--- title: "Analysing Cycling Data with R" author: "Jordan Mackie" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Cycling Data Analysis} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, include=FALSE, echo=FALSE} knitr::opts_chunk$set(fig.width = 7, fig.height = 5) options(digits = 2) ``` `cycleRtools` is a package intended to open up the power of R to cyclists and those interested in cycling data. Functions are provided for reading raw data files into the R environment--a signficant barrier in any case--as well as several specialist functions that are provided by other, often proprietary platforms. **** ```{r, echo=FALSE} library(cycleRtools) ``` # Reading in data For these examples we'll be using a preloaded dataset, named `intervaldata` (i.e `data(intervaldata)`). The corresponding Garmin .fit file is provided in the `extdata` directory of this package, tarballed together with other files of various formats. See `?read_ride` and the example therein. Briefly, if `intervaldata.fit` is the file name, then this dataset was generated via: ```r intervaldata <- read_ride("intervaldata.fit", format = TRUE, CP = 310, # Critical power. sRPE = 7) # Session RPE. ``` Note that `read_ride()` is a generic wrapper for all `read_*` functions in this package (`read_fit()`, `read_srm()` etc...) and calls the appropriate function according to file extension. ## CP and sRPE arguments When these (numeric) arguments are supplied, they are associated with the parsed data and subsequently used by other functions in this package; they can also serve as useful records should the object be saved as part of ongoing training monitoring. They correspond to "**C**ritical **P**ower" and "**s**ession **RPE**", respectively. For more information on these metrics see Jones *et al.*, 2010 and Foster *et al.*, 2001. ## The 'cycleRdata' format Calling any `read_*` function with the argument `format = TRUE` will generate a data.frame-type object of class `cycleRdata`. The creation of this class was deemed necessary so that certain columns could be assumed to exist in the data; thus ultimately making life more straightforward for the user. See `?cycleRdata` for details of the format. **** # Exploring the data It goes without saying that once these cycling data are within the R environment, endless analytical procedures become available. For the purposes of this demonstration, only some of those procedures facilitated by this package are shown. ## Visualising the data As is customary, data should first be plotted. `cycleRdata` objects have an associated `plot` method: ```{r, fig.height = 10} plot(x = intervaldata, # "x" is the data, for consistency with other methods. y = 1:3, # Which plots should be created? see below. xvar = "timer.min", # What should be plotted on the x axis? xlab = "Time (min)", # x axis label. laps = TRUE, # Should different laps be coloured? breaks = TRUE) # Should stoppages in the ride be shown? ``` The `y` argument for this `plot` method specifies the way in which the plot stack should be generated. Specifically `length(y)` determines the number of plots to be stacked, and the combination of `c(1, 2, 3)` controls what is plotted and where. As is described in `?plot.cycleRdata`, numbers in this argument give: 1. A W' balance plot (Skiba et al., 2012). 2. A power plot. 3. An elevation plot. So for example: ```r plot(intervaldata, y = c(3:1)) # Inverts the above plot. plot(intervaldata, y = 2) # Just plots power. plot(intervaldata, y = c(1, 3)) # W' balance over elevation. ``` Plots can also be zoomed, and the title metric will be adjusted accordingly. ```{r} ## Zoom to 0-50 minutes. plot(intervaldata, y = 3, xvar = "timer.min", xlim = c(0, 50)) ``` ## Time in zones Another want of cyclists is the ability to analyse "time in zones". This is provided primarily by the functions `zone_time()` and `zdist_plot()`. ### zone_time() ```{r} zone_time(data = intervaldata, column = power.W, # What are we interested in? zbounds = c(100, 200, 300), # Zone boundaries. pct = FALSE) / 60 # Output in minutes. ## How about time above and below CP, as a percentage? ## NB: column = power.W is the default. zone_time(intervaldata, zbounds = 310, pct = TRUE) ``` ### zdist_plot() ```{r} zdist_plot(data = intervaldata, binwidth = 10, # 10 Watt bins. zbounds = c(100, 200, 300), # Zone boundaries. xlim = c(50, 400)) # Zoom to 50-400 Watts. ``` ## Summarising the data `cycleRdata` objects have a `summary` method, which calculates common metrics and will produce a lap and/or interval summary as appropriate. ```{r} summary(intervaldata) ``` Also see `?summary_metrics` for other common metrics. ### Power profiling Best mean powers for specified durations can be generated via the `mmv()` function. This will return both the best recorded values, as well as when those were recorded in the ride (seconds). ```{r} times_sec <- 2:20 * 60 # 2-20 minutes. prof <- mmv(data = intervaldata, column = power.W, # Could also use speed.kmh. windows = times_sec) print(prof) ``` This returns the usual, hyperbolic power-time profile. ```{r} hypm <- lm(prof[1, ] ~ {1 / times_sec}) # Hyperbolic model. ## Critical Power (Watts) and W' (Joules) estimates hypm <- setNames(coef(hypm), c("CP", "W'")) print(hypm) ## Plot with the inverse model overlaid. plot(times_sec, prof[1, ], ylim = c(hypm["CP"], max(prof[1, ])), xlab = "Time (sec)", ylab = "Power (Watts)") curve((hypm["W'"] / x) + hypm["CP"], add = TRUE, col = "red") abline(h = hypm["CP"], lty = 2) legend("topright", legend = c("Model", "CP"), bty = "n", lty = c(1, 2), col = c("red", "black")) ``` ### Pt_model() More sophisticated modelling is provided by the `Pt_model` function; see `?Pt_model`. Briefly: ```{r} ms <- Pt_model(prof[1, ], times_sec) print(ms) plot(times_sec, prof[1, ], ylim = c(hypm["CP"], max(prof[1, ])), xlab = "Time (sec)", ylab = "Power (Watts)") ## Showing an exponential model, as it best fits these data. curve(ms$Pfn$exp(x), add = TRUE, col = "red") ``` ## Mapping Viewing a route map is simple thanks to the `leaflet` package. ```{r, fig.height=3} library(leaflet) leaflet(intervaldata) %>% addTiles() %>% addPolylines(~lon, ~lat) ``` # References
Foster C, Florhaug JA, Franklin J, Gottschall L, Hrovatin LA, Parker S, Doleshal P, Dodge C. A New Approach to Monitoring Exercise Training. *Journal of Strength and Conditioning Research* 15: 109–115, 2001.
Jones AM, Vanhatalo A, Burnley M, Morton RH, Poole DC. Critical Power: Implications for Determination of VO2max and Exercise Tolerance. *Medicine & Science in Sports & Exercise* 42: 1876–1890, 2010.
Skiba PF, Chidnok W, Vanhatalo A, Jones AM. Modeling the Expenditure and Reconstitution of Work Capacity above Critical Power. *Medicine & Science in Sports & Exercise* 44: 1526–1532, 2012.