rStrava

Marcus W. Beck, mbafs2012@gmail.com, Pedro Villarroel, pedrodvf@gmail.com, Daniel Padfield, dp323@exeter.ac.uk, Lorenzo Gaborini, lorenzo.gaborini@unil.ch, Niklas von Maltzahn, niklasvm@gmail.com

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Overview and installation

This is the development repository for rStrava, an R package to access data from the Strava API. The package can be installed from CRAN. It is also available on r-universe.

install.packages('rStrava')

The development version from this repository can be installed as follows:

install.packages('devtools')
devtools::install_github('fawda123/rStrava')

Issues and suggestions

Please report any issues and suggestions on the issues link for the repository.

Package overview

The functions are in two categories depending on mode of use. The first category of functions scrape data from the public Strava website and the second category uses the API functions or relies on data from the API functions. The second category requires an authentication token. The help files for each category can be viewed using help.search:

help.search('notoken', package = 'rStrava')
help.search('token', package = 'rStrava')

Scraping functions (no token)

An example using the scraping functions is below. Some users may have privacy settings that block public access to account data.

# get athlete data 
athl_fun(2837007, trace = FALSE)
## $`2837007`
## $`2837007`$name
## [1] "Marcus Beck"
## 
## $`2837007`$location
## [1] "Saint Petersburg, Florida"
## 
## $`2837007`$units
## [1] "mi" "ft"
## 
## $`2837007`$monthly
##        month      miles hours elev_gain
## 1 2021-06-01  71.347704     9       101
## 2 2021-07-01  61.492136     8        86
## 3 2021-08-01  69.587981     9       149
## 4 2021-09-01  57.588682     8        94
## 5 2021-10-01  90.255408    11       132
## 6 2021-11-01 101.732756    11       221
## 7 2021-12-01   3.237965     0         5
## 
## $`2837007`$recent
##           id           name type startDateLocal distance elevation movingTime
## 1 6329459767    Morning Run  run     2021-12-01      3.2        17      26:36
## 2 6327742864 Afternoon Ride ride     2021-11-30      2.9        24      14:02
## 3 6325224982   Morning Ride ride     2021-11-30      2.7        12      11:59
## 
## $`2837007`$achievements
##                                                                      description
## 1                  2nd fastest time on Bay Shore northbound - Pier to 5th Ave NE
## 2 2nd fastest time on Bay Shore and Vinoy northbound - Pier to the tennis courts
## 3                          3rd fastest time on Barwood, VInoy, and N Straub Park
##               timeago
## 1 2021-11-14 17:17:56
## 2 2021-11-14 17:17:56
## 3 2021-11-14 17:17:56

API functions (token)

Setup

These functions require a Strava account and a personal API, both of which can be obtained on the Strava website. The user account can be created by following instructions on the Strava homepage. After the account is created, a personal API can be created under API tab of profile settings. The user must have an application name (chosen by the user), client id (different from the athlete id), and an application secret to create the authentication token. Additional information about the personal API can be found here. Every API retrieval function in the rStrava package requires an authentication token (called stoken in the help documents). The following is a suggested workflow for using the API functions with rStrava.

First, create the authentication token using your personal information from your API. Replace the app_name, app_client_id, and app_secret objects with the relevant info from your account.

app_name <- 'myappname' # chosen by user
app_client_id  <- 'myid' # an integer, assigned by Strava
app_secret <- 'xxxxxxxx' # an alphanumeric secret, assigned by Strava

# create the authentication token
stoken <- httr::config(token = strava_oauth(app_name, app_client_id, app_secret, app_scope="activity:read_all"))

Setting cache = TRUE for strava_oauth will create an authentication file in the working directory. This can be used in later sessions as follows:

stoken <- httr::config(token = readRDS('.httr-oauth')[[1]])

Finally, the get_heat_map and get_elev_prof functions require a key from the Google API. Follow the instructions here. The key can be added to the R environment file for later use:

# save the key, do only once
cat("google_key=XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
    file=file.path(normalizePath("~/"), ".Renviron"),
    append=TRUE)

# retrieve the key, restart R if not found
google_key <- Sys.getenv("google_key")

The get_heat_map function uses ggmap to create base maps. A Google API key is needed to use the function. This key must be registered externally with the ggmap package using register_google() before executing get_heat_map.

library(ggmap)
register_google(google_key)

Using the functions

The API retrieval functions are used with the token.

myinfo <- get_athlete(stoken, id = '2837007')
head(myinfo)
## $id
## [1] 2837007
## 
## $username
## [1] "beck_marcus"
## 
## $resource_state
## [1] 2
## 
## $firstname
## [1] "Marcus"
## 
## $lastname
## [1] "Beck"
## 
## $bio
## [1] ""

An example creating a heat map of activities:

library(dplyr)

# get activities, get activities by lat/lon, distance, plot
my_acts <- get_activity_list(stoken)
act_data <- compile_activities(my_acts) %>% 
    filter(start_longitude < -86.5 & start_longitude > -88.5) %>% 
    filter(start_latitude < 31.5 & start_latitude > 30) %>% 
    filter(distance > 20)
    
get_heat_map(act_data, key = google_key, col = 'darkgreen', size = 2, distlab = F, f = 0.4)

Plotting elevation and grade for a single ride:

# actitivy id
id <- 1784292574

# plot elevation along a single ride
get_heat_map(my_acts, id = id, alpha = 1, add_elev = T, f = 0.3, distlab = F, key = google_key, size = 2, col = 'Spectral', maptype = 'satellite', units = 'imperial')

# plot % gradient along a single ride
get_heat_map(my_acts, id = id, alpha = 1, add_elev = T, f = 0.3,  distlab = F, as_grad = T, key = google_key, size = 2, col = 'Spectral', expand = 5, maptype = 'satellite', units = 'imperial')

Get elevation profiles for activities:

# get activities
my_acts <- get_activity_list(stoken) 

get_elev_prof(my_acts, id = id, key = google_key, units = 'imperial')

get_elev_prof(my_acts, id = id, key = google_key, units = 'imperial', total = T)

Plot average speed per split (km or mile) for an activity:

# plots for most recent activity
plot_spdsplits(my_acts, stoken, id = id, units = 'imperial')

Additional functions are provided to get “stream” information for individual activities. Streams provide detailed information about location, time, speed, elevation, gradient, cadence, watts, temperature, and moving status (yes/no) for an individual activity.

Use get_activity_streams for detailed info about activities:

# get streams for the first activity in my_acts
strms_data <- get_activity_streams(my_acts, stoken, id = id)
head(strms_data)
##   altitude distance grade_smooth      lat       lng moving time velocity_smooth
## 1    310.0   0.0000         -1.2 33.60582 -117.8021  FALSE    0            0.00
## 2    310.1   0.0034         -2.1 33.60582 -117.8021   TRUE    9            1.44
## 3    309.9   0.0082         -2.2 33.60581 -117.8020   TRUE   11            2.52
## 4    309.7   0.0141         -3.2 33.60580 -117.8020   TRUE   13            3.96
## 5    309.6   0.0180         -1.6 33.60580 -117.8019   TRUE   14           10.44
## 6    309.5   0.0223          0.0 33.60580 -117.8019   TRUE   15           11.52
##           id
## 1 1784292574
## 2 1784292574
## 3 1784292574
## 4 1784292574
## 5 1784292574
## 6 1784292574

Stream data can be plotted using any of the plotting functions.

# heat map
get_heat_map(strms_data, alpha = 1, filltype = 'speed', f = 0.3, size = 2, col = 'Spectral', distlab = F)

# elevation profile
get_elev_prof(strms_data)

# speed splits
plot_spdsplits(strms_data, stoken)

Contributing

Please view our contributing guidelines for any changes or pull requests.

License

This package is released in the public domain under the creative commons license CC0.