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 “Critical Power” and “session 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.

Visualising the data

As is customary, data should first be plotted. cycleRdata objects have an associated plot method:

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:

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.

## 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(data = intervaldata,
          column = power.W,           # What are we interested in?
          zbounds = c(100, 200, 300), # Zone boundaries.
          pct = FALSE) / 60           # Output in minutes.

## Zone 1 Zone 2 Zone 3 Zone 4 
##     22     41     39     14

## How about time above and below CP, as a percentage?
## NB: column = power.W is the default.
zone_time(intervaldata, zbounds = 310, pct = TRUE) 

## Zone 1 Zone 2 
##     90     10

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.

summary(intervaldata)

## 
## Date:       05 Jan 2016 
## Ride start: 09:15:08 
## 
## **SUMMARY**
##                      
## ride.time.min     114
## elapsed.time.min  152
## distance.km        52
## work.kJ          1296
## norm.work.kJ     1673
## climbing.m        812
## sRPE.score        801
## TRIMP.score       104
## speed.mean.kmh     27
## speed.sd.kmh        7
## power.mean.W      189
## power.sd.W        108
## xPower.W          244
## power.max.W       928
## 
## **POWER-TIME PROFILE**
## 
##        Watts
## 1 min    524
## 2 min    430
## 5 min    359
## 10 min   272
## 15 min   256
## 20 min   235
## 
## **LAPS**
## 
##  # time.min distance.km work.kJ climbing.m speed.mean.kmh speed.sd.kmh
##  1     45.2        20.7     544        228             27          6.2
##  2      5.0         2.5     108         53             30          3.2
##  3     53.5        24.2     533        481             27          7.4
##  4      2.0         1.3      51         10             38          5.6
##  5      8.6         3.3      60         40             23          6.5
##  power.mean.W power.sd.W xPower.W power.max.W
##           200         98      227         600
##           359        106      387         817
##           166         83      194         469
##           425        153      460         928
##           116         99      161         468

Also see ?summary_metrics for other common metrics.

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)

##                  120  180  240  300  360  420  480  540  600  660  720  780
## Best mean value  430  390  368  359  335  298  291  285  272  263  263  262
## Recorded @      2734 2736 2735 2735 2677 2645 2537 2511 2437 2363 2316 2257
##                  840  900  960 1020 1080 1140 1200
## Best mean value  257  256  249  245  243  240  235
## Recorded @      2197 2136 2077 2017 1957 1897 2243

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)

##    CP    W' 
##   227 28454

## 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"))

ms <- Pt_model(prof[1, ], times_sec)
print(ms)

##                                        formula  RSE fit
## Inverse                28453.749 / x + 226.732 16.6    
## Exponential      287.697 * e^-0.003x + 232.806  6.4  **
## Power              1613.408 * x^-0.272 - 1.235  8.3    
## Three-param 86157.002 / (x + 211.465) + 173.43  7.3

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.

library(leaflet)
leaflet(intervaldata) %>% addTiles() %>% addPolylines(~lon, ~lat)