

Arguments

Details

Note that a threshold is always applied to the pace. This (upper) threshold corresponds to a speed of 1.4 meters per second, the preferred walking speed of humans. The lower threshold is 0.

The units for the variables match those of the sport specified by unit_reference_sport.

See Also

trackeRdata

Examples

## Not run: 
data('runs', package = 'trackeR')
## plot heart rate and pace for the first 3 sessions
plot(runs, session = 1:3)
## plot raw speed data for session 4
plot(runs, session = 4, what = "speed", threshold = FALSE, smooth = FALSE)
## threshold speed variable
plot(runs, session = 4, what = "speed", threshold = TRUE, smooth = FALSE,
    variable = "speed", lower = 0, upper = 10)
## and smooth (thresholding with default values)
plot(runs, session = 4, what = "speed", threshold = TRUE,
    smooth = TRUE, width = 15, parallel = FALSE)
#'
## Speed and elevation gain
plot(runs, session = 2:10, what = c("speed", "cumulative_elevation_gain"), trend = FALSE)

## End(Not run)

Plot an object of class trackeRdataSummary.

Description

Plot an object of class trackeRdataSummary.

Usage

## S3 method for class 'trackeRdataSummary'
plot(x, date = TRUE, what = NULL, group = NULL, trend = TRUE, ...)

Arguments

See Also

summary.trackeRdata

Examples

## Not run: 
data('runs', package = 'trackeR')
runSummary <- summary(runs)
plot(runSummary)
plot(runSummary, date = FALSE, group = 'total',
    what = c('distance', 'duration', 'avgSpeed'))

## End(Not run)

Plot training zones.

Description

Plot training zones.

Usage

## S3 method for class 'trackeRdataZones'
plot(x, percent = TRUE, ...)

Arguments

Examples

## Not run: 
data('run', package = 'trackeR')
runZones <- zones(run, what = 'speed', breaks = c(0, 2:6, 12.5))
plot(runZones, percent = FALSE)

## End(Not run)

Plot function for functional principal components analysis of distribution and concentration profiles.

Description

Plot function for functional principal components analysis of distribution and concentration profiles.

Usage

## S3 method for class 'trackeRfpca'
plot(x, harm = NULL, expand = NULL, pointplot = TRUE, ...)

Arguments

References

Ramsay JO, Silverman BW (2005). Functional Data Analysis. Springer-Verlag New York.

See Also

plot.pca.fd

Examples

## Not run: 
data('runs', package = 'trackeR')
dp <- distributionProfile(runs, what = 'speed')
dp.pca <- funPCA(dp, what = 'speed', nharm = 4)
## 1st harmonic  captures vast majority of the variation
plot(dp.pca)
plot(dp.pca, harm = 1, pointplot = FALSE)

## End(Not run)

Plot routes for training sessions

Description

Plot the route ran/cycled during training onto a background map. Internet connection is required to download the background map.

Usage

plot_route(
  x,
  session = 1,
  zoom = NULL,
  speed = TRUE,
  threshold = TRUE,
  mfrow = NULL,
  maptype = "stamen_terrain",
  messaging = FALSE,
  ...
)

plotRoute(
  x,
  session = 1,
  zoom = NULL,
  speed = TRUE,
  threshold = TRUE,
  mfrow = NULL,
  maptype = "stamen_terrain",
  messaging = FALSE,
  ...
)

Arguments

Details

plot_route() requires a a Stadia Maps API key. See register_stadiamaps for details.

See Also

get_stadiamap, ggmap

Examples

## Not run: 
data('runs', package = 'trackeR')
plot_route(runs, session = 4, zoom = 13)
plot_route(runs, session = 4, zoom = 13, maptype = "outdoors")
## multiple sessions
plot_route(runs, session = c(1:4, 8:11))
## different zoom level per panel
plot_route(runs, session = 6:7, zoom = c(13, 14))

## End(Not run)

Prepare a data.frame for use in leaflet_route and plot_route

Description

Prepare a data.frame for use in leaflet_route and plot_route

Usage

prepare_route(x, session = 1, threshold = TRUE, ...)

Arguments

Details

To be used internally in mapping function and rarely by the user.

Value

A data.frame with variables longitude, latitude, speed, SessionID, longitude0, longitude1, latitude0, latitude1. The observations are ordered according to the timestamp they have in x. A suffix of 0 indicates 'start' and a suffix of 1 indicates 'end' at any given observation.

Returns 'pretty' units for use for plotting or printing

Description

Returns 'pretty' units for use for plotting or printing

Usage

prettifyUnit(unit)

prettifyUnits(unit)

Arguments

Details

prettifyUnits is the vectorized version of prettifyUnit

Examples

prettifyUnit("m_per_s")
prettifyUnit("rev_per_min")
prettifyUnits(c("rev_per_min", "ft_per_min"))

print method for trackeRdata objects

Description

print method for trackeRdata objects

Usage

## S3 method for class 'trackeRdata'
print(x, duration_unit = "h", digits = 2, ...)

Arguments

Details

The print method returns training coverage, number of sessions and total training duration from the data in the trackeRdata object.

Print method for session summaries.

Description

Print method for session summaries.

Usage

## S3 method for class 'trackeRdataSummary'
print(x, ..., digits = 2)

Arguments

Transform distribution and concentration profiles to functional data objects of class fd.

Description

Transform distribution and concentration profiles to functional data objects of class fd.

Usage

profile2fd(object, what, ...)

Arguments

Value

An object of class fd.

Examples

## Not run: 
library('fda')
data('runs', package = 'trackeR')
dp <- distributionProfile(runs, what = 'speed')
dpFun <- profile2fd(dp, what = 'speed',
    fdnames = list('speed', 'sessions', 'time above threshold'))
dp.pca <- pca.fd(dpFun, nharm = 4)
## 1st harmonic  captures vast majority of the variation
dp.pca$varprop
## time spent above speed = 0 is the characteristic distinguishing the profiles
plot(dp.pca, harm = 1)
sumRuns <- summary(runs)
plot(sumRuns$durationMoving, dp.pca$scores[,1])

## End(Not run)

Read a training file in tcx, gpx, db3 or Golden Cheetah's JSON format

Description

Read a training file in tcx, gpx, db3 or Golden Cheetah's JSON format

Usage

readTCX(file, timezone = "", speedunit = "m_per_s", distanceunit = "m", ...)

readGPX(file, timezone = "", speedunit = "km_per_h", distanceunit = "km", ...)

readDB3(
  file,
  timezone = "",
  table = "gps_data",
  speedunit = "km_per_h",
  distanceunit = "km",
  ...
)

readJSON(file, timezone = "", speedunit = "km_per_h", distanceunit = "km", ...)

Arguments

Details

Available options for speedunit currently are km_per_h, m_per_s, mi_per_h, ft_per_min and ft_per_s. The default is m_per_s for TCX files and km_per_h for db3 and Golden Cheetah's json files. Available options for distanceunit currently are km, m, mi and ft. The default is m for TCX and km for gpx, db3 and Golden Cheetah's json files.

readTCX, readGPX, readGPX and readDB3, try to identify the sport from the data in the container file. If that fails, then an attempt is made to guess the sport from keywords in the filename. If identification is not possible then the file attribute of the returned object has value NA.

Reading Golden Cheetah's JSON files is experimental.

Examples

## read raw data
filepath <- system.file("extdata/tcx", "2013-06-08-090442.TCX.gz", package = "trackeR")
run0 <- readTCX(file = filepath, timezone = "GMT")

## turn into trackeRdata object
units0 <- generate_units()
run0 <- trackeRdata(run0, units = units0)

## alternatively
## Not run: 
run0 <- read_container(filepath, type = "tcx", timezone = "GMT")

## End(Not run)

Read a GPS container file.

Description

Read a GPS container file.

Usage

read_container(
  file,
  type = c("tcx", "gpx", "db3", "json"),
  table = "gps_data",
  timezone = "",
  session_threshold = 2,
  correct_distances = FALSE,
  smooth_elevation_gain = TRUE,
  country = NULL,
  mask = TRUE,
  from_distances = NULL,
  speedunit = NULL,
  distanceunit = NULL,
  sport = NULL,
  lgap = 30,
  lskip = 5,
  m = 11,
  silent = FALSE
)

readContainer(
  file,
  type = c("tcx", "gpx", "db3", "json"),
  table = "gps_data",
  timezone = "",
  session_threshold = 2,
  correct_distances = FALSE,
  smooth_elevation_gain = TRUE,
  country = NULL,
  mask = TRUE,
  from_distances = NULL,
  speedunit = NULL,
  distanceunit = NULL,
  sport = NULL,
  lgap = 30,
  lskip = 5,
  m = 11,
  silent = FALSE
)

Arguments

Details

Available options for speedunit currently are km_per_h, m_per_s, mi_per_h, ft_per_min and ft_per_s. Available options for distanceunit currently are km, m, mi and ft.

read_container try to identify the sport from the data in the container file. If that fails, then an attempt is made to guess the sport from keywords in the filename. If identification is not possible then an error is returned from trackeRdata. To avoid that error, and if the sport is known, append an appropriate keyword to the filename (e.g. 'ride', 'swim', 'run'). This should fix the error.

Value

An object of class trackeRdata.

See Also

trackeRdata, readTCX, readDB3, readJSON

Examples

filepath <- system.file("extdata/tcx", "2013-06-08-090442.TCX.gz", package = "trackeR")
run <- read_container(filepath, type = "tcx", timezone = "GMT")

Read all supported container files from a supplied directory

Description

Read all supported container files from a supplied directory

Usage

read_directory(
  directory,
  aggregate = FALSE,
  table = "gps_data",
  timezone = "",
  session_threshold = 2,
  smooth_elevation_gain = TRUE,
  correct_distances = FALSE,
  country = NULL,
  mask = TRUE,
  from_distances = NULL,
  speedunit = list(tcx = "m_per_s", gpx = "km_per_h", db3 = "km_per_h", json =
    "km_per_h"),
  distanceunit = list(tcx = "m", gpx = "km", db3 = "km", json = "km"),
  sport = NULL,
  lgap = 30,
  lskip = 5,
  m = 11,
  silent = FALSE,
  parallel = FALSE,
  verbose = TRUE
)

readDirectory(
  directory,
  aggregate = FALSE,
  table = "gps_data",
  timezone = "",
  session_threshold = 2,
  smooth_elevation_gain = TRUE,
  correct_distances = FALSE,
  country = NULL,
  mask = TRUE,
  from_distances = NULL,
  speedunit = list(tcx = "m_per_s", gpx = "km_per_h", db3 = "km_per_h", json =
    "km_per_h"),
  distanceunit = list(tcx = "m", gpx = "km", db3 = "km", json = "km"),
  sport = NULL,
  lgap = 30,
  lskip = 5,
  m = 11,
  silent = FALSE,
  parallel = FALSE,
  verbose = TRUE
)

Arguments

Details

Available options for speedunit currently are km_per_h, m_per_s, mi_per_h, ft_per_min and ft_per_s. Available options for distanceunit currently are km, m, mi and ft.

If aggregate = TRUE, then if sport = NULL the sport in all sessions is determined by the first file read with a sport specification; else if sport is one of the other valid options it determines the sport for all sessions.

Value

An object of class trackeRdata.

See Also

trackeRdata, readTCX, readDB3, readJSON

Examples

## Not run: 
filepath <- system.file("extdata/gpx", package = "trackeR")
gpx_files <- read_directory(filepath)

## End(Not run)

Generic function for ridgeline plots

Description

Generic function for ridgeline plots

Usage

ridges(x, ...)

Arguments

See Also

ridges.trackeRdata ridges.conProfile ridges.distrProfile

Ridgeline plots for distrProfile objects

Description

Ridgeline plots for distrProfile objects

Usage

## S3 method for class 'conProfile'
ridges(x, session = NULL, what = NULL, smooth = FALSE, ...)

Arguments

Examples

## Not run: 

data('runs', package = 'trackeR')
dProfile <- distributionProfile(runs, what = c('speed', 'heart_rate'))
cProfile <- concentrationProfile(dProfile)
ridges(cProfile, what = "speed")
ridges(cProfile, what = "heart_rate")

## End(Not run)

Ridgeline plots for distrProfile objects

Description

Ridgeline plots for distrProfile objects

Usage

## S3 method for class 'distrProfile'
ridges(x, session = NULL, what = NULL, smooth = FALSE, ...)

Arguments

Examples

## Not run: 

data('runs', package = 'trackeR')
dProfile <- distribution_profile(runs, what = c("speed", "heart_rate"))
ridges(dProfile)


## End(Not run)

Ridgeline plots for trackeRdata objects

Description

Ridgeline plots for trackeRdata objects

Usage

## S3 method for class 'trackeRdata'
ridges(x, session = NULL, what = "speed", smooth = TRUE, ...)

Arguments

Examples

## Not run: 
data('runs', package = 'trackeR')
ridges(runs)

## End(Not run)

Training session.

Description

Training session.

Usage

run

Format

A trackeRdata object containing one running training session.

Training sessions.

Description

Training sessions.

Usage

runs

Format

A trackeRdata object containing 33 running training sessions.

Sanity checks for tracking data

Description

Heart rate measurements of 0 are set to NA, assuming the athlete is alive. Observations with missing or duplicated time stamps are removed.

Usage

sanity_checks(dat, silent)

Arguments

Generic function for scaling

Description

Generic function for scaling

Usage

scaled(object, ...)

Arguments

Scale the distribution profile relative to its maximum value.

Description

Scale the distribution profile relative to its maximum value.

Usage

## S3 method for class 'distrProfile'
scaled(object, session = NULL, what = NULL, ...)

Arguments

Generic function for calculating session durations

Description

Generic function for calculating session durations

Usage

session_duration(object, session, duration_unit, ...)

## S3 method for class 'trackeRdata'
session_duration(object, session = NULL, duration_unit = "h", ...)

## S3 method for class 'trackeRdataSummary'
session_duration(object, session = NULL, ...)

Arguments

Details

The times units will be inherited from object.

Generic function for calculating session times

Description

Generic function for calculating session times

Usage

session_times(object, session, duration_unit, ...)

## S3 method for class 'trackeRdata'
session_times(object, session = NULL, ...)

## S3 method for class 'trackeRdataSummary'
session_times(object, session = NULL, ...)

Arguments

Generic function for smoothing

Description

Generic function for smoothing

Usage

smoother(object, ...)

Arguments

Smoother for concentration profiles.

Description

To ensure positivity of the smoothed concentration profiles, the concentration profiles are transformed to distribution profiles before smoothing. The smoothed distribution profiles are then transformed to concentration profiles.

Usage

## S3 method for class 'conProfile'
smoother(object, session = NULL, what = NULL, control = list(...), ...)

Arguments

See Also

smoother_control.distrProfile

Smoother for distribution profiles.

Description

The distribution profiles are smoothed using a shape constrained additive model with Poisson responses to ensure that the smoothed distribution profile is positive and monotone decreasing.

Usage

## S3 method for class 'distrProfile'
smoother(object, session = NULL, what = NULL, control = list(...), ...)

Arguments

References

Kosmidis, I., and Passfield, L. (2015). Linking the Performance of Endurance Runners to Training and Physiological Effects via Multi-Resolution Elastic Net. ArXiv e-print arXiv:1506.01388.

Pya, N. and Wood S. (2015). Shape Constrained Additive Models. Statistics and Computing, 25(3), 543–559. Frick, H.,

Kosmidis, I. (2017). trackeR: Infrastructure for Running and Cycling Data from GPS-Enabled Tracking Devices in R. Journal of Statistical Software, 82(7), 1–29. doi:10.18637/jss.v082.i07

See Also

smoother_control.distrProfile

Smoother for trackeRdata objects.

Description

Smoother for trackeRdata objects.

Usage

## S3 method for class 'trackeRdata'
smoother(object, session = NULL, control = list(...), ...)

Arguments

Value

An object of class trackeRdata.

See Also

smoother_control.trackeRdata

Examples

## Not run: 
data('run', package = 'trackeR')
## unsmoothed speeds
plot(run, smooth = FALSE)
## default smoothing
plot(run, smooth = TRUE)
## smoothed with some non-default options
runS <- smoother(run, fun = 'median', width = 20, what = 'speed')
plot(runS, smooth = FALSE)

## End(Not run)

Auxiliary function for smoother.distrProfile. Typically used to construct a control argument for smoother.distrProfile.

Description

Auxiliary function for smoother.distrProfile. Typically used to construct a control argument for smoother.distrProfile.

Usage

smoother_control.distrProfile(k = 30, sp = NULL, parallel = FALSE)

smootherControl.distrProfile(k = 30, sp = NULL, parallel = FALSE)

Arguments

Auxiliary function for smoother.trackeRdata. Typically used to construct a control argument for smoother.trackeRdata.

Description

Auxiliary function for smoother.trackeRdata. Typically used to construct a control argument for smoother.trackeRdata.

Usage

smoother_control.trackeRdata(
  fun = "mean",
  width = 10,
  parallel = FALSE,
  what = c("speed", "heart_rate"),
  nsessions = NA,
  ...
)

smootherControl.trackeRdata(
  fun = "mean",
  width = 10,
  parallel = FALSE,
  what = c("speed", "heart_rate"),
  nsessions = NA,
  ...
)

Arguments

See Also

smoother.trackeRdata

Sort sessions in trackeRdata objects

Description

Sort the sessions trackeRdata objects into ascending or descending order according to the first session timestamp.

Usage

## S3 method for class 'trackeRdata'
sort(x, decreasing = FALSE, ...)

Arguments

Convert speed to distance.

Description

Convert speed to distance.

Usage

speed2distance(speed, time, timeunit, cumulative = TRUE)

Arguments

Value

Distance in meters.

Summary of training sessions

Description

Summary of training sessions

Usage

## S3 method for class 'trackeRdata'
summary(
  object,
  session = NULL,
  moving_threshold = NULL,
  unit_reference_sport = NULL,
  ...
)

Arguments

Details

The default speed thresholds are 1 m/s for running (3.6 km/h; slow walking), 2 m/s for cycling (7.2 km/h) for cycling and 0.5 m/s (1.8km/h) for swimming. For reference, the preferred walking speed for humans is around 1.4 m/s (Bohannon, 1997).

The units for the computed summaries match those of the sport specified by unit_reference_sport.

If object has thresholds then the thresholds that match those of the sport specified by unit_reference_sport are applied to the respective summaries.

Value

An object of class trackeRdataSummary.

References

Bohannon RW (1997). 'Comfortable and Maximum Walking Speed of Adults Aged 20–79 Years: Reference Values and Determinants.' Age and Ageing, 26(1), 15–19. doi: 10.1093/ageing/26.1.15.

See Also

plot.trackeRdataSummary

Examples

data('runs', package = 'trackeR')
runSummary <- summary(runs, session = 1:2)
## print summary
runSummary
print(runSummary, digits = 3)
## Not run: 
## change units
change_units(runSummary, variable = 'speed', unit = 'km_per_h')
## plot summary
runSummaryFull <- summary(runs)
plot(runSummaryFull)
plot(runSummaryFull, group = c('total', 'moving'),
    what = c('avgSpeed', 'distance', 'duration', 'avgHeartRate', "total_elevation_gain"))

## End(Not run)

Thresholding for variables in trackeRdata objects

Description

Thresholding for variables in trackeRdata objects

Usage

## S3 method for class 'trackeRdata'
threshold(object, variable, lower, upper, sport, trace = FALSE, ...)

threshold(object, ...)

Arguments

Details

lower and upper are always understood as referring to the units of the object.

If the arguments variable, lower, and upper are all unspecified, the following default thresholds are employed

• latitude [-90, 90] degrees

• longitude [-180, 180] degrees

• altitude [-500, 9000] m

• distance [0, Inf] meters

• cadence_running [0, Inf] steps per min

• cadence_cycling [0, Inf] revolutions per min

• speed [0, Inf] meters

• heart rate [0, 250] bpm

• power [0, Inf] W

• pace [0, Inf] min per km

• duration [0, Inf] seconds

• temperature [-20, 60] C

after they have been transformed to the units of the object

The thresholds for speed differ across sports: for running they are [0, 12.5] meters per second, for cycling [0, 100] meters per second and for swimming [0, 5] meters per second.

Examples

## Not run: 
data('runs', package = 'trackeR')
plot(runs, session = 4, what = 'speed', threshold = FALSE)
runsT <- threshold(runs, variable = 'speed', lower = 0, upper = 12.5, sport = "running")
plot(runsT, session = 4, what = 'speed', threshold = FALSE)

## End(Not run)

Time spent above a certain threshold.

Description

Time spent above a certain threshold.

Usage

timeAboveThreshold(object, threshold = -1, ge = TRUE)

Arguments

Generic function for visualising the sessions on a time versus date plot

Description

Generic function for visualising the sessions on a time versus date plot

Timeline plot for trackeRdata objects.

Timeline plot for trackeRdataSummary objects

Usage

timeline(object, lims, ...)

## S3 method for class 'trackeRdata'
timeline(object, lims = NULL, ...)

## S3 method for class 'trackeRdataSummary'
timeline(object, lims = NULL, ...)

Arguments

Examples

## Not run: 
data('runs', package = 'trackeR')
## timeline plot applied on the \code{trackeRdata} object directly and with
## inferred limits for the time axis
timeline(runs)

## the same timeline plot applied on the \code{trackeRdataSummary} object
runSummary <- summary(runs)
timeline(runSummary, lims = c('00:01', '23:59'))

## End(Not run)

trackeR: Infrastructure for running and cycling data from GPS-enabled tracking devices

Description

trackeR provides infrastructure for handling cycling and running data from GPS-enabled tracking devices. After extraction and appropriate manipulation of the training or competition attributes, the data are placed into session-aware data objects with an S3 class trackeRdata. The information in the resultant data objects can then be visualised, summarised and analysed through corresponding flexible and extensible methods.

Note

Core facilities in the trackeR package, including reading functions (see readX), data pre-processing strategies (see trackeRdata), and calculation of concentration and distribution profiles (see distributionProfile and concentrationProfile) are based on un-packaged R code that was developed by Ioannis Kosmidis for the requirements of the analyses in Kosmidis & Passfield (2015).

Note

This work has been supported by the English Institute of Sport (currently UK Sports Institute) https://uksportsinstitute.co.uk and University College London (UCL), which jointly contributed to the grant that funded Hannah Frick's Post Doctoral Research Fellowship at UCL between 2014 and 2016 and a percentage of Ioannis Kosmidis' time. Ioannis Kosmidis has also been supported by the Alan Turing Institute under the EPSRC grant EP/N510129/1 (Turing award number TU/B/000082). The support of the aforementioned organisations is greatly acknowledged.

Hannah Frick maintained trackeR from its first release up and since version 1.0.0.

References

Frick, H., Kosmidis, I. (2017). trackeR: Infrastructure for Running and Cycling Data from GPS-Enabled Tracking Devices in R. Journal of Statistical Software, 82(7), 1–29. doi:10.18637/jss.v082.i07

Kosmidis, I., and Passfield, L. (2015). Linking the Performance of Endurance Runners to Training and Physiological Effects via Multi-Resolution Elastic Net. ArXiv e-print arXiv:1506.01388.

Create a trackeRdata object

Description

Create a trackeRdata object from a data frame with observations being divided in separate training sessions. For breaks within a session observations are imputed.

Usage

trackeRdata(
  dat,
  units = NULL,
  sport = NULL,
  session_threshold = 2,
  correct_distances = FALSE,
  smooth_elevation_gain = TRUE,
  from_distances = TRUE,
  country = NULL,
  mask = TRUE,
  lgap = 30,
  lskip = 5,
  m = 11,
  silent = FALSE
)

Arguments

Details

During small breaks within a session, e.g., because the recording device was paused, observations are imputed the following way: 0 for speed, last known position for latitude, longitude and altitude, NA or 0 power for running or cycling session, respectively, and NA for all other variables. Distances are (re-)calculated based on speeds after imputation.

trackeRdata assumes that all observations in dat are from the same sport, even if dat ends up having observations from different sessions (also depending on the value of session_threshold.

if attr(dat, 'sport') is NA then the current implementation of trackeRdata returns an error.

More details about the resulting trackeRdata object are available in the package vignette, which is an up-to-date version of Frick & Kosmidis (2017).

References

Frick, H., Kosmidis, I. (2017). trackeR: Infrastructure for Running and Cycling Data from GPS-Enabled Tracking Devices in R. Journal of Statistical Software, 82(7), 1–29. doi:10.18637/jss.v082.i07

See Also

readContainer for reading .tcx and .db3 files directly into trackeRdata objects, and get_elevation_gain for details on the computation of the elevation gain.

Examples

## read raw data
filepath <- system.file('extdata/tcx/', '2013-06-08-090442.TCX.gz', package = 'trackeR')
run0 <- readTCX(file = filepath, timezone = 'GMT')

## turn into trackeRdata object
units0 <- generate_units()
run0 <- trackeRdata(run0, units = units0)

Extract unique sessions in a trackerRdata object

Description

Extract unique sessions in a trackerRdata object

Usage

## S3 method for class 'trackeRdata'
unique(x, incomparables = FALSE, ...)

Arguments

Details

Uniqueness is determined by comparing the first timestamp of the sessions in the trackeRdata object.

Time spent in training zones.

Description

Time spent in training zones.

Usage

zones(
  object,
  session = NULL,
  what = c("speed"),
  breaks = NULL,
  parallel = FALSE,
  n_zones = 9,
  unit_reference_sport = NULL,
  ...
)

Arguments

Value

An object of class trackeRdataZones.

See Also

plot.trackeRdataZones

Examples

data('run', package = 'trackeR')
runZones <- zones(run, what = 'speed', breaks = list(speed = c(0, 2:6, 12.5)))
## if breaks is a named list, argument 'what' can be left unspecified
runZones <- zones(run, breaks = list(speed = c(0, 2:6, 12.5)))
## if only a single variable is to be evaluated, 'breaks' can also be a vector
runZones <- zones(run, what = 'speed', breaks = c(0, 2:6, 12.5))
plot(runZones)