I am an avid runner and cyclist. For the past couple of years, I have recorded almost all my activities on some kind of GPS device.

I record my runs with a Garmin device and my bike rides with a Wahoo device, and I synchronize both accounts on Strava. I figured that it would be nice to directly access my data from my Strava account.

In the following text, I will describe the progress to get Strava data into R, process the data, and then create a visualization of activity routes.

You will need the following packages:

library(tarchetypes)
library(conflicted)
library(tidyverse)
library(lubridate)
library(jsonlite)
library(targets)
library(httpuv)
library(duckdb)
library(httr2)
library(arrow)
library(pins)
library(glue)
library(fs)

conflict_prefer("filter", "dplyr")

Data

The whole data pipeline is implemented with the help of the targets package. You can learn more about the package and its functionalities here.

In order to reproduce the analysis, perform the following steps:

• Clone the repository: https://github.com/duju211/pin_strava
• Install the packages listed in the libraries.R file
• Run the target pipeline by executing targets::tar_make() command
• Follow the instructions printed in the console

Target Plan

We will go through the most important targets in detail.

OAuth Dance from R

The Strava API requires an ‘OAuth dance’, described below.

Create an OAuth Strava app

To get access to your Strava data from R, you must first create a Strava API. The steps are documented on the Strava Developer site. While creating the app, you’ll have to give it a name. In my case, I named it r_api.

After you have created your personal API, you can find your Client ID and Client Secret variables in the Strava API settings. Save the Client ID as STRAVA_KEY and the Client Secret as STRAVA_SECRET in your R environment.

You can edit your R environment by running `usethis::edit_r_environ()`, saving the keys, and then restarting R.

STRAVA_KEY=<Client ID>
STRAVA_SECRET=<Client Secret>

The information in my_sig can now be used to access Strava data. Set the cue_mode of the target to ‘always’ so that the following API calls are always executed with an up-to-date authorization token.

Current authenticated user

Download information about the currently authenticated user. When preprocessing the data, the columns shoes, clubs and bikes need special attention, because they can contain multiple entries and can be interpreted as list columns.

active_user <- function(json_active_user, user_list_cols) {
  json_active_user[
    map_lgl(json_active_user, negate(is.null))
    & map_lgl(names(json_active_user), ~ !.x %in% user_list_cols)] |>
    as_tibble()
}

In the end there is a data frame with one row for the currently authenticated user:

## # A tibble: 1 × 26
##         id resource_state firstname lastname city    state country sex   premium
##      <int>          <int> <chr>     <chr>    <chr>   <chr> <chr>   <chr> <lgl>  
## 1 26845822              3 "Julian " During   Baling… Bade… Germany M     FALSE  
## # … with 17 more variables: summit <lgl>, created_at <chr>, updated_at <chr>,
## #   badge_type_id <int>, weight <dbl>, profile_medium <chr>, profile <chr>,
## #   blocked <lgl>, can_follow <lgl>, follower_count <int>, friend_count <int>,
## #   mutual_friend_count <int>, athlete_type <int>, date_preference <chr>,
## #   measurement_preference <chr>, is_winback_via_upload <lgl>,
## #   is_winback_via_view <lgl>

Activities

Load a data frame that gives an overview of all the activities from the data. Because the total number of activities is unknown, use a while loop. It will break the execution of the loop if there are no more activities to read.

read_all_activities <- function(access_token, active_user_id) {
  act_vec <- vector(mode = "list")
  df_act <- tibble(init = "init")
  i <- 1L

  while (nrow(df_act) != 0) {
    req <- request("https://www.strava.com/api/v3/athlete/activities") |>
      req_auth_bearer_token(token = access_token) |>
      req_url_query(page = i)

    resp <- req_perform(req)

    resp_check_status(resp)

    df_act <- resp |>
      resp_body_json(simplifyVector = TRUE) |>
      as_tibble()

    if (nrow(df_act) != 0)
      act_vec[[i]] <- df_act
    i <- i + 1L
  }

  act_vec |>
    bind_rows() |>
    mutate(
      start_date = ymd_hms(start_date),
      active_user_id = active_user_id)
}

The resulting data frame consists of one row per activity:

## # A tibble: 701 × 57
##    resource_state athlete$id name              distance moving_time elapsed_time
##             <int>      <int> <chr>                <dbl>       <int>        <int>
##  1              2   26845822 "Volleyball 🏐 "    10199         1953        76946
##  2              2   26845822 "Cycling Club"      18284.        3860         5146
##  3              2   26845822 "Volleyball 🏐 "    10475.        2141        15766
##  4              2   26845822 "Abendradfahrt"      6041.        1147         4513
##  5              2   26845822 "TSG SSV"           56324.        9668        23624
##  6              2   26845822 "Volleyball 🏐 "     4246.         775          795
##  7              2   26845822 "Ballon d‘Alsace"   22693.        4740         8341
##  8              2   26845822 "Super Planche d…   49032.       10076        24068
##  9              2   26845822 "Volleyball 🏐 "     8503.        1408        15990
## 10              2   26845822 "Planche Prep 2"    32375.        5042         5242
## # … with 691 more rows, and 52 more variables: athlete$resource_state <int>,
## #   total_elevation_gain <dbl>, type <chr>, sport_type <chr>,
## #   workout_type <int>, id <dbl>, start_date <dttm>, start_date_local <chr>,
## #   timezone <chr>, utc_offset <dbl>, location_city <lgl>,
## #   location_state <lgl>, location_country <chr>, achievement_count <int>,
## #   kudos_count <int>, comment_count <int>, athlete_count <int>,
## #   photo_count <int>, map <df[,3]>, trainer <lgl>, commute <lgl>, …

Make sure that all ID columns have a character format and improve the column names.

pre_process_act <- function(df_act_raw, active_user_id, meas_board) {
  df_act_raw |>
    mutate(across(contains("id"), as.character))
}

Extract ids of all activities. Exclude activities which were recorded manually, because they don’t include additional data:

rel_act_ids <- function(df_act) {
  df_act %>%
    filter(!manual) %>%
    pull(id)
}

Measurements

A ‘stream’ is a nested list (JSON format) with all available measurements of the corresponding activity.

To get the available variables and turn the result into a data frame, define a helper function read_activity_stream. This function takes an ID of an activity and an authentication token, which you created earlier.

Preprocess and unnest the data in this function. The column latlng needs special attention, because it contains latitude and longitude information. Separate the two measurements before unnesting all list columns.

read_activity_stream <- function(id, access_token) {
  req <- request("https://www.strava.com/api/v3/activities") |>
    req_auth_bearer_token(token = access_token) |>
    req_url_query(keys = str_glue(
      "distance,time,latlng,altitude,velocity_smooth,heartrate,cadence,",
      "watts,temp,moving,grade_smooth")) |>
    req_url_path_append(id) |>
    req_url_path_append("streams")

  resp <- req_perform(req)

  resp_check_status(resp)

  df_stream_raw <- resp |>
    resp_body_json(simplifyVector = TRUE) |>
    as_tibble() |>
    mutate(id = id) %>%
    pivot_wider(names_from = type, values_from = data)

  if ("latlng" %in% colnames(df_stream_raw)) {
    df_stream <- df_stream_raw %>%
      mutate(
        lat = map(
          .x = latlng, .f = ~ .x[, 1]),
        lng = map(
          .x = latlng, .f = ~ .x[, 2])) %>%
      select(-latlng)
  } else {
    df_stream <- df_stream_raw
  }

  df_stream %>%
    unnest(where(is_list)) %>%
    mutate(id = id)
}

Do this for every id and save the resulting data frames as feather file. By doing so we can later effectively query the data.

The name of the board is determined by the currently logged in user and will have a different name, if you run the pipeline.

Visualisation

Visualize the final data by displaying the geospatial information in the data. Join all the activities into one data frame. To do this, get the paths to all the measurement files:

meas_paths <- function(board_name) {
  dir_ls(
    board_name, type = "file", regexp = "df_\\d.*\\.arrow$", recurse = TRUE)
}

Insert them all into a duckdb and select relevant columns:

meas_all <- function(paths_meas) {
  act_col_types <- schema(
    moving = boolean(), velocity_smooth = double(),
    grade_smooth = double(), distance = double(),
    altitude = double(), heartrate = int32(), time = int32(),
    lat = double(), lng = double(), cadence = int32(),
    watts = int32(), id = string())

  open_dataset(paths_meas, format = "parquet", schema = act_col_types) %>%
    to_duckdb() %>%
    select(id, lat, lng) %>%
    filter(!is.na(lat) & !is.na(lng)) %>%
    collect()
}

## # A tibble: 2,396,364 × 3
##    id           lat   lng
##    <chr>      <dbl> <dbl>
##  1 7536987223  48.3  8.85
##  2 7536987223  48.3  8.85
##  3 7536987223  48.3  8.85
##  4 7536987223  48.3  8.85
##  5 7536987223  48.3  8.85
##  6 7536987223  48.3  8.85
##  7 7536987223  48.3  8.85
##  8 7536987223  48.3  8.85
##  9 7536987223  48.3  8.85
## 10 7536987223  48.3  8.85
## # … with 2,396,354 more rows

In the final plot every facet is one activity. Keep the rest of the plot as minimal as possible.

vis_meas <- function(df_meas_pro) {
  df_meas_pro %>%
    ggplot(aes(x = lng, y = lat)) +
    geom_path() +
    facet_wrap(~ id, scales = "free") +
    theme(
      axis.line = element_blank(),
      axis.text.x = element_blank(),
      axis.text.y = element_blank(),
      axis.ticks = element_blank(),
      axis.title.x = element_blank(),
      axis.title.y = element_blank(),
      legend.position = "bottom",
      panel.background = element_blank(),
      panel.border = element_blank(),
      panel.grid.major = element_blank(),
      panel.grid.minor = element_blank(),
      plot.background = element_blank(),
      strip.text = element_blank())
}

And there it is: All your Strava data in a few tidy data frames and a nice-looking plot. Future updates to the data shouldn’t take too long, because only measurements from new activities will be downloaded. With all your Strava data up to date, there are a lot of appealing possibilities for further data analyses of your fitness data.