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.
Install the released version from CRAN:
Or the development version from github:
# install.packages("devtools") devtools::install_github("trackerproject/trackeR")
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"))
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$varprop, 2) ##  0.66 0.25 0.06 0.02 ## plot harmonics plot(cp_PCA, harm = 1:2)
## plot scores vs summary statistics scores_SP <- data.frame(cp_PCA$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()
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