Install pelotonR

First, to install the package, you can run the following code to download/install it from Ben’s repo.

devtools::install_github("bweiher/pelotonR")

Load Libraries

Let’s fire up pelotonR and, as always, the tidyverse and take them for a spin.

library(pelotonR)
library(tidyverse)
library(lubridate)

Connect To API

Use your Peloton email address and password.

peloton_auth(login = "your.email@domain.com", password = "cl3v3r_pa$$w0rd")

After connecting to the API, you can get your info with the following function.

me <- get_my_info() 

For our own edification, here is a list of all the output columns alphabetized.

allow_marketing, birthday, block_explicit, block_prenatal, can_charge, contract_agreements, created_at, created_country, customized_heart_rate_zones, customized_max_heart_rate, cycling_ftp, cycling_ftp_source, cycling_ftp_workout_id, cycling_workout_ftp, default_heart_rate_zones, default_max_heart_rate, email, estimated_cycling_ftp, external_music_auth_list, facebook_access_token, facebook_id, first_name, gender, hardware_settings, has_active_device_subscription, has_active_digital_subscription, has_signed_waiver, height, id, image_url, instructor_id, is_complete_profile, is_demo, is_effort_score_enabled, is_external_beta_tester, is_fitbit_authenticated, is_internal_beta_tester, is_profile_private, is_provisional, is_strava_authenticated, last_name, last_workout_at, locale, location, member_groups, middle_initial, name, obfuscated_email, paired_devices, phone_number, quick_hits, referrals_made, total_followers, total_following, total_non_pedaling_metric_workouts, total_pedaling_metric_workouts, total_pending_followers, total_workouts, username, v1_referrals_made, weight, workout_counts

Explore API

Currently, the pelotonR package has a nice function for exploring your information. As with most APIs the data is stored in JSON format The function below extracts the data and unpacks some of the nested lists.

Here it is below:

my_workout_df <- get_all_workouts(userid = me$id, num_workouts = me$total_workouts, joins = "ride,ride.instructor", parse_dates = T)
my_workout_df 

## # A tibble: 398 x 89
##    created_at          device_type  end_time            fitness_discipline
##    <dttm>              <chr>        <dttm>              <chr>             
##  1 2021-06-20 20:59:24 home_bike_v1 2021-06-20 21:19:25 stretching        
##  2 2021-06-20 20:48:58 home_bike_v1 2021-06-20 20:58:59 stretching        
##  3 2021-03-24 20:06:30 home_bike_v1 2021-03-24 20:27:27 cycling           
##  4 2021-03-24 19:58:42 home_bike_v1 2021-03-24 20:03:40 cycling           
##  5 2021-03-17 20:41:26 home_bike_v1 2021-03-17 21:02:21 cycling           
##  6 2021-03-17 20:31:24 home_bike_v1 2021-03-17 20:36:22 cycling           
##  7 2021-03-08 20:24:45 home_bike_v1 2021-03-08 20:45:40 cycling           
##  8 2021-03-08 20:16:04 home_bike_v1 2021-03-08 20:21:01 cycling           
##  9 2021-03-05 20:40:23 home_bike_v1 2021-03-05 21:01:20 cycling           
## 10 2021-03-05 20:32:06 home_bike_v1 2021-03-05 20:37:05 cycling           
## # ... with 388 more rows, and 85 more variables: has_pedaling_metrics <lgl>,
## #   has_leaderboard_metrics <lgl>, id <chr>,
## #   is_total_work_personal_record <lgl>, metrics_type <chr>, name <chr>,
## #   peloton_id <chr>, platform <chr>, start_time <dttm>, status <chr>,
## #   timezone <chr>, title <chr>, total_work <dbl>, user_id <chr>,
## #   workout_type <chr>, total_video_watch_time_seconds <int>,
## #   total_video_buffering_seconds <int>,
## #   v2_total_video_watch_time_seconds <dbl>,
## #   v2_total_video_buffering_seconds <dbl>,
## #   total_music_audio_play_seconds <chr>,
## #   total_music_audio_buffer_seconds <chr>, ride <list>, created <dttm>,
## #   device_time_created_at <dttm>, strava_id <chr>, fitbit_id <chr>,
## #   effort_zones <chr>, ride_has_closed_captions <lgl>,
## #   ride_content_provider <chr>, ride_content_format <chr>,
## #   ride_description <chr>, ride_difficulty_rating_avg <dbl>,
## #   ride_difficulty_rating_count <int>, ride_difficulty_level <chr>,
## #   ride_duration <int>, ride_extra_images <chr>,
## #   ride_fitness_discipline <chr>, ride_fitness_discipline_display_name <chr>,
## #   ride_has_pedaling_metrics <lgl>, ride_home_peloton_id <chr>, ride_id <chr>,
## #   ride_image_url <chr>, ride_instructor_id <chr>, ride_is_archived <lgl>,
## #   ride_is_closed_caption_shown <lgl>, ride_is_explicit <lgl>,
## #   ride_is_live_in_studio_only <lgl>, ride_language <chr>, ride_length <int>,
## #   ride_live_stream_id <chr>, ride_live_stream_url <chr>, ride_location <chr>,
## #   ride_metrics <list>, ride_origin_locale <chr>,
## #   ride_original_air_time <dttm>, ride_overall_rating_avg <dbl>,
## #   ride_overall_rating_count <int>, ride_pedaling_start_offset <int>,
## #   ride_pedaling_end_offset <int>, ride_pedaling_duration <int>,
## #   ride_rating <int>, ride_ride_type_id <chr>, ride_ride_type_ids <list>,
## #   ride_sample_vod_stream_url <chr>, ride_scheduled_start_time <dttm>,
## #   ride_series_id <chr>, ride_sold_out <lgl>, ride_studio_peloton_id <chr>,
## #   ride_title <chr>, ride_total_ratings <int>,
## #   ride_total_in_progress_workouts <int>, ride_total_workouts <int>,
## #   ride_vod_stream_url <chr>, ride_vod_stream_id <chr>,
## #   ride_class_type_ids <list>, ride_difficulty_estimate <dbl>,
## #   ride_overall_estimate <dbl>, ride_availability <list>,
## #   ride_is_dynamic_video_eligible <lgl>, ride_is_fixed_distance <lgl>,
## #   ride_dynamic_video_recorded_speed_in_mph <dbl>, ride_distance <dbl>,
## #   ride_distance_unit <chr>, ride_distance_display_value <chr>,
## #   ride_instructor <list>

You can see the function returns quite a bit of information - one row per workout.

Dig in deeper into the API

The Peloton API has a lot more information that you cannot yet access in the library. As I get more familiar with the API / library, I may do a few more pull requests. In the meantime, lets see what else we can find.

What we see in the my_workout dataframe above list element that contains the instructor ID.

my_workout_df %>%
  select(ride_instructor) %>% 
  group_by(ride_instructor) %>%
  group_split() %>% 
  purrr::map(~unnest(.x)) %>% 
  purrr::map(~slice(.x, 1)) %>% 
  purrr::map_dfr(~unnest(.x))

## # A tibble: 33 x 1
##    ride_instructor                 
##    <chr>                           
##  1 561f95c405734d8488ed8dcc8980d599
##  2 1b79e462bd564b6ca5ec728f1a5c2af0
##  3 4904612965164231a37143805a387e40
##  4 048f0ce00edb4427b2dced6cbeb107fd
##  5 7f3de5e78bb44d8591a0f77f760478c3
##  6 05735e106f0747d2a112d32678be8afd
##  7 2e57092bee334c8c8dcb9fe16ba5308c
##  8 4672db841da0495caf4b8f9cda405512
##  9 c0a9505d8135412d824cf3c97406179b
## 10 f6f2d613dc344e4bbf6428cd34697820
## # ... with 23 more rows

While I was selective in what I extracted from this JSON, I do not see instructor name. Lets see what we can find if we dig into the API.

Access API

To find the instructor information, we need to use the correct instructor endpoint. Some API’s are not documented very well, so you have to get lucky sometimes to find these.

pull <- httr::GET(url = str_c("https://api.onepeloton.com/api/instructor?limit=1000&page=0"))
parsed <- jsonlite::fromJSON(httr::content(pull, "text", encoding = "UTF-8"), simplifyVector = FALSE)
pull

## Response [https://api.onepeloton.com/api/instructor?limit=1000&page=0]
##   Date: 2021-06-24 16:00
##   Status: 200
##   Content-Type: application/json
##   Size: 190 kB

Status 200 means success!

This function below will pull out one instructor’s information. There is a LOT more information for each instructor – but I will only extract a few interesting headings.

instructor_function <- function(num = 2) {
  tibble(
    instructor_name = pluck(parsed$data, num, c("name")),
    ride_instructor_id = pluck(parsed$data, num, c("id")),
    ride_instructor_bio = pluck(parsed$data, num, c("bio")),
    ride_instructor_background = list(pluck(parsed$data, num, c("fitness_disciplines")))
  )
}

instructor_function(5)

## # A tibble: 1 x 4
##   instructor_name ride_instructor_id  ride_instructor_bio      ride_instructor_~
##   <chr>           <chr>               <chr>                    <list>           
## 1 Ally Love       731d7b7f6b414a4989~ "As a model, dancer, ce~ <list [1]>

Ally Love, my wife’s favorite!

Lets get a look at how we can access the data of all the Peloton instructors.

instructor_df <-
1:parse_list_to_df(parsed, 
                   parse_dates = T, 
                   dictionary = list(numeric = c("v2_total_video_buffering_seconds", "ride_distance",
    "v2_total_video_watch_time_seconds")))$total %>%
  purrr::map_dfr(~ instructor_function(num = .x))
instructor_df

## # A tibble: 41 x 4
##    instructor_name  ride_instructor_id ride_instructor_bio     ride_instructor_~
##    <chr>            <chr>              <chr>                   <list>           
##  1 Aditi Shah       b8c2734e18a7496fa~ "To Aditi, yoga goes b~ <list [2]>       
##  2 Adrian Williams  f962a2b1b34d424ca~ "Adrian is a powerhous~ <list [6]>       
##  3 Anna Greenberg   a8c56f162c964e939~ "When Anna first turne~ <list [2]>       
##  4 Alex Toussaint   2e57092bee334c8c8~ "Alex Toussaint has a ~ <list [1]>       
##  5 Ally Love        731d7b7f6b414a498~ "As a model, dancer, c~ <list [1]>       
##  6 Andy Speer       c9fa21c2004c4544a~ "Andy takes a techniqu~ <list [6]>       
##  7 Becs Gentry      286fc17080d34406a~ "Becs joins Peloton fr~ <list [3]>       
##  8 Ben Alldis       7f3de5e78bb44d859~ "Growing up, Ben was a~ <list [2]>       
##  9 Bradley Rose     01f636dc54a145239~ "If there’s one thing ~ <list [1]>       
## 10 Callie Gullicks~ 255c81782f7242c9a~ "Callie radiates sunsh~ <list [3]>       
## # ... with 31 more rows

There’s all the instructors. Now lets join them to our workout dataframe. You can see the additional columns of instructor information at the end of the dataframe.

my_workout_df <-
  my_workout_df %>%
  left_join(instructor_df)

my_workout_df %>% 
  select(created_at ,name, instructor_name) %>% 
  filter(!is.na(instructor_name)) %>% 
  slice_head(n = 5)

## # A tibble: 5 x 3
##   created_at          name               instructor_name
##   <dttm>              <chr>              <chr>          
## 1 2021-06-20 20:59:24 Stretching Workout Hannah Corbin  
## 2 2021-06-20 20:48:58 Stretching Workout Jess Sims      
## 3 2021-03-24 20:06:30 Cycling Workout    Kendall Toole  
## 4 2021-03-24 19:58:42 Cycling Workout    Kendall Toole  
## 5 2021-03-17 20:41:26 Cycling Workout    Jess King

Visualize

In my next plot post, I’ll do more to explore the data in the API. In the meantime, lets take a look at which instructors I seem to prefer!

my_workout_df %>% 
  filter(created_at <= ymd(20200809)) %>% 
  filter(fitness_discipline == "cycling") %>%
  count(instructor_name,fitness_discipline) %>% 
  filter(!is.na(instructor_name)) %>% 
  mutate(instructor_name = fct_lump_min(instructor_name,5,w = n)) %>% 
  group_by(instructor_name) %>% summarise(n = sum(n),.groups = "drop_last") %>% arrange(-n) %>% 
  ggplot(aes(x=fct_reorder(instructor_name,n),y=n,fill = instructor_name)) +
  geom_col(position = "dodge") +
  scale_fill_manual(values = wesanderson::wes_palette("Rushmore1", n = 8,type = "continuous")) +
  coord_flip() +
  labs(title = "My Favorite Instructors", x = "", y = "Rides") +
  theme(legend.position = "none")