Marcus W. Beck, [email protected], Pedro Villarroel, [email protected], Daniel Padfield, [email protected], Lorenzo Gaborini, [email protected], Niklas von Maltzahn, [email protected]
Linux:
Windows:
This is the development repository for rStrava, an R package to access data from the Strava API. The package can be installed and loaded as follows:
install.packages('devtools')
devtools::install_github('fawda123/rStrava')Please report any issues and suggestions on the issues link for the repository.
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')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] "Irvine, California"
##
## $`2837007`$units
## [1] "mi" "ft"
##
## $`2837007`$monthly
## month miles hours elev_gain
## 1 2018-12-01 161.58634 14 964
## 2 2019-01-01 391.10905 28 1110
## 3 2019-02-01 247.53005 18 679
## 4 2019-03-01 343.69594 24 1039
## 5 2019-04-01 332.47584 23 990
## 6 2019-05-01 351.16172 25 1266
## 7 2019-06-01 79.16518 5 375
##
## $`2837007`$recent
## id name type startDateLocal distance elevation
## 1 2430862993 Morning Ride ride 2019-06-06 10.6 51
## 2 2428239554 Morning Run run 2019-06-05 3.2 71
## 3 2428239599 Afternoon Ride ride 2019-06-05 10.8 117
## movingTime
## 1 40:43
## 2 25:40
## 3 41:42
##
## $`2837007`$achievements
## description
## 1 PR on El Camino to Irvine Blvd Sprint
## 2 2nd fastest time on Lower PC to Bryan
## 3 2nd fastest time on Start it up
## 4 2nd fastest time on Back on track
## 5 2nd fastest time on Walnut Trail - Jeffrey to Sand Canyon
## timeago
## 1 2019-06-04 23:28:48
## 2 2019-06-04 23:28:48
## 3 2019-06-04 23:28:48
## 4 2019-06-04 23:28:48
## 5 2019-06-04 14:03:04
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))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
mykey <- 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(mykey)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] 3
##
## $firstname
## [1] "Marcus"
##
## $lastname
## [1] "Beck"
##
## $city
## [1] "Irvine"
An example creating a heat map of activities:
library(dplyr)
# get activities, get activities by lat/lon, 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)
get_heat_map(act_data, key = mykey, col = 'darkgreen', size = 2, distlab = F, f = 0.4)
Plotting elevation and grade for a single ride:
# plot elevation along a single ride
get_heat_map(my_acts, acts = 1, alpha = 1, add_elev = T, f = 0.3, key = mykey, size = 2, col = 'Spectral', maptype = 'satellite', units = 'imperial')
# plot % gradient along a single ride
get_heat_map(my_acts, acts = 1, alpha = 1, add_elev = T, f = 0.3, as_grad = T, key = mykey, 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, acts = 1, key = mykey, units = 'imperial')
get_elev_prof(my_acts, acts = 1, key = mykey, 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, acts = 1, 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 activites:
# get streams for the first activity in my_acts
strms_data <- get_activity_streams(my_acts, stoken, acts = 1)
head(strms_data)## altitude distance grade_smooth moving time velocity_smooth lat
## 1 34.6 0.0000 -1.4 FALSE 0 0.00 33.72592
## 2 34.6 0.0036 -2.0 TRUE 7 1.80 33.72594
## 3 34.5 0.0074 -1.5 TRUE 12 2.16 33.72591
## 4 34.3 0.0149 -0.9 TRUE 14 5.76 33.72584
## 5 34.3 0.0204 -0.4 TRUE 15 7.56 33.72580
## 6 34.4 0.0259 0.9 TRUE 16 9.00 33.72576
## lng id
## 1 -117.7833 2420187776
## 2 -117.7834 2420187776
## 3 -117.7834 2420187776
## 4 -117.7834 2420187776
## 5 -117.7833 2420187776
## 6 -117.7833 2420187776
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)
Please view our contributing guidelines for any changes or pull requests.
This package is released in the public domain under the creative commons license CC0.