README.md

trackeR

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Description

The purpose of this package is to provide infrastructure for handling running and cycling 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’s .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:

Sports supported:

Data processing:

Analysis:

Visualisation:

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()



hfrick/trackeR documentation built on June 5, 2019, 4 p.m.