Converting Peloton resistance to Bowflex C6 resistance

Visualizing spin bike difficulty

Around the holidays, my partner and I decided to bring a stationary exercise bike into our home. After many pros and cons lists, we opted for the Bowflex C6. I decided to try out this whole Peloton thing, and quickly found that the resistance adjustments are not equivalent between the Peloton bike and the C6. Thankfully, there is a wonderful subreddit just for this model! The folks there directed me to this conversion chart, which then led me to a 3D-printed plate to keep on my handlebars.

As I kept going along in my classes, all I could think was “What does this look like on a plot? What is the distribution?” Here is the result.

Load packages

## load packages
library(dplyr)
library(tidyr)
library(purrr)
library(ggplot2)

## theme and Peloton(c) red
source("theme.R")
pelo_red <- "#df1c2f"

Create tibble

Create data table.

conv_table <- tibble(C6 = c(0, 5, 9, 17, 25, 33, 49, 100),
                     Peloton = c(0, 25, 30, 35, 40, 45, 50, 100),
                     Difficulty = seq(from = 0, to = 10, length.out = 8 ))

conv_table_long <- conv_table %>% 
  gather(C6, Peloton, key = "Bike", value = "Resistance")

Plot

Make the plot.

plot <- ggplot(data = conv_table_long) +
  geom_smooth(mapping = aes(x = Difficulty,
                            y = Resistance,
                            color = Bike),
              se = FALSE) + 
  scale_color_manual(values = c("white",
                                pelo_red),
                     labels = c("Bowflex C6",
                                "Peloton")) +
  scale_x_continuous(n.breaks = 3,
                     labels = c("Easy",
                                "Hard",
                                "Impossible")) +
  labs(title = "Spin bike resistance",
       subtitle = "Peloton vs. Bowflex C6",
       x = "Difficulty",
       caption = "@LizRoten 2021 | Data r/pelotoncycle") +
  my_theme  

Display plot

plot

Final plot

Update!

I was scrolling through aforementioned r/SchwinnIC4_BowflexC6 and found a post by another data person!

The conversion formula u/raintower579 found is below

$$ y = 0.0171x^2 - 0.64x + 9.1429 $$
where x is the Peloton resistance and y is the C6 resistance.

pelo_conversion <- function(x){
  (0.0171*x^2) - (0.64*x) + 9.1429
}

To estimate the equivalence beyond 50, we can create a new tibble and apply the function to a sequence of Peloton resistance settings. Instructors don’t tend to call out any value below 20, so we can start there.

tibble(peloton_resistance = seq(20,100,5)) %>%  # create Peloton resistance sequence, 20-100 by 5s
  mutate(c6_resistance = round(pelo_conversion(peloton_resistance), 1))
#> # A tibble: 17 x 2
#>    peloton_resistance c6_resistance
#>                 <dbl>         <dbl>
#>  1                 20           3.2
#>  2                 25           3.8
#>  3                 30           5.3
#>  4                 35           7.7
#>  5                 40          10.9
#>  6                 45          15  
#>  7                 50          19.9
#>  8                 55          25.7
#>  9                 60          32.3
#> 10                 65          39.8
#> 11                 70          48.1
#> 12                 75          57.3
#> 13                 80          67.4
#> 14                 85          78.3
#> 15                 90          90.1
#> 16                 95         103. 
#> 17                100         116.

By this table, I’ve been making my classes much harder than necessary.

We can plot this function, as shown below.

ggplot(data = conv_table,
       aes(x = Peloton,
           y = C6)) +
  geom_function( 
    fun = pelo_conversion,
    color = "white") +
  stat_function(
    fun = pelo_conversion,
    geom = "point",
    color = pelo_red,
    size = 2, 
    n = 17) +
  scale_x_continuous(limits = c(20, 100)) +
  scale_y_continuous(limits = c(0, 100)) +
  labs(title = "Peloton to Bowflex C6 resistance conversion",
       x = "Peloton",
       y = "Bowflex C6",
       caption = "@LizRoten 2021 | Data r/pelotoncycle | Model u/raintower579") +
  my_theme2

The big, flashing caveat here is that every bike is calibrated just slightly differently, so this might not be correct for the machine in my bedroom.

Liz Roten
Liz Roten
Data Scientist & Cartographer

My favorite food is eggplant.

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