trackeR

Description

The purpose of this package is to provide infrastructure for handling running, cycling, and swimming data from GPS-enabled tracking devices.

The formats that are currently supported for the training activity files are .tcx (Training Center XML), Strava .gpx, .db3 and Golden Cheetah .json files. After extraction and appropriate manipulation of the training or competition attributes, the data are placed into session-based and unit-aware data objects of class trackeRdata (S3 class). The information in the resultant data objects can then be visualised, summarised, and analysed through corresponding flexible and extensible methods.

Current capabilities

Read:

• Read data from .tcx, Strava .gpx, .db3 or Golden Cheetah .json files.
• Read all supported files in a specified directory.

Sports supported:

• Running
• Cycling
• Swimming

Data processing:

• Automatically identify sessions from timestamps.
• Imputation of data to characterise times when the device is paused or remains stationary.
• Correction of GPS-measured distances using elevation data.
• Basic data cleaning capabilities e.g., no negative speeds or distances.
• Specify and conveniently change units of measurement.
• Organise data into session-based and unit-aware data objects of class trackeRdata.

Analysis:

• Session summaries: distance, duration, time moving, average speed/pace/heart rate/cadence/power (overall and moving), work to rest ratio, temperature.
• Time spent exercising in user-supplied zones, e.g., heart rate zones or speed zones.
• Work capacity above critical power (W’, W prime)
• Distribution profiles: time spent exercising above thresholds of training attributes.
• Concentration profiles: negative derivatives of distribution profiles.
• Functional principal components analysis of distribution and concentration profiles.

Visualisation:

• Plot session progression in, e.g., pace, heart rate, etc.
• Plot route covered during session on static and interactive maps from various providers.
• Plot session summary statistics.
• Plot date time of sessions in timeline plots.
• Plot time spent exercising in zones.
• Plot distribution/concentration profiles.
• Plot principal components of distribution/concentration profiles.
• Ridgeline (or joy) plots for distribution/concentration profiles.

Installation

Install the released version from CRAN:

install.packages("trackeR")

Or the development version from github:

# install.packages("devtools")
devtools::install_github("trackerproject/trackeR")

Example

Plot workout data

data(runs, package = "trackeR")
plot(runs, session = 1:5, what = c("speed", "pace", "altitude"))

Change the units

data(runs, package = "trackeR")
runs0 <- change_units(runs,
                      variable = c("speed", "altitude"),
                      unit = c("km_per_h", "ft"),
                      sport = c("running", "running"))
plot(runs0, session = 1:5, what = c("speed", "pace", "altitude"))

Summarise sessions

library("trackeR")
runs_summary <- summary(runs)
plot(runs_summary, group = c("total", "moving"),
    what = c("avgSpeed", "distance", "duration", "avgHeartRate"))

Generate distribution and concentration profiles

runsT <- threshold(runs)
dp_runs <- distribution_profile(runsT, what = c("speed", "heart_rate"))
dp_runs_smooth <- smoother(dp_runs)
cp_runs <- concentration_profile(dp_runs_smooth)
plot(cp_runs, multiple = TRUE, smooth = FALSE)

A ridgeline plot of the concentration profiles

ridges(cp_runs, what = "speed")

ridges(cp_runs, what = "heart_rate")

Explore concentration profiles for speed, e.g., via functional principal components analysis (PCA)

## fit functional PCA
cp_PCA <- funPCA(cp_runs, what = "speed", nharm = 4)

## pick first 2 harmonics/principal components
round(cp_PCA$varprop, 2)

## [1] 0.66 0.25 0.06 0.02

## plot harmonics
plot(cp_PCA, harm = 1:2)

## plot scores vs summary statistics
scores_SP <- data.frame(cp_PCA$scores)
names(scores_SP) <- paste0("speed_pc", 1:4)
d <- cbind(runs_summary, scores_SP)

library("ggplot2")
## pc1 ~ session duration (moving)
ggplot(d) + geom_point(aes(x = as.numeric(durationMoving), y = speed_pc1)) + theme_bw()

## pc2 ~ avg speed (moving)
ggplot(d) + geom_point(aes(x = avgSpeedMoving, y = speed_pc2)) + theme_bw()