Strava

Note: This package has not been actively maintained for some time. If you are familiar with Python please consider using the Python version which is available at https://github.com/marcusvolz/strava_py

Create artistic visualisations with your Strava exercise data

Examples

Facets

A plot of activities as small multiples. The concept behind this plot was originally inspired by Sisu.

Map

Elevations

Calendar

Ridges

Packed circles

Activities by year ridges

Activities by year

Activities by month

Activities by week

Individual activity map

How to use

Bulk export from Strava

The process for downloading data is described on the Strava website here: [https://support.strava.com/hc/en-us/articles/216918437-Exporting-your-Data-and-Bulk-Export#Bulk], but in essence, do the following:

1. Log in to Strava
2. Select "Settings" from the main drop-down menu at top right of the screen
3. Select "My Account" from the navigation menu to the left of the screen.
4. Under the "Download or Delete Your Account" heading, click the "Get Started" button.
5. Under the "Download Request", heading, click the "Request Your Archive" button. Don't click anything else on that page, i.e. particularly not the "Request Account Deletion" button.
6. Wait for an email to be sent
7. Click the link in email to download zipped folder containing activities
8. Unzip files

Install the packages

install.packages(c("devtools", "mapproj", "tidyverse", "gtools", "lubridate", "wesanderson", "ggmap", "patchwork"))
devtools::install_github("marcusvolz/strava")
devtools::install_github("AtherEnergy/ggTimeSeries")

Load the library

library(strava)

Process the data

Note: Strava changed the way that activity files are bulk exported in ~May 2018. The process_data function only works with gpx files, so if your exported files are in some other format they will need to be converted (or imported into R some other way). One way to do this is to use GPSBabel, which converts between different GPS data formats (e.g. fit to gpx).

data <- process_data(<path to folder with gpx files>)

Note: In order to run year_history_ridges, year_history_plot, month_history_plot, week_history_plot you need to first run process activities and then join_data_activities functions.

Load activities data

Functions dependent on process_activities data (year_history_ridges, year_history_plot, month_history_plot, week_history_plot) come with default arguments. The output can be modifited by unit (Count, Distance, Time), available activity type (e.g. Ride, Run, Walk or other), date and other.

activities <- process_activities(<path to activities.csv file>)

Join data with activities

data <- join_data_activities()

There are some sample data included with the package:

# Sample running data.
running <- process_data(system.file("gpx/running", package = "strava"))
# Sample cycling data.
cycling <- process_data(system.file("gpx/cycling", package = "strava"))

You can also list them all or get the path to a specific one using its name:

# List all the examples (no argument)
gpx_example()
# Get a specific gpx (using its partial or full name)
gpx_example("734")

Plot activities as small multiples

p1 <- plot_facets(data)
ggsave("plots/facets001.png", p1, width = 20, height = 20, units = "cm")

Plot activity map

p2 <- plot_map(data, lon_min = 144.9, lon_max = 145.73, lat_min = -38.1, lat_max = -37.475)
ggsave("plots/map001.png", p2, width = 20, height = 15, units = "cm", dpi = 600)

Plot elevation profiles

Note: Strava changed the way that activity files are bulk exported in ~May 2018. Unfortunately this plot will not work with files exported from Strava after this time.

p3 <- plot_elevations(data)
ggsave("plots/elevations001.png", p3, width = 20, height = 20, units = "cm")

Plot calendar

p4 <- plot_calendar(data, unit = "distance")
ggsave("plots/calendar001.png", p4, width = 20, height = 20, units = "cm")

Plot ridges

p5 <- plot_ridges(data)
ggsave("plots/ridges001.png", p5, width = 20, height = 20, units = "cm")

Plot packed circles

p6 <- plot_packed_circles(data)
ggsave("plots/packed_circles001.png", p6, width = 20, height = 20, units = "cm")

Plot year history ridges

p7 <- year_history_ridges(activities)
ggsave("plots/year_history_ridges.png", p7, width = 20, height = 20, unit = "cm")

Plot year history

p8 <- year_history_plot(activities)
ggsave("plots/year_history_plot.png", p8, width = 25, height = 20, unit = "cm")

Plot month history

p9 <- month_history_plot(activities)
ggsave("plots/month_history_plot.png", p9, width = 25, height = 15, unit = "cm")

Plot week history

p10 <- week_history_plot(activities)
ggsave("plots/week_history_plot.png", p10, width = 25, height = 15, unit = "cm")

Plot individual map

You need to register your Google API key first to run this function as it relies on ggmap package. This can be done on a temporary basis with register_google(key = "[your key]") or permanently using register_google(key = "[your key]", write = TRUE). As described on LITTLE MISS DATA page [https://www.littlemissdata.com/blog/maps] in order to access your own API you need to follow those steps:

1. Visit - https://console.cloud.google.com and sign up for a google cloud platform trial.
2. Create a project. In the top nav, you can either select an existing project or create a new one.
3. Add the “Maps Static API” service to the project. Navigate to the library of API services and search for “Maps Static API”. Enable the service.
4. Generate an API Key. Navigate to the credentials area and select “Create credentials”. Take note of your API key. You will need to register it with the package later.
5. Run register_google(key = "[your key]") command.

For futher details you can also see ggmap readme here: [https://cran.r-project.org/web/packages/ggmap/readme/README.html]

p11 <- individual_plot_map(data)
ggsave("plots/individual_plot_map.png", p11, width = 20, height = 20, unit = "cm")