trackerR
2023-02-10
Objective
The trackeR package aims to fill the gap between the routine
collection of data from GPSenabled tracking devices and the
analyses of such data within the R ecosystem.
- The package provides utilities to import sports data from GPS-enabled devices, and, after careful processing, organises them in data objects which are organised in separate sessions/workouts and carry information about the units of measurement (e.g., distance and speed units) as well as of any data operations that have been carried out (e.g., smoothing).
- The package also implements core infrastructure for the handling of measurement units and for summarising and visualising tracking data. It also provides functionality for calculating time in zones (e.g., Seiler and Kjerland 2006), work capacity W′ (Skiba, Chidnok, Vanhatalo, and Jones 2012), and distribution and concentration profiles (Kosmidis and Passfield 2015), including a few methods for the analysis of these profiles
library(trackeR)## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric##
## Attaching package: 'trackeR'## The following object is masked from 'package:base':
##
## appendfilepath <- system.file("extdata/tcx", "2013-06-01-183220.TCX.gz", package = "trackeR") # data from training centre XML (tcx data)
runDF <- readTCX(file = filepath, timezone = "GMT") # reading a single file of TCX
str(runDF)## 'data.frame': 3881 obs. of 11 variables:
## $ time : POSIXct, format: "2013-06-01 17:32:20" "2013-06-01 17:32:21" ...
## $ latitude : num 50.8 50.8 50.8 50.8 50.8 ...
## $ longitude : num -1.7 -1.7 -1.7 -1.7 -1.7 ...
## $ altitude : num 83.4 83.8 84 83.8 83.6 ...
## $ distance : num 1.26 3.3 7.12 11.12 16.76 ...
## $ heart_rate : num 56 61 61 71 71 74 74 85 85 85 ...
## $ speed : num 0.885 1.209 1.801 2.205 2.756 ...
## $ cadence_running: num 60 63 70 78 83 84 84 85 85 86 ...
## $ cadence_cycling: logi NA NA NA NA NA NA ...
## $ power : logi NA NA NA NA NA NA ...
## $ temperature : logi NA NA NA NA NA NA ...
## - attr(*, "sport")= chr "running"
## - attr(*, "file")= chr "C:/Users/linhp/App/R/R-4.2.2/library/trackeR/extdata/tcx/2013-06-01-183220.TCX.gz"runTr0 <- trackeRdata(runDF) # contruct the trackerdata object ## Warning in sanity_checks(dat = dat, silent = silent): Observations with
## duplicated time stamps have been removed.runTr0## A trackeRdata object
## Sports: running
##
## Training coverage: from 2013-06-01 18:32:15 to 2013-06-01 19:37:56
## Number of sessions: 1
## Training duration: 1.09 h
##
## Units
##
## latitude degree cycling
## longitude degree cycling
## altitude m cycling
## distance m cycling
## heart_rate bpm cycling
## speed m_per_s cycling
## cadence_cycling rev_per_min cycling
## power W cycling
## temperature C cycling
## pace min_per_km cycling
## duration min cycling
## latitude degree running
## longitude degree running
## altitude m running
## distance m running
## heart_rate bpm running
## speed m_per_s running
## cadence_running steps_per_min running
## temperature C running
## pace min_per_km running
## duration min running
## latitude degree swimming
## longitude degree swimming
## altitude m swimming
## distance m swimming
## heart_rate bpm swimming
## speed m_per_s swimming
## temperature C swimming
## pace min_per_km swimming
## duration min swimmingreadContainer - function is used to call single file
runTr1 <- readContainer(filepath, type = "tcx", timezone = "GMT") #read single file only - read container ## Warning in sanity_checks(dat = dat, silent = silent): Observations with
## duplicated time stamps have been removed.identical(runTr0, runTr1)## [1] TRUEgpxDir <- system.file("extdata/gpx", package = "trackeR") #Data from strava (data gpx)
workouts <- readDirectory(gpxDir, verbose = FALSE) # Reading multiple file in a Directory
get_sport(workouts)## [1] "running" "cycling" "swimming"The function readDirectory() allows the user to read all
files of a supported format in a directory, rather than calling, e.g.,
readContainer() on each file separately
trackeR data ship into 2 kinds of data (1 session and 27 sessions)
test1 <- data("run", package = "trackeR")
test2 <- data("runs", package = "trackeR")
data("run", package = "trackeR")
data("runs", package = "trackeR")Data Visualisation
plot(runs, session = 1:3)
plotRoute(runs, session = 4, zoom = 13)## ℹ Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL.
# Scalar Summary
Moving thershold <- definition on it: move or not
based on speed (moving or resting)
Summary each session with a number of KPIs
summary(runs, session = 1:2)##
## *** Session 1 : running ***
##
## Session times: 2013-06-01 18:32:15 - 2013-06-01 19:37:56
## Distance: 14130.7 m
## Duration: 65.68 mins
## Moving time: 64.17 mins
## Average speed: 3.59 m_per_s
## Average speed moving: 3.67 m_per_s
## Average pace (per 1 km): 4:38 min:sec
## Average pace moving (per 1 km): 4:32 min:sec
## Average cadence running: 88.66 steps_per_min
## Average cadence cycling: NA rev_per_min
## Average cadence running moving: 88.87 steps_per_min
## Average cadence cycling moving: NA rev_per_min
## Average power: NA W
## Average power moving: NA W
## Average heart rate: 141.11 bpm
## Average heart rate moving: 141.13 bpm
## Average heart rate resting: 136.76 bpm
## Average temperature: NA C
## Total elevation gain: 94.2 m
## Work to rest ratio: 42.31
##
## *** Session 2 : running ***
##
## Session times: 2013-06-02 07:23:43 - 2013-06-02 08:09:47
## Distance: 9450.24 m
## Duration: 46.07 mins
## Moving time: 44.13 mins
## Average speed: 3.42 m_per_s
## Average speed moving: 3.57 m_per_s
## Average pace (per 1 km): 4:52 min:sec
## Average pace moving (per 1 km): 4:40 min:sec
## Average cadence running: 88.21 steps_per_min
## Average cadence cycling: NA rev_per_min
## Average cadence running moving: 88.25 steps_per_min
## Average cadence cycling moving: NA rev_per_min
## Average power: NA W
## Average power moving: NA W
## Average heart rate: 139.48 bpm
## Average heart rate moving: 139.44 bpm
## Average heart rate resting: 141.16 bpm
## Average temperature: NA C
## Total elevation gain: 124.52 m
## Work to rest ratio: 22.83
##
## Moving thresholds: 2.0 (cycling) 1.0 (running) 0.5 (swimming) m_per_s
## Unit reference sport: runningsummary(runs, session = 1)##
## *** Session 1 : running ***
##
## Session times: 2013-06-01 18:32:15 - 2013-06-01 19:37:56
## Distance: 14130.7 m
## Duration: 65.68 mins
## Moving time: 64.17 mins
## Average speed: 3.59 m_per_s
## Average speed moving: 3.67 m_per_s
## Average pace (per 1 km): 4:38 min:sec
## Average pace moving (per 1 km): 4:32 min:sec
## Average cadence running: 88.66 steps_per_min
## Average cadence cycling: NA rev_per_min
## Average cadence running moving: 88.87 steps_per_min
## Average cadence cycling moving: NA rev_per_min
## Average power: NA W
## Average power moving: NA W
## Average heart rate: 141.11 bpm
## Average heart rate moving: 141.13 bpm
## Average heart rate resting: 136.76 bpm
## Average temperature: NA C
## Total elevation gain: 94.2 m
## Work to rest ratio: 42.31
##
## Moving thresholds: 2.0 (cycling) 1.0 (running) 0.5 (swimming) m_per_s
## Unit reference sport: runningrunSummaryFull <- summary(runs)
plot(runSummaryFull, group = c("total", "moving")) # not select any metrics 
plot(runSummaryFull, group = c("total", "moving"),
what = c("avgSpeed", "distance", "duration", "avgHeartRate")) # select some metrics to visualise 