Fit file viewer3/25/2023 Garmin_hr_zone = cut ( Garmin, breaks = seq ( 0, 200, 20 ) ) ) %>% mutate (hr_60 = zoo :: rollmean ( hr_diff, k = 60, fill = NA ) ) ggplot ( hr_differences, aes (x = timestamp, y = hr_diff ) ) geom_point ( aes (col = Garmin_hr_zone ) ) geom_line ( aes (y = hr_60 ), col = "grey40", lwd = 1.6, alpha = 0.7 ) geom_abline (intercept = 0, slope = 0 ) theme_bw ( ) ylab ( "Heart Rate Difference\n ve Chest Strap Greater / -ve Wrist Watch Greater" ) scale_colour_brewer (palette = "PRGn" )įor the most part the readings are quite similar between the two devices. Library ( zoo ) hr_differences % pivot_wider (id_cols = timestamp, names_from = device, values_from = heart_rate ) %>% mutate (hr_diff = Garmin - Zwift, We can then use dplyr::select() to extract the latitude and longitude columns from our tibble, so we can pass them easily to a plotting function. See the “Plotting a route” section below for an example of how to handle this. Note: sometimes the bulk of your data may be spread across multiple tibbles in the list rather than a single entry. In this example it seems clear that we can use the second entry, which contains the vast majority of the data. This normally happens if data recording begins before a sensor (e.g. a heart rate monitor) has been attached to a device, or GPS position has been acquired, although sometimes the reason can be more opaque. This is because in this particular file there are three distinct definitions of what a record contains. In this example we actually get a list with three tibbles. 140 36 # … with 3 more variables: temperature, cycles, # fractional_cadence
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