trackeR: trackeR: Infrastructure for running and cycling data from...

In trackeR: Infrastructure for Running, Cycling and Swimming Data from GPS-Enabled Tracking Devices

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.

trackeR documentation built on May 29, 2024, 5:04 a.m.